Revealing cognitive mechanisms in the pigeon brain:
categorization and inter-hemispheric interaction
A Dissertation submitted for the Degree of
PhD in Neuroscience
The International Graduate School of Neuroscience
Ruhr-University Bochum
by
Ruth Adam
January 2008
Printed with permission of the International Graduate School of Neuroscience, RuhrUniversity Bochum
First supervisor: Prof. Dr. Dr. h.c. Onur Güntürkün
Second supervisor: Prof. Dr. Gregor Schöner.
ii
"The relevant experiments are on categorization by animals, mostly but not entirely
on pigeons. No careful forethought was involved in the choice of an experimental subject,
but pigeons were a happy accident. First of all, they perch on a separate branch of the
phylectic tree, being birds and not mammals, let alone apes like us. Anythings in common
between people and pigeons probably has substantial biological generality. Secondly,
pigeons have unimpressive brains, neurophysiologically speaking. Even among birds, they
have relatively little neural tissue left over after attending to the problems of coordinating
flight, compared to crows for example. Whatever a pigeon does must be reckoned
neurophysiologically simple compared to higher mammlas. Whatever a pigeon does easily
must be reckoned elementary. Finally and most significantly, the categorical powers of
pigeons have proved to be remarkably abstract."
Herrnstein, R.J., 1982
iii
Abstract
This thesis is based on four experiments in which we studied the classification
abilities of the pigeon and the relationship between the two cerebral hemispheres in
performing discrimination tasks.
Studying these questions in animals is important, not only from the viewpoint of
comparative cognition but also from that of research on humans. Pigeons were
previously shown to be able to discriminate and categorize various stimuli; but what
are the underlying mechanisms that facilitate this ability? Are they similar to those
mechanisms that were found in humans, adults, and infants? Is it possible that birds
and humans share similar cognitive mechanisms despite the fact that they have a very
different cerebral organization? Perhaps the most apparent brain organization is the
division of the brain into two functionally different parts: the left and the right
cerebral hemispheres. It is now widely recognized that humans and birds possess
hemispheric specialization that might even be functionally similar. For example, in
both species it was shown that the left hemisphere is superior in computing local
features and that the right hemisphere computes mainly global features. Although the
two hemispheres are functionally different, they together produce a single holistic
perception. Studying hemispheric interaction mechanisms in birds can help us to
better understand those mechanisms in our brain. The first two experiments in this
thesis dealt with category formation, its borders and mechanisms. Briefly, these two
experiments demonstrated that pigeons attend to their everyday environments and
that, like in humans, this previous (3-dimensional) experience facilitates (2dimensional) category formation. Experiment 1 also suggested that the pigeon cannot
categorize a complex and novel class. The third and fourth experiments investigated
hemispheric asymmetry and hemispheric interaction in discrimination tasks. From the
third experiment, we concluded that interaction of the two hemispheres is needed in
order to discriminate complex visual stimuli, although each hemisphere computes
different aspects of the stimuli. We also showed that previous experience enhances
the abilities of both hemispheres similarly. Experiment 4 provided evidence that in
some cases the uni-hemispheric performances are higher than the bi-hemispheric
ones, hinting that for simple discrimination computations, a single hemisphere is
sufficient. Taken together, those two studies implied that, like in humans, the task
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complexity affects the exhibited hemispheric interaction Moreover, the fourth
experiment showed, for the first time that the pigeon possesses metacontrol, and
suggested the existence of a lateralized metacontrol mechanism.
Collectively, we showed that, although not all, similar cognitive mechanisms do exist
in both humans and pigeons.
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Table of Content
Abstract ………………………………………………………………………………iv
1. Introduction………………………………………………………………………...1
1.1 Categorization…………………………………………………….……….1
1.1.1 Categorization: definition..….…………………………………..1
1.1.2 Classification levels……..….…………………………………...1
1.1.3 Classification in pigeons.………...………………………...……2
1.1.4 Objects and their pictorial representation.………..……………..6
1.2 Hemispheric lateralization...........................................................................8
1.2.1 Lateralization: general introduction…………………………......8
1.2.2 Birds as an animal model for laterality studies………………….8
1.2.3 Hemispheric differences in categorization tasks……………...…9
1.2.4 Inter-hemispheric interaction and metacontrol………………...11
2. Aims ……………………………………………………………………………....13
3. Experiment 1: Category formation: borders and mechanisms ………………....16
3.1 Introduction..……………………………………………………………..16
3.2 Materials and Methods..............................................................................16
3.2.1 Subjects………………………………………………………...16
3.2.2 Apparatus ……………………………………………………...17
3.2.3 Stimuli………………………………………………………….17
3.2.4 Procedure……………………………………………………….20
3.2.5 Controls………………………………………………………...21
3.2.6 Analysis and Statistic…………………………………………..23
3.3 Results….………………………………………………………………...24
3.3.1 Initial training ……………………………………………….…24
3.3.2 Discrimination training………………………………………...24
3.3.3 Transfer tests…………………………………………………...25
3.3.4 'Human cartoon' skin color control ………………………….…27
3.3.5 Testing order control…………………………………………...27
3.3.6 Inter-class similarities control……………………………….…28
3.3.7 Picture-photo transfer control…………………………………..28
3.4 Discussion………………………………………………………………..29
4. Experiment 2: 3-dimensional spontaneous discrimination ………………..……33
4.1 Introduction…………………………………………………………..…..33
4.2 Materials and Methods………………………………………………..….33
4.2.1 Subjects………………………………………………………...33
4.2.2 Procedure……………………………………………………….33
4.2.3 Controls………………………………………………………...34
4.2.4 Analysis………………………………………………………...34
4.2.5 Statistics………………………………………………………..35
4.2.6 Hypothesis……………………………………………………...35
4.3 Results……………………………………………………………………36
4.4 Discussion ………………………………………………………………..38
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5. Experiment 3: Hemispheric interaction in complex discrimination tasks …..….40
5.1 Introduction….………………………………………………………..….40
5.2 Materials and methods ……………………………………………..……40
5.2.1 Subjects………………………………………………….……..40
5.2.2 Apparatus………………………………………………………40
5.2.3 Stimuli………………………………………………….………40
5.2.4 Procedure……………………………………………….………41
5.2.5 Analysis and Statistic.……………………………………….…41
5.3 Results…………………………………………………………………....43
5.4 Discussion.…………………………………………………………….…47
6. Experiment 4: Metacontrol computation as a strategy to solve binocularly
conflicting discrimination ……………………………………..…50
6.1 Introduction.……………………………………………………..……….50
6.2 Materials and Methods…………………………………………..……….50
6.2.1 Subjects………………………………………………….……..50
6.2.2 Apparatus ……………………………………………….……..51
6.2.3 Stimuli…………………………………………………….……51
6.2.4 Procedure………………………………………………….……51
6.2.5 Analysis and Statistic………………………………………..…52
6.3 Results……………………………………………………………………54
6.3.1 Monocular discrimination training………………………….….54
6.3.2 Testing session………………………………………………....54
6.4 Discussion.…………………………………………………………….....57
7. General Discussion ……………………………………………………..………60
7.1 Summary of the experimental findings.………………………….………60
7.1.1 Experiment 1: Category formation: borders and mechanisms…60
7.1.2 Experiment 2: 3-dimensional spontaneous discrimination….….60
7.1.3 Experiment 3: Hemispheric interaction in complex
discrimination tasks…………………………..….61
7.1.4 Experiment 4: Metacontrol computation as a strategy to solve
binocularly conflicting discrimination…...…….61
7.2 Categorization mechanisms and the effect of knowledge………………..63
7.3 Categorization border……...…………………………………………….67
7.4 Hemispheric performance in complex discrimination tasks……………..69
7.5 Hemispheric interaction continuum and complexity of stimuli………….71
7.6 General outlook and future directions…………………………...………74
Appendix 1: The questionnaire distributed in experiment 2.…….………….....….…76
Appendix 2: Example of dominance index calculation in one session,
for a single pigeon………………………………………………….......77
Appendix 3: Laterality index mechanisms: Hemispheric interaction in the binocular
discrimination of the monocularly-learned color pairs……………...…79
Reference list ……………………………...…………………….…………………..80
Declaration……………………………..…………………………………….….…..93
Acknowledgements….………………………………………………….…….….......94
Curriculum Vitae…………………………………………………………….............95
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1. Introduction
1.1 Categorization
1.1.1 Categorization: definition
How do we know that Max is a dog and not a cat? How do monkeys discriminate
between an apple and a pear? How do cows distinguish between toxic and edible
plants? The answer to all those questions is that humans and animals can categorize.
Categorization is defined as the ability to generalize within a class of stimuli and to
discriminate between the classes (Keller & Schoenfeld, 1950), as well as to
extrapolate the categorical knowledge to new members of the stimulus classes
(Wasserman et al., 1988).
Categorization is a fundamental capacity that enables us and other organisms to act in
a varied, constantly changing environment (Miller et al., 2002). Grouping similar
objects reduces computational demands and enables an organism to use its resources
for other computations (Wasserman, 1993).
1.1.2 Classification levels
Categorization is only one way of sorting different objects. The renowned
comparative psychologist, Herrnstein (1990) defined five (functional) levels of
classification (Figure 1.1; see also Zayan & Vauclair, 1998), which are described as
follows.
1. Discrimination
Discrimination is the ability to distinguish between several stimuli. A lack of
discrimination constitutes the basic, sensory level of classification. When an organism
does not see the difference between a few stimuli, it will treat them equally. Then it
will seemingly classify them in one group, when in fact it does not distinguish
between the stimuli.
2. Categorization by rote
Categorization by rote is a memory-based classification strategy, in which stimuli that
provide the same reinforcement, are learned as a list of individual items.
1
Thus, only known (memorized) exemplars can be correctly classified.
3. Open-ended categorization (categorization in short)
This classification level is based on perceptual similarities, and therefore confers the
ability to classify correctly not only known but also novel stimuli. In order to produce
open-ended categorization, the subject must be exposed to different classes, and must
compare their members (Sutton & Roberts, 2002). For example, one cannot acquire
the 'Cat' category after only seeing cats.
4. Conceptualization
Conceptualization is defined as classification via functional (psychological)
similarities that do not need to involve perceptual similarities. As an example, let us
look on the concept 'Food'. Not all food items look the same, but all of them provide
energy and nutrition.
5. Abstract relations
Grouping can be done according to the relation between concepts. This is a complex
classification level by itself (Herrnstein, 1990), and ranges from abstract relations
such as identity and oddity, to an infinite permutation of relations between relations
(n-order relations).
1.1.3 Classification in pigeons
In the field of comparative psychology, the pigeon (Columba livia) is a well-known
animal model for categorization studies, and it exhibits remarkable abilities. This
section will review the classification levels that have been achieved by pigeons. The
summary will start with the second level, categorization by rote.
Categorization by rote in pigeons
Pigeons can correctly sort a large number of arbitrarily grouped stimuli. Vaughan and
Greene (1984) tested pigeons with 320 photographs of natural scenes which the
pigeons had to classify into two randomly assigned positive and negative groups.
Surprisingly, after more than two years of not seeing the stimuli, the pigeons were still
able to classify them correctly (Vaughan & Greene, 1984). Vaughan and Greene
showed in addition that pigeons are able to memorize 160 stimuli of squiggles. In a
2
later study, pigeons could remember even a higher number of 725 stimuli, and could
retain them after a period of six months1 (von Fersen & Delius, 1989).
Open-ended categorization in pigeons
Studies on open-ended categorization in pigeons have a long history. Pigeons have
been tested according to various categories, natural as well as artificial. In the
pioneering study by Herrnstein & Loveland in 1964, pigeons successively viewed
hundreds of photographic scenes that either contained or did not contain a human.
Pecking on a picture that contained a human (Go stimulus) was food-rewarded,
whereas pecking on a picture without humans (NoGo stimulus) was not rewarded
(Herrnstein & Loveland, 1964). The stimuli were changed daily (some were repeated
but never in the same order). Interestingly, within 7-10 sessions, the pigeons could
reliably discriminate between Go and NoGo stimuli. This ability was even more
remarkable since the Go-pictures often included humans that were partly hidden by
other objects. Since then, the ‘Human’ category was extensively studied, aiming at
understanding which features the pigeons rely on while performing this categorization
(Aust & Huber 2001, 2002, 2003, 2006; Yamazaki et al., 2007). Numerous
subsequent experiments further demonstrated the pigeons’ ability to categorize using
other natural classes, such as oak leaves (Cerella, 1980), trees (Herrnstein, 1979),
bodies of water (Herrnstein et al., 1976), a particular person (Herrnstein et al., 1976),
cats and dogs (Ghosh et al., 2004), individual pigeons (Nakamura et al., 2003), and
male versus female pigeons (Nakamura et al., 2003 ,2006). Nevertheless, in those two
last studies, the pigeons only partly transferred their knowledge to novel, previouslyunseen photographs.
However, those natural classes were not novel to the pigeons, e.g., they were exposed
to humans before the experiments. Herrnstein & DeVilliers (1980) referred to this
problem by studying the pigeons' ability to categorize the 'Fish' class, a class intended
to be both novel and natural. The pigeons could categorize this class, indicating that
novelty does not affect the categorization capabilities of pigeons. However, from the
stimuli examples cited in their paper, apparently the Go stimuli contained round
shapes (fish), whereas the NoGo stimuli contained mainly linear shapes (corals). In
1
The retention abilities were investigated after a period of six months, the pigeon might have the
ability to remember the stimuli for a longer time.
3
this situation, the pigeons could have based their discrimination only on the detection
of simple features.
Pigeons were also able to categorize artificial stimuli. An artificial category refers to
every category that does not exist in nature. One such artificial category is the ‘Styles
of paintings’ class, in which pigeons discriminated between the paintings of Monet
and those of Picasso (Watanabe et al., 1995). It is not clear, however, what 'having an
impressionistic category' means. A second category was ‘Human-like cartoons’
(Cerella, 1980;Watanabe, 2001;Matsukawa et al., 2004) and ‘Pigeon cartoons’
(Watanabe, 2001). Those cartoons were simpler than natural objects: they were either
in black and white (Matsukawa et al, 2004; and the pigeon cartoons in Watanabe,
2001), line drawings (Cerella, 1980), or with a plain, uni-colored background
(Watanabe, 2001). Others studies used artificial polymorphous stimuli in order to
question the underlying mechanisms of category formation in pigeons (Von Fersen &
Lea, 1990;Lea et al., 1993;Jitsumori, 1993;Makino & Jitsumori, 2007). Artificial
polymorphous stimuli have the advantage that feature control is simpler than in
natural categories (Von Fersen & Lea, 1990). However, for the same reason,
polymorphous stimuli do not represent natural discrimination.
Cars, and chairs were additional artificial stimuli that were studied (Bhatt et al.,
1988;Bhatt & Wasserman, 1989;Lazareva et al., 2004). In those experiments, the
stimulus world was restricted to four classes (e.g., 'Car', 'Chair', 'Flower', and 'Cat').
Overall, pigeons were able to categorize a wide variety of classes in an open-ended
manner. Yet, the artificial classes were less complex and had a more reduced
background than the natural classes. Neither of the previously studied classes, natural
and artificial, was complex as well as novel.
Conceptualization in pigeon
It is difficult to distinguish between open-ended categorization and conceptualization.
Behavioral classification without language can be explained by both levels. For
instance, pigeons' ability to discriminate between 'humans' and 'non-humans' can be
dually explained. On the one hand, the discrimination can be solved by perceptual
similarities and thus the pigeons are considered to achieve open–ended categorization.
On the other hand, one cannot rule out the possibility that pigeons attribute functions
4
to humans, such as 'big animals that provide me with food'. In this case, pigeons
possess the 'Human' concept.
Conceptual behavior in pigeons might be indicated by their ability to master reversal
tasks (Herrnstein, 1990). Vaughan (1988) grouped slides of trees into two arbitrary
groups: reinforced and non-reinforced. After reaching high discrimination
performances, the reinforcement contingencies were reversed. The contingencies were
reversed back and forth and after many reversal sessions, the pigeons could detect the
reinforced group already from the first few stimuli.
Abstract relations in pigeons
Pigeons were tested with low-level abstract relations.
Few studies showed that pigeons understood same-different relations (Young et al.,
1997;Cook et al., 1997). A recent study by Katz and Wright (2006) showed that
pigeons can even transfer a two-item same-different classification to novel stimuli.
Pigeon were also been able to solve logically analogous similar abilities such as
matching to sample (Colombo et al., 2003). Apparently the pigeons’ ability to
succeed in those tasks greatly depends on the procedure and number of stimuli
(Herrnstein, 1990; Young et al., 1997; Colombo et al., 2003; Katz & Wright, 2006;
see also Huber et al., 2005 for fast discrimination owing to a procedural change).
In other experiment, pigeons could learn inside –outside relations only after extensive
training. At first, the pigeons were trained to perform a task that could be solved via
open-ended categorization. The features of the stimuli were gradually changed, so that
at the end, the task could be solved only by understanding inside–outside relations
(Herrnstein et al., 1989). To conclude, pigeons can understand abstract relations, but
this capacity is fragile (Herrnstein, 1990).
5
Figure 1.1 Levels of classification (Modified from Herrnstein, 1990).
Arrows point to the loci of classification, filled circles indicate confirmed exemplars,
open circles indicate exemplars to which generalization is appropriate, and Xs
indicate exemplars to which generalization is not appropriate.
1.1.4 Objects and their pictorial representation
In the previous section, we discussed pictorial learning acquired in the Skinner box.
Naturally, pigeons also acquire experience and knowledge through every-day life
interactions within their 3-dimensional environment. Pigeons might use the
spontaneously learned real-world information for classification in the Skinner box.
Few researchers have studied the relationship between 3-dimensional (3D) and 2dimensional (2D) knowledge in the pigeon. Watanabe (1999) showed that object
6
familiarity enhances picture discrimination performance in the Skinner box. Other
studies showed that pigeons can infer 2D information from 3D information and vice
versa. For example, 2D-to-3D transfer was tested and validated with spherical versus
non-spherical objects (Delius, 1992). Pigeons successfully transferred information by
both 2D and 3D transfer of functionally relevant groups: food versus non-food items
(Watanabe, 1993). In those studies, the 3–dimensional objects may have been too
simple to differ appreciably from their pictures. Thus, transfer could have occurred by
mere confusion, or the inability to discriminate between the objects and the pictures.
When tested with spatial locations, pigeons could transfer 2D information to the 3D
locations, but not the other way around (Cole & Honig, 1994). The failure to achieve
a 3D-to-2D transfer was explained by the different information acquired by training
either with 2D or 3D information.
It is important that one should differentiate between recognizing common features of
the object and the picture and establishing a correspondence between a picture and its
object (Bovet & Vauclair, 2000). Both can exhibit similar behavioral transfer, but
only correspondence enables an organism to transfer knowledge about the object to its
picture and vice versa. Two recent studies addressed this problem, and both confirmed
that pigeons see correspondence between objects and their pictures. In the first study,
pigeons were tested to discriminate between two three-part objects, and they exhibited
a bi-directional transfer. Next, a second group of pigeons was trained to discriminate
between the two objects presented in twelve 3D views. The pigeons could transfer the
discrimination to novel 2D views. Combining those two studies, the authors
concluded that the pigeons see bi-directional correspondence between objects and
their pictures (Spetch & Friedman, 2006). In an elegant but indirect study, Aust &
Huber (2006) used a complementary information strategy to differentiate between the
two above-mentioned options. To this end, pigeons were trained to discriminate
photographs with incomplete human figures from photographs without humans. In the
testing sessions, the pigeons showed a tendency to peck more on the unseen body
parts than on skin patches. Aust and Huber concluded that the birds recognized the
humans in the pictures.
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1.2 Hemispheric lateralization
1.2.1 Lateralization: general introduction
Brain laterality or cerebral asymmetry refers to differential information processing
and computation by each hemisphere. Although brain laterality is still considered by
some to be a human-only trait (Leask & Crow, 2001), it is generally thought that
hemispheric
asymmetry
exists
in
the
whole
animal
kingdom
(Hellige,
1990;Vallortigara et al., 1999). The earliest evolutionary evidence for asymmetry
was found in predation wounds of Palaeozoic trilobites, which lived about 500 million
years ago. The wounds were mainly on the right side, due to a preference of the
predators to attack that side (Babcock & Robison, 1989). Behavioral lateralization
was further found in invertebrates (e.g., lateralized eye use in octopuses: Byrne et al,
2004) and in many vertebrates: fish (e.g., inspection of social visual stimuli: Sovrano
et al., 2001); amphibians (e.g., sexual behavior in newt: Green, 1997); reptiles (e.g.,
aggressive behavior in lizards: Deckel & Jevitts, 1997); birds (e.g., tool manufacture
in crows: Hunt et al., 2001); mammals (e.g., tail wagging in dogs: Quaranta et al.,
2007). Even the most well known behavioral lateralization in humans, namely,
handedness or differential limb use, was found in many other organisms. Examples
range from octopuses (Byrne et al., 2006) and toads (Bisazza et al., 1996) through
pigeons (Fisher, 1957), mice (Waters & Denenberg, 1991), and dogs (Poyser et al.,
2006), to the great apes (Rogers & Kaplan, 1996).
Interestingly, having a lateralized brain is beneficial. The more lateralized the pigeons
are, the better they binocularly discriminate grid from grain (Güntürkün et al., 2000).
Other examples exist throughout the animal kingdom. For instance, lateralized
chimpanzees were shown to catch more prey (McGrew & Marchant, 1999).
Moreover, lateralized brains are better able to process dual information. For example,
lateralized chicks pecked more grains than pebbles and, at the same time, were more
watchful for a predator (Rogers et al., 2004;Dharmaretnam & Rogers, 2005).
1.2.2 Birds as an animal model for laterality studies
Birds serve as an excellent animal model to study visual asymmetry. First, birds
depend heavily on vision, more than any other vertebrate class (Güntürkün, 2000).
Second, their optic nerves are virtually completely crossed, allowing each hemisphere
to be tested separately by occluding one eye (Weidner et al., 1985; Güntürkün, 2003).
8
Finally, birds lack a corpus callosum. In mammals, the corpus callosum is the main
connection between the two hemispheres (for a review, see Bloom & Hynd, 2005).
Birds have four commissures connecting the two hemispheres: (1) The supra-optic
decussation at the diencephalic level. (2) The tecto-tectal commissure, which can be
divided into the tectal and the posterior commissures, and two commissures at the
forebrain level: (3) The anterior commissure, and (4) The hippocampal commissure.
1.2.3 Hemispheric differences in categorization tasks
Many studies collectively show that the right eye and thus the left hemisphere
(RE/LH) and the left eye/right hemisphere (LE/RH) have different strategies.
Hemispheric dichotomy was suggested to be found in spatial relations (Kosslyn,
1987): the LH processes categorical or abstract spatial relations, and the RH encodes
coordinate information, meaning metrically precise spatial relations (Andresen &
Marsolek. 2005; for a review, see Jager & Postma, 2003). This dichotomy can be
further extend to object processing (Jager & Postma, 2003;Vauclair et al., 2006).
Object recognition is a lateralized function in the human brain (Marsolek, 1999,
Koivisto & Revonsuo, 2003; Laeng et al., 2003). The LH employs an abstractcategorical strategy and the RH utilizes a specific-exemplar mode by which it
surpasses the LH in detecting previously seen stimuli (Kosslyn et al., 1999; Koivisto
& Revonsuo, 2003; Laeng et al., 2003; Andresen & Marsolek, 2005).
Another example of asymmetry can be found in the feature type each hemisphere
encodes. The LH excels in computing local cues, whereas the RH specializes in
encoding global features2 (Evert and Kmen, 2003; fMRI study: Lux et al., 2004; Brain
damage patients: Schatz et al., 2000; but see Blanca and Alarcon, 2002 for
symmetrical local-global computation). These findings constitute the basis of the
assumption that local feature computation forms the basis of the LH's ability to detect
categories, whereas the reliance of the RH on global features confers its advantage in
exemplar detection (Kosslyn et al., 1999).
9
Global-local2 asymmetry was also found in avian brains, indicating a cerebral
lateralization pattern that resembles the human counterpart. The RE/LH performed
better in visual discrimination tasks and local features detection. In contrast, the
LE/RH relies more on global cues such as relational configuration or geometrical
spatial cues (chicks: Tommassi & Vallortigara, 2004; Vallortigara et al., 2004;
Regolin et al., 2005; marsh tits: Clayton & Krebs, 1994; pigeons: Güntürkün, 1985;
Prior et al., 2002; Vauclair et al., 2006).
This local-global asymmetry may underlie lateralization of category- versus memorybased discrimination in birds. According to Yamazaki et al., 2007, concept formation
is probably lateralized in pigeons. They found an initial RH advantage in
discriminating known exemplars. However, the LH improved and subsequently
performed as well as the RH in discriminating novel stimuli. These findings were
explained by the RH's faster acquisition speed. Further experiments, which used
manipulated stimuli such as scrambled pictures, suggested that the RH uses
familiarity and configurational cues, whereas the LH uses local, category-defining
features (Yamazaki et al., 2007).
However, hemispheric asymmetry does not mean that the entire computation is done
by a single hemisphere. Both local (elemental) and global (configural) information are
used by the pigeons in discrimination (Kirkpatrick-Steger et al., 1998) and
categorization tasks (Aust & Huber, 2003). Additionally, recent studies with animals
as well as with humans showed an advantage for dual hemispheric use (selective
feeding in chicks: Prior and Wilzeck, 2008; parallel processing in humans: Hirnstein
et al., 2008). Finally, not all studies detected those reported hemispheric
specializations. For example, Sergent's study with healthy human subjects hinted that
the dichotomy between the two hemispheres in spatial relation tasks is task
dependent, since a relation between the asymmetry degree and the stimuli’s
luminance was found (Sergent, 1991).
2
Local feature: a specific element in a stimulus, e.g., nose in a human face.
Global feature: characteristics of the whole stimulus.
The most common example for local and global features is Navon visual stimulus which is a large
letter (the global level) composed of smaller letters (the local level), e.g., the letter H composed out of
small L's (Navon, 1977).
10
1.2.4 Inter-hemispheric interaction and metacontrol
In the previous section we saw that the each of the cerebral hemispheres processes
differently. Though possessing an asymmetric brain, an organism has often has only
one holistic percept of the world. In this section we will look at the ways in which the
hemispheres interact.
Hemispheric interaction can be described along a continuum3 (Banich, 1995).
At one edge, one hemisphere can take control over the other and perform solely the
task. The choice mechanism that determines which hemisphere will dominate the task
is called metacontrol (Levy & Trevarthen, 1976). In this case, the performances under
bi-hemispheric viewing will be similar to the performances by one hemisphere, and
different from the performances of the other hemisphere (Figure 1.2 A).
Alternatively, both hemispheres can compute the task in parallel (Zaidel, 1995,
Figure 1.2 B), with each hemisphere donating its expertise in order to achieve the
complete computation. Parallel computation, however, does not mean that each
hemisphere contributes equally to the task.
On the other edge, there is an interaction type known as emergence in which
hemispheres interaction creates a new way of information processing, or even
understanding (Figure 1.2 C). The bilateral performances in this case are different
from both unilateral performances and from their sum.
The conditions that determine which type of hemispheric interaction will occur are
largely unknown. A few studies hint that that type of interaction is task dependent.
The degree of hemispheric interaction is affected by the computational demands of
the task (Belger & Banich, 1992;Bloom & Hynd, 2005). With a lateralized brain, in
simple tasks, it is more beneficial to process uni-hemispherically than to invest in
computation integration (Hellige, 1990). Metacontrol will then occur, and one
hemisphere will gain control over the task, possibly by inhibition of the other
3
When discussing about hemispheric interaction, some researchers (e.g., Zaidel, 1995) assume that
each hemisphere is an independent cognitive system. An alternative view held by M.S. Gazzaniga
suggests that the hemispheres are incapable of computing the same information, due to specialization
mechanisms (Gazzaniga, 2000;Corballis et al., 2000). Originally, both hemispheres were able to
process the same perceptual cues. However, as the LH became specialized for language, it lost the
ability to process visuo-spatial information. The RH, on the other hand, maintained this ability. Thus,
hemispheric dichotomy resulted from the lingual specialization of the LH, and it does not mirror labor
division.
11
hemisphere (Bloom & Hynd, 2005). Surprisingly, the dominating hemisphere is not
always the specialized one. This is true for split-brain humans (Levy & Trevarthen,
1976) as well as for healthy subjects (Lazarus-Mainka & Hormann, 1978;Hellige et
al., 1989;Urgesi et al., 2005). Thus, some researchers have suggested that hemisphere
dominance is not fixed, but instead is influenced by task details such as the chosen
strategy (Lazarus-Mainka & Hormann, 1978) and stimulation timing (Urgesi et al.,
2005). Unfortunately, there are only a handful of studies about metacontrol. It was
studied mostly in humans, and also with split brain monkeys (Kavcic et al., 2000).
Yet, metacontrol processes might be especially important in animals with laterally
placed eyes in which each hemisphere receives different visual input. Animals such as
octopuses, pigeons, and rats can serve as excellent subjects for studying hemispheric
interaction processes.
Figure 1.2 The continuum of hemispheric interaction:
A. Metacontrol mechanism: one hemisphere takes control over a task. (i) The
behavioral outcome. (ii) Inhibition as a proposed metacontrol mechanism. B. Parallel
computation: both hemispheres contribute to the task. C. Emergence: the interaction
between the two hemispheres leads to a new way of information processing.
12
2. Aims
The general aim of this thesis was to explore the cognitive mechanisms involved in
discrimination and hemispheric interaction in the pigeon brain.
By studying animals we were able to control the environment and the history of the
tested subjects, a necessary requirement for category formation studies. The pigeon
was chosen as the animal model for various reasons. First, we were interested in the
categorization abilities of birds from an evolutionary perspective. Second, the pigeon
is a good animal model to study brain asymmetry. Finally, pigeons can discriminate
and categorize many classes. Overall, pigeons are well suited for studying
lateralization of high cognitive abilities.
The first experiment examined the categorization ability of pigeons, its limitations,
and the role of past experience in category formation. The second study examined
whether pigeons spontaneously attend to 3-dimensional information in their daily-life
environment. The third experiment inquired the role of the two hemispheres in
complex discrimination tasks. Lastly, the fourth experiment investigated hemispheric
interaction during a binocularly conflicting situation.
Experiment 1: Category formation: borders and mechanisms
Pigeons have been tested with a wide variety of natural and artificial categories.
However, it is unclear which mechanisms facilitate category formation, and how
those mechanisms restrict their categorical ability.
This study examined whether pigeons can categorize a complex, novel and artificial
stimulus class with a rich background. By comparing the pigeons’ performances with
this class to a known and artificial class, and to a known and natural class, we were
able to determine the role of past experience in category formation.
13
Experiment 2: 3-dimensional spontaneous discrimination
Experiment 1 studied the role of pre-experimental knowledge in category acquisition.
If pre-experimental experience influences the pigeons, one should be able to detect
categorization processes in pigeons in their real-life, 3D environment. Although
previous studies examined 3D to 2D transfer, they used relatively simple stimuli. Aust
& Huber (2006) used the 'Human' class in a picture-object recognition study, but they
did not directly show that the pigeons were able to categorize humans in real life. We
designed the second experiment to directly check whether the pigeons would
spontaneously attend to and learn from their 3-dimensional environment. We chose
the 'Human' class because it is complex, and because the widely studied human
pictures are different from 3-dimensional humans due to the small size of the picture
in the Skinner box. We first investigated whether the pigeons discriminate between
two individual humans. Briefly, two groups of pigeons were fed by two different
experimenters. We checked if the pigeons would alter their behavior according to
which experimenter they saw.
Work in progress: Complex natural objects and their pictorial representation by
pigeons. This extension will examine whether pigeons can transfer the spontaneously
gained
3-dimensional
knowledge
to
2-dimensional
representations
and
(spontaneously) discriminate, or even categorize photographs of those two
experimenters in the Skinner box.
Experiment 3: Hemispheric interaction in complex discrimination tasks
After revealing how the environment affects categorization, we were interested to
determine how the brain might encode this information. Since the pigeon possesses
both high visual cognition and a highly lateralized brain, we proceeded to study how
each of the two cerebral hemispheres handles discrimination tasks. This study enabled
us to link our findings with human behavior, in which hemispheric differences in
discrimination tasks were also shown. Pigeons were tested monocularly seeing with
known stimuli drawn from two different classes used in experiment 1. One class was
learned by rote whereas the second one was an open-ended category. If indeed two
dissociable subsystems participate in visual categorization, we would expect to find
lateralization that is dependent on the specific discrimination strategy. However, if
categorization does not depend solely on local features, then those two subsystems
might have to work together to discriminate visual stimuli.
14
Experiment 4: Metacontrol computation as a strategy to solve binocularly conflicting
discrimination
The third study provided hints as to which task a single hemisphere might be
insufficient. Hemispheric interaction is a continuum that ranges from metacontrol to
emergence. There are relatively few studies on metacontrol. Not only that, but this
phenomenon was never studied outside the mammalian Class. Can we find
metacontrol process in the lateralized pigeon? In our final experiment, we wanted to
reveal the kind of interaction that will occur when a binocular task can be solved by a
single hemisphere alone. To investigate this question, we taught monocularly seeing
pigeons a different color discrimination task with each hemisphere. After reaching the
criterion, the pigeons were binocularly confronted with a double-color discrimination
task that had a different solution according to each hemisphere. When there are two
independent monocular information channels, how will decisions be made during
binocular viewing? Will a single hemisphere always dominate binocular viewing or is
decision making a dynamic, individual-dependent process?
15
3. Experiment 1:
Category formation: borders and mechanisms
3.1 Introduction
The categorization ability of pigeons was tested with a variety of classes: simple and
complex, novel and known, natural and artificial. So far, the pigeons were never
tested with a complex class that was both novel and artificial. In the first experiment,
pigeons were tested with three complex classes: novel and artificial, known and
artificial, known and natural, all of which are complex and possess rich background.
By comparing the pigeons' performances with those three classes we were able not
only to determine if pigeons can learn a complex, novel, and artificial category, but
also to explore the mechanisms that lead to category formation. Can the pigeons
categorize all three classes? If not, which variable restricts their ability, artificiality or
novelty?
3.2 Materials and Methods
3.2.1 Subjects
15 homing pigeons (Columba livia) participated in this study. Nine pigeons were used
throughout the whole study, while a tenth pigeon was tested from the second phase of
the experiment (the 'Human’ class) onwards. Five additional pigeons participated in a
control study. All studies were carried out according to the specifications of the
German law for the prevention of cruelty to animals and hence, the European
Communities Council Directive of 24 November 1986. The birds were housed
individually in a room with other conspecifics and placed on a 12/12h light/dark
cycle. They were kept at 80–90% of their free feeding weight. Food was provided
during the experiment and after experimental sessions. Water was freely available in
their home cages throughout the experimental period.
The pigeons were trained on average 5 times a week.
16
3.2.2 Apparatus
The experiment was conducted in a 33(w) x 34.5(d) x 36(h) cm3 Skinner box, which
was made in the faculty workshop. The box was equipped with a house light (on the
side panel), a centered feeder containing mixed grains (on the front panel, 14 cm from
the ceiling, 5 cm from the right side), a feeder light located above the feeder that was
lit simultaneously with the feeder activation, and a transparent centered pecking key
(on the front panel, its upper right corner was located 14(w) x 7.5(h) cm from the
upper right corner of the Skinner box). Through the pecking key, the pigeons viewed
the 5(w) x 2.8(h) cm2 stimuli which were presented on a TFT LCD monitor
(Brilliance 150P2, Philips), with a resolution of 1024x768 Pixels. Pecking correctly on
the pecking key reinforced the pigeons with the activation of the feeder. Experimental
sessions and data collection were controlled by a Pentium PC running MATLAB (The
MathWorks, Inc., Natick, USA).
3.2.3 Stimuli
Initial training
A white 5(w) x 2.8(h) cm2 square was used during the autoshaping training.
Experimental stimuli
Class is a group (of pictures) containing common properties, e.g., the appearance of
human beings in the picture. Three different classes were used in this study:
‘Imaginary cartoon’, ‘Human’, and ‘Human cartoon’ (Table 3.1). All pigeons were
first trained and tested with the ‘Imaginary cartoon’, then with ‘Human’ and finally
with the ‘Human cartoon’.
'Imaginary cartoon' Class
9
Artificiality
9
Novelty
9
Complexity
9
Rich background
Table 3.1 Properties of the experimental stimuli.
17
‘Human cartoon’ Class
'Human' Class
9
8 (?)
9
9
8
8 (?)
9
9
'Imaginary cartoon': An artificial and novel class
Stimuli were based on video frames taken from Pokémon, a kids fantasy world
created
by
Nintendo©
1995-2007
Nintendo/
Creatures
Inc.
(http://www.pokemon.com). Go (positive) stimuli were defined by the presence of the
character 'Pikachu': the main character in Pokémon which is visually characterized by
yellow color, red dots on the cheeks, zigzagged tail and long ears (see Fig 3.1). Other
Pokémon characters appeared in the Go and NoGo stimuli. All Pokémon characters
appeared in various sizes and angles, and could be only partially shown. The only
consistent difference between Go and NoGo stimuli was the appearance or lacking of
the character ‘Pikachu’. To prevent fundamental but non-conceptual differences
between the two stimuli types, changes were done to the video frames using Adobe
Photoshop © 2007 (Adobe Systems Inc. San Jose, USA). The dominant yellowish
color of ‘Pikachu’ as well as the two red dots on its cheeks were added to NoGo
stimuli, mainly by re- coloring NoGos’ characters. The number of imaginary and
human-like characters in a stimulus as well as the backgrounds between the Gos and
the NoGos were controlled (Figure 3.1). Since ' Pikachu ' and the other Pokémon
characters were drawn characters that the pigeons have never seen before, this class
was artificial and novel4 to the pigeons.
Figure 3.1 Examples of ‘Imaginary cartoon’ class stimuli.
4
Are the Pokémon characters are really novel to the pigeons? One may not rule out the possibility that
an organism that possesses 'Pikachu'-like features can exist in nature, such as a yellow dog. However, it
is highly unlikely that such an organism would simultaneously possess all of 'Pikachu' features.
Furthermore, such instances of exposure would have been very limited, and it is unlikely that all our
pigeons, who most of their adult life were in the lab, would have been exposed to such an organism. In
any case, even if such exposure did occur, it would be much fewer compared to their daily interaction
with humans.
18
'Human': A natural and presumably known class
Stimuli were royalty free photographs taken from the Photodisc collection ©19992007 Getty Images, Inc. (www.photodisc.com). Go stimuli were characterized by the
presence of human/s that varied in number, identity, size, race, gender, age, posture,
position, completeness, relative size in the photograph and background. As in
‘Imaginary cartoon’ category, we aimed for homogeny between the Gos to the
NoGos. All of the NoGo stimuli contained clear objects. The number of objects, the
background and the complexity of the stimuli were controlled between Gos to NoGos
(based on visual judgment of the experimenters). Figure 3.2 illustrates the stimuli.
The ‘Human’ class was a natural class since every-day life photographs were used.
Since pigeons were in daily contact with humans, and since it was shown previously
that pigeons can transfer between 3-dimensional to 2-dimensional information (e.g.,
(Spetch & Friedman, 2006), we suggested that the 'Human' class was not novel for the
pigeons.
'Human cartoon': An artificial and presumably known class
Stimuli were based on frames taken from ‘Family Guy’, an animated TV show
created by Twentieth Century Fox Film Corporation © 2005 Twentieth Century Fox
Corporation (http://www.familyguy.com). Go stimuli were characterized by the
presence of human cartoons, in various number, identity, size, race, gender, age,
posture, position, completeness, relative size in the photograph and background.
NoGo stimuli contained other animated characters such as dog as well as inanimate
Figure 3.2 Examples of ‘Human’ class stimuli.
19
objects. The Gos and the NoGos were similar in the number of objects, background,
and complexity of the stimuli (Figure 3.3).
The 'Human cartoon' class was an artificial class since it contained drawn characters.
We hypothesized that this class was not novel to the birds since it depicted human
characters.
76 Go and 76 No Go stimuli were prepared for each class, and were assigned
randomly to the discrimination training or to the transfer test.
3.2.4 Procedure
Initial training
The pigeons were trained to peck on a lighted pecking key (white square) in a
standard autoshaping procedure containing 40 trials. The white square was presented
for 5 seconds followed by 3 seconds of food access. Seven pigeons pecked
spontaneously on the pecking key. The other three subjects had to be manually
shaped. After the pigeons started to respond to the pecking key, they were trained
with a continuous reinforcement schedule. Afterwards, the pigeons were
progressively trained with variable ratio (VR4, VR7, VR10), fixed interval (FI3, FI5,
FI10) and variable interval (VI10, VI15 and VI20) schedules. Each schedule
proceeded until the pigeons responded correctly to more then 85% of the trials, in two
consecutive sessions. Each session contained 40 trials.
Figure 3.3 Examples of ‘Human cartoon’ class stimuli.
20
Discrimination training
A Go-NoGo task was used to teach the pigeons the discrimination. The schedule used
was similar to the one used by Yamazaki et al. (2007). A trial began with 20s inter
trial interval. Next, a stimulus was presented for 10s FI and then for 5s VI. In a Go
(positive) trial the pigeons had to respond two or more times i.e., peck on the pecking
key, and thus were rewarded with 3s food access and illumination of the feeder. In a
NoGo (negative) trial, a stimulus was presented for additional 8s after the VI period,
in which the subjects had to refrain from responding. A NoGo trial was terminated
only after no response occurred for 8s. Each session consisted of 40 trials. The 40
stimuli were chosen pseudo-randomly from a larger stimuli set containing 40 Gos and
40 NoGos such that no more than 3 Gos or NoGos were presented consecutively. On
average, half of the stimuli were Gos.
Transfer Test:
The transfer test checked the generalization ability of the pigeons, by analyzing their
performances with novel stimuli. Obtaining an open-ended category is indicated by
the correct discrimination of novel stimuli.
Six transfer tests were conducted, each contained different novel stimuli. Each
transfer session contained 52 stimuli, 40 of which were known stimuli used in the
discrimination training, 12 were novel stimuli (transfer stimuli). Half of the known
and half of the novel stimuli were Gos. In total, the pigeons were tested with 72
unrepeated transfer stimuli. The transfer test procedure was identical to the
discrimination training one, and the pigeons received feedback also in the transfer
trials. The order of the stimuli within a single session, as well as the order of the
transfer sessions among the pigeons was randomized.
3.2.5 Controls
1. ‘Human cartoon’ skin color
To verify that the pigeon’s ability to discriminate between ‘Human cartoon’ stimuli
containing human figures to stimuli lacking them did not depend solely on the humanlike skin color, the ‘Human cartoon’ transfer test was repeated. This time, one or more
‘Human cartoon’ NoGo characters/objects in each NoGo transfer stimulus were
colored with human-like skin color, taken from different Go transfer characters
(Figure 3.4). A similar control was not made with the 'Human' class, since Aust and
21
Huber (2002, 2006) showed that pigeons do not classify skin patches as belonging to
this class.
2. Testing order
In order to rule out that the testing order affected the categorization capabilities of the
pigeons with ‘Imaginary cartoon’ (the first class tested), we repeated the ‘Imaginary
cartoon’ transfer test after the pigeons were tested in the 'Human' and ‘Human
cartoon’ transfer tests and the ‘Human cartoon’ skin color control. Prior to the transfer
test the pigeons were re-trained in the ‘Imaginary cartoon’ discrimination till criterion.
3. Inter-class similarities in ‘Imaginary cartoon’ stimuli
All the NoGo ‘Imaginary cartoon’ transfer stimuli contained other non-human
Pokémon characters. In the ‘Human cartoon’ 12 out of the 36 NoGo transfer stimuli
contained non-human animated characters. This might have caused the ‘Imaginary
cartoon’ transfer test to be more difficult for the pigeons than the ‘Human cartoon’
one. Therefore, another ‘Imaginary cartoon’ transfer test was conducted, in which
only 12 ‘Imaginary cartoon’ NoGo transfer stimuli contained animate characters.
This control also studied if the pigeons' differentiated between the Pokémon
characters. If the pigeons performed significantly higher with this transfer test
compared with the original one, it is possible that they did not discriminate between
the Pokémon characters.
Controls 2 (Testing order) and 3 (Inter-class similarities in ‘Imaginary cartoon’
stimuli) were done on average 10 months after the ' Imaginary cartoon ' transfer test.
4. Picture-photo transfer
This control checked the assumption that the pigeons transfer their already established
'Human' knowledge to 'Human cartoon' stimuli. Five naïve pigeons were trained in the
'Human cartoon' discrimination training. After reaching criterion they were tested in
the six 'Human cartoon' transfer tests. Immediately afterwards the transfer tests were
repeated, however this time the pigeons saw the ‘Human’ transfer stimuli mixed
within the ‘Human cartoon’ training stimuli.
22
3.2.6 Analysis and Statistic
Performances were measured in term of the rho value (Herrnstein et al., 1976). Rho
compares the number of pecks in Go versus NoGo trials in a single session using the
U value of the Mann-Whitney U test divided by the product of the number of Go and
NoGo trials. Ties were zero-ranked, as pecking equally on Go and NoGo stimuli
implies random and thus incorrect discrimination (Clauß & Ebner, 1975).
The criterion in discrimination training was rho≥.85 in three consecutive sessions.
The transfer test criterion was set to rho≥.80 for the 72 novel stimuli. The transfer
criterion was lower since this task is more demanding.
One way ANOVA compared performances between the three classes. Post-hoc
analysis used Bonferroni correction as the number of all possible contrasts was small.
Paired t-tests analyzed the pigeons' performances with known compared to novel
stimuli. One way ANOVA and pair t-tests were further used to analyze the controls’
results.
All reported mean values are in the format of mean ± SEM.
Figure 3.4 Examples of NoGo stimuli used in the 'Human cartoon’ skin color control.
23
3.3 Results
3.3.1 Initial training
All in all, the pigeons needed on average 43.9±2.66 initial training sessions, from the
autoshaping till they finished successfully the VI20 procedure.
3.3.2 Discrimination training
The acquisition time to reach the learning criterion of rho≥.85 in three consecutive
sessions differed between the different classes. The pigeons needed 44.78±6.84
sessions to obtain the criterion with the ' Imaginary cartoon '. 14±1.44 and 11.8±1.33
sessions were required till reaching the criterion with the ‘Human’ and the ‘Human
cartoon’ classes respectively (Table 3.2).
Those differences in acquisition time between the classes were significant
(F(2,26)=23.084, p=.000). The pigeons learned significantly slower with the
‘Imaginary cartoon’ class than with the other two classes (p=.000, Bonferroni
corrected). No significant difference between the acquisition time of ‘Human’ versus
the ‘Human cartoon’ was found (p=1.00, Bonferroni corrected). Comparing
performances in the first session with ‘Human’ (rho=.261±.041) versus ‘Human
cartoon’ (rho=.569±.053) revealed that the initial performances with the ‘Human
cartoon’ were significantly higher (t(9) =-5.514 , p =.000).
Discrimination training
number of sessions till criterion
Transfer test
% of pigeons that reached criterion
with novel stimuli
44.78±6.84
0
‘Human’ Class
14±4.55
90 + 10
‘Human Cartoon’ Class
11.8 ±4.2
70
'Imaginary cartoon’ Class
Table 3.2 Summary of the pigeons’ performances.
24
3.3.3 Transfer tests
For all three classes, the average performances in the six transfer tests were
significantly higher with known versus novel stimuli (‘Imaginary cartoon’: t(8) =
15.801, p =.000, ‘Human’: t(9)=6.359, p=.000, ‘Human cartoon’: t(9) = 4.155, p
=.002). The performances with novel stimuli varied among the classes.
Figure 3.5 shows the mean performances of each pigeon in the ‘Imaginary cartoon’
transfer test. The mean performances were rho=.94±.054, and rho=.614±.022 for the
known and novel stimuli respectively. Although the pigeons learned to discriminate
the known stimuli, they were incapable of distinguishing between novel Gos to novel
NoGos. This is exemplified in Figure 3.6 which shows the performances of a single
pigeon in a single transfer session.
Figure 3.5 The pigeons’ performances in the ‘Imaginary cartoon’ transfer test.
The gray line represents the transfer test criterion. Error bars represent SEM.
Number of pecks
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Trials
Novel Go stimuli
Novel NoGo stimuli
Known Go stimuli
Known NoGo stimuli
Figure 3.6 Example of an individual's performances in a single ‘Imaginary cartoon’
transfer test. The pigeon discriminated correctly the known training stimuli
(rho=.947), but failed to discriminate the novel transfer stimuli (rho=.528).
25
We further analyzed the mistakes the pigeons made, in order to see if the pigeons had
difficulties with specific pictures. For every transfer test we calculated the average
pecks on transfer stimuli, Gos and NoGos. Every NoGo stimulus on which the
pigeons pecked more times then the average was regarded as a causing-mistakestimulus. This calculation was possible for seven out of the nine pigeons. In the six
transfer tests, only seven out of the 36 NoGo stimuli were mistaken by more than half
of the pigeons. None of those stimuli was mistaken by all the pigeons. The pigeons'
low performances were not due to some problematic NoGo stimuli.
With the 'Human' class the results were different. The mean performances were
rho=.921±.008 with the known 'Human' stimuli, and rho=.841±.013 with the novel
'Human' stimuli. Nine out of the ten pigeons performed above the transfer criterion of
rho≥.80 (Figure 3.7) while the remaining pigeon approached the criterion with mean
performance of rho=.792 (pigeon no.1 in Figure 3.7).
The last tested class was the ‘Human cartoon’. The mean performances with the
known ‘Human cartoon’ stimuli were rho=.892±.017. The pigeons achieved
rho=.807±.026 with the novel stimuli, with seven out of the ten pigeons performing
above the transfer criterion (Figure 3.8). One pigeon (pigeon no.6 in Figure 3.7)
approached criterion with mean performances of rho=.796.
Figure 3.7 The pigeons’ performances in the ‘Human’ transfer test.
The gray line represents the transfer test criterion. Error bars represent SEM
26
The performance with novel stimuli significantly differs between the three classes
(F(2,26)=32.154, p=.000). The pigeons were significantly worse with novel
‘Imaginary cartoon’ stimuli than with ‘Human’ and 'Human cartoon' novel stimuli
(p=.000, Bonferroni corrected). No significant difference was found between the
‘Human’ and the ‘Human cartoon’ novel stimuli (p=.805, Bonferroni corrected).
Known stimuli in the transfer tests were discriminated similarly across the three
classes (F(2,26)= 2.581, p=.095). Table 3.2 summarizes the results.
3.3.4 ‘Human cartoon’ skin color control:
The pigeons were tested a second time with the ‘Human cartoon’ class. The objects in
the NoGo transfer stimuli were colored in the human-like skin color. This control
examined if the ‘Human Cartoon’ task was a mere color discrimination for the pigeon,
i.e., if the pigeons used the human-like color skin as the only critical feature. Whereas
the mean rho value with the known stimuli was .936±.0522, only two pigeons
performed above the rho≥.80 criterion with the colored transfer stimuli (Figure 3.8).
However, no significant difference was found between the pigeon’s performances in
the two ‘Human cartoon’ transfer tests (t(9)=1.604, p=.143), with and without color
adjusted NoGos.
To verify the results, two additional 'Imaginary cartoon' transfer tests were carried:
3.3.5 Testing order control:
This control studied if the order by which the classes were tested affected the transfer
performances. To this end, the pigeons were re-trained with the known
Figure 3.8 The pigeons’ performances in the two ‘Human cartoon’ transfer tests.
The gray line represents the transfer test criterion. Error bars represent SEM
27
‘Imaginary cartoon’ stimuli. They needed 5.111±.455 sessions till criterion, ranging
from 3 to 7 sessions. Then we repeated the 'Imaginary cartoon' transfer test. The mean
performances were rho=.928±.097 for known and rho=.614±.17 for novel5 stimuli (for
statistical analysis see below).
3.3.6 Inter-class similarities control:
The purpose of this control was to test if the failure of the pigeons to pass the
‘Imaginary cartoon’ transfer test can be explain by the high number of animated
NoGo characters. The mean performances in this control were rho=.947±.046 and
rho=.671±.138 for the known and novel stimuli correspondingly.
No difference between the three 'imaginary cartoons' transfer tests was found
(F(2,24)=.357, p=.704 for known stimuli; F(2,24)= 2.131, p =.141 for novel stimuli).
In some transfer sessions the pigeons performed above the transfer criterion. The total
number of those sessions was 8/54 sessions in the first transfer test, 14/54 in the
testing order control, and 14/54 in the inter-class similarities control. Further
indicating similar performances in the three ‘Imaginary cartoon’ transfer tests.
3.3.7 Picture-photo transfer control
The five pigeons needed on average 21.6±5.840 sessions, ranging from 10 to 33, to
reach the discrimination criterion with the 'Human cartoon' class.
The mean performances in the ‘Human cartoon’ transfer test were rho=.893±.021
with the known stimuli, and rho=.738±.089 with the novel stimuli. Two pigeons
passed the transfer test, two did not reach criterions and one pigeon almost reached
criterion.
The mean performances in the ‘picture to photo’ transfer test were rho=.914±.023 and
rho=.712±.042 with the known 'Human cartoon' and novel 'Human' stimuli,
respectively. None of the pigeons reached criterion with the 'Human' novel stimuli.
Still, no performance difference was found between those transfer tests (t(4)=.790, p
=.474). Furthermore, no difference in the 'Human cartoon' transfer performances was
found between the five control pigeons to the five experimental pigeons with the
lowest transfer performances (F=.085, p=.778).
5
The pigeons were tested with the same transfer stimuli before, thus those stimuli are not novel.
However, the pigeons saw those stimuli only once, meaning that those stimuli are not completely
known to them. If this lack of pure novelty affected the test abilities, it should have improved the
transfer performances.
28
3.4 Discussion
The pigeons differ in their categorization abilities with the three classes. None of the
pigeons acquired the 'Imaginary cartoon’ class. The pigeons discriminated correctly
only learned exemplars from this class. On the other hand, all pigeons categorized the
‘Human’ class. With the ‘Human cartoon’ class, the results were intermediate; 80% of
the pigeons acquired this class.
With the 'Imaginary cartoon' class the pigeons took almost four times longer than the
other two classes to reach the training criterion. With the 'Human' and the 'Human
cartoon', the pigeons needed a short duration to achieve the discrimination. Those
results match previous studies in which the pigeons needed 12 sessions to learn the
'Human' class (Aust & Huber, 2001;Yamazaki et al., 2007). The difference in
acquisition time between the classes indicates that there were difficulties in
discriminating ‘Imaginary cartoon’ Gos from ‘Imaginary cartoon’ NoGos, compared
with discriminating of human figures from non-humans, either real or drawings.
With the ‘Imaginary cartoon’ class, the pigeons discriminated incorrectly novel
stimuli. The pigeons were unable to extract the categorical information from the
learned stimuli, although they could memorize the highly complex discrimination
training
stimuli.
Memorizing
specific
exemplars
without
generalizing
the
discrimination to novel stimuli is a strategy known as categorization by rote
(Herrnstein, 1990). The pigeons’ memory for this stimuli were vey robust. The birds
needed just a minimal number of sessions until they achieved again the training
criterion with the discrimination stimuli, even though 10 months separated the initial
transfer test from the re-training (Control 2: Testing order).
Most of the birds generalized their discrimination to new instances of the ‘Human’
and the ‘Human cartoon’ classes. They acquired the category or achieved open-ended
categorization (Herrnstein, 1990) with both the 'Human' and the 'Human cartoon'
classes. The testing order control indicated that the pigeons' failure in the 'Imaginary
cartoon' transfer test did not result from the order in which the pigeons were
examined.
The pigeons’ transfer performances with the ‘Human cartoon’ do not go along with a
previous study (Watanabe, 2001). In this experiment, pigeons had to discriminate a
29
single person from other people, and pigeons from other birds. The stimuli were either
photographs or cartoons. Pigeons generalized their discrimination with photographs of
people, photographs of pigeons, and cartoons of pigeons, but not with cartoons of
people. Yet the pigeons were presented with only three novel stimuli, perhaps with
more novel stimuli generalization might have taken place.
In the 'Imaginary cartoon' discrimination task, a single character had to be
discriminated, whereas in the 'Human' and in the 'Human cartoon', a group had to be
discriminated. Thus, the 'Human' and 'Human cartoon' discrimination tasks represent a
basic level of abstraction6 (Rosch et al., 1976). However, the 'Imaginary cartoon' Go
character is significantly different from other Pokémon characters7, and this character
varied among the different Go stimuli (e.g., the exact yellowish color). Thus, one can
conclude that the Go 'Imaginary cartoon' character shares more features with itself
than it shares with other Pokémon characters; meaning that also in the 'Imaginary
cartoon' discrimination task, a group had to be discriminated: "the group of Pikachu".
Hence, the differences in generalization abilities with the three classes cannot be
explained by the level of abstraction. Likewise, the difference in transfer
performances between the three classes cannot be explained by the use of human-like
skin color as the primary feature for the discrimination (Control 1:‘Human cartoon’
skin color), or a possible high similarity between 'Imaginary cartoon' Gos and
'Imaginary cartoon' NoGos (Control 3: Inter-class similarities).
The classes that were tested differed in their general properties: The 'Imaginary
cartoon' was an artificial and novel class. The natural 'Human' class was known to the
pigeons as the result of meaningful daily experience (they were fed by humans). The
'Human cartoon' class is an artificial class since it represents cartoon drawings. Both
the ‘Human cartoon’ and ‘Imaginary cartoon’ were artificial, and shared picturecomic characteristics such as distortion of nature, imaginary characters and strong
colors. Thus, from the results it is clear that the element of artificiality cannot explain
the transfer performances. We claim that the novelty variable can explain our results.
6
The basic level of categorization is the one that maximizes the number of distinctive features.
Example to a basic level is a 'chair' in opposed to 'furniture' which is a superordinate level, and a 'beach
chair' which is a subordinate level.
7
A few Pokémon characters from the original series are similar to the target (Go) character used in
'Imaginary cartoon'. We did not present those characters to the pigeons.
30
The categories ‘Human’ and ‘Human cartoon’ had in common the familiar item
'human' as the positive item. The pigeons might have recognized human
characteristics in the human cartoons. Thus, the ‘Human cartoon’ class was indirectly
known to the pigeons. Three more points support this idea. First, the acquisition speed
of the ‘Human cartoon’ was fast, and in absolute value even faster than the ‘Human’
class acquisition. Second, the pigeons performed significantly better in the first
‘Human cartoon’ class discrimination training session, compared to the first training
session with the ‘Human’ class. Finally, the picture-photo transfer demonstrated that
the pigeons saw a correspondence between human pictures and human photographs,
since they responded similarly to novel 'Human cartoon' stimuli as well as to 'Human'
stimuli.
Is familiarity with the 'Human cartoon' a result of previous training with the 'Human'
class, meaning with photographs containing humans? Or did the pigeons connect
between 3D humans and the 'Human cartoon' stimuli (a object-cartoon transfer)? The
training with the 'Human' photographs facilitated the 'Human cartoon' discrimination.
The pigeons that were taught only the 'Human cartoon' needed a longer time to reach
the 'Human cartoon' discrimination training criterion as well as performed worse with
the novel stimuli. Still, this duration was shorter than the duration needed with the
'Imaginary cartoon' class, and the pigeons responded similarly to the novel 'Human'
and to the novel 'Human cartoon'. Thus, the pigeons used the every-day-life
knowledge about humans as well as the 2-dimensional 'Human' class knowledge to
solve the 'Human cartoon' transfer test. The nature of exposure possibly influences the
transfer between the dimensions (Cole & Honig, 1994). Humans, their photographs,
and their representation in drawings maintained the same functional relevance to the
pigeons. The birds were fed by humans, and pecking on stimuli containing humans
also led to a food reward. It would be interesting to see how altering the reward value
of the stimuli, e.g., rewarding them by pecking on non-human pictures, would affect
the pigeons' performances.
The idea of past experience influencing category formation is not new.
Herrnstein and Loveland (1964) suggested that their pigeons probably formed the
‘Human’ class prior to the experiment, since they learned the discrimination rapidly.
31
Monkeys learned same-different task better when the task was conducted with known
objects (Neiworth & Wright, 1994). Similarly, infants use knowledge gained from
everyday life for object segregation (Needham et al., 2006). Also in human adults,
prior knowledge influences concept formation (Murphy & Allopenna, 1994).
To summarize, while pigeons were unable to categorize a novel and artificial class,
they could categorize a natural-known class and an artificial-known class.
Categorization of an artificial class was possible when it was (partially) known. We
suggest that past experience plays an important role in pigeon's (2-dimensional)
category formation. Without previous knowledge the categorization abilities of
pigeons are restricted.
32
4. Experiment 2:
3-dimensional spontaneous discrimination
4.1 Introduction
The first experiment showed that previous experience helps to categorize in the
Skinner box.
The second experiment tested whether being in their home cages, the pigeons indeed
attend to humans. More specifically, we examined whether pigeons spontaneously
discriminate between two individuals humans.
4.2 Materials and Methods
4.2.1 Subjects
Twelve pigeons (Columba livia) were studied in the current experiment. Those
pigeons were dark incubated and raised in the lab. They were the subjects of previous
behavioral experiments aiming to check their lateralization such as grid-grain
discrimination. There was no relation between prior studies to the current one. The
pigeons were in their 100% of their free feeding weight. Water and grit were freely
available. All other keeping conditions and experimental regulations were identical to
the ones described in experiment 1 (3.2.1).
4.2.2 Procedure
The pigeons were divided into two groups (Figure 4.1). Each group was fed daily by a
different experimenter. The experimenters entered the room successively, while
variables like the person who entered first, time of entry, and time interval between
the entrances of both experimenters were randomized. One experimenter was a female
(experimenter A) and the other was a male (experimenter B). Throughout the feeding
sessions, both experimenters wore a white lab coat, and the female's hair was tied.
Neither of the pigeons was exposed to the experimenters prior to the study, and none
of the experimenters entered the housing room except during the feeding sessions.
33
The behavior of the pigeons during the feeding sessions was video recorded using a
Sony DCR-TRV725E Handycam (Sony Deutschland GmbH, Berlin, Germany).
Previously to the feedings, the camera was placed in the room by a third person who
differed across the days. Only the first feeding session in each day, when both groups
anticipated food, was further analyzed. Therefore, we had two experimental
conditions, one with each experimenter. 18 sessions from each experimental condition
were collected.
4.2.3 Controls
Three control sessions were recorded.
1. The pigeons' activity was recorded while an unknown person entered the room.
This control was done twice, once with an unfamiliar male and once with an
unfamiliar female.
2. The pigeons were recorded while both experimenters were present in the room.
4.2.4 Analysis
The activity of the groups shortly before feeding and while one of the experimenters
was present in the room was measured. From each recording session, a 10 seconds
video before the actual feeding was constructed, using Pinnacle Studio Plus Version
10.7 (Pinnacle Systems Inc., Mountain View, USA). During those 10s, one of the
experimenters was standing in front of the cages holding food. The 39 videos (36
experimental sessions + 3 controls) were assigned in two random orders to two
movies. 79 German-speaking psychology students (78% females, 24.015±.669
average age) ranked the videos; the first movie was ranked by 54 students, the second
by 25 students. For their participation, the students received either two Euros or half
an hour record of experiment participation. The students were instructed to rank the
activity level of each pigeon group in all videos, using a scale from 1 to 6 (1-No
activity; 6- Highest activity). We introduced a 3-second intermission between
individual videos to allow subjects to fill their rankings in the questionnaire. Prior to
the experiment, the human subjects were shown a sample video of activity level 1 of
both pigeon groups, in order to produce a reference point for the subsequent rankings.
This video was recorded when no one was in the room.
The full questionnaire (translated from German) appears in Appendix 1.
34
4.2.5 Statistics
The relative activity of the groups in each video was calculated by subtracting the
averaged ranked activity of group B from the average ranked activity of group A.
⎧⎪ n
⎫⎪
⎨ activitygroupA− activitygroupB ⎬
⎪⎩ i =1
⎪⎭
∑
Formula: Relative activity =
n
whereas n=79
The values ranged between -5 to +5.
A positive value indicated that group A was more active, while a negative value
pointed that the group B was more active. Less then 10% difference between the
groups (relative activity between -0.5 and 0.5) was regarded as equal activity.
We then applied one way ANOVA and paired t-tests to compare between the activity
rankings of the two groups in the two experimental conditions.
All reported mean values are in the format of mean ± SEM.
4.2.6 Hypothesis
We hypothesized that the groups would differ in their activities depending on the
experimenter who was present in the room. The group whose feeding experimenter
was located in the room would then be the more active group.
Figure 4.1 The experimental groups.
Twelve pigeons were divided into two groups: A and B. Each group was fed daily by a
different experimenter. Each square represents a cage, a letter represents a group.
35
4.3 Results
The rankings of both movies were combined in the analysis, since there was no
difference between the two.
The average relative activities were 2.05±.18 and -1.33±.27 for sessions in which
experimenter A or experimenter B were present in the room, respectively. The relative
activities in 32 out of 39 videos were in the hypothesized direction. Group A was
ranked as more active in 17 out of 18 videos in which experimenter A was located in
the room. Group B was more active in 13 of 18 cases in which its experimenter was
present (Table 4.1). Active pigeons flapped their wings, and moved more vigorously
from side to side. In general, group B was less active as can be seem from the
absolute value of the average activity rankings: 1.33 for group B and 2.05 for group
A. The difference in the relative activity between the two experimental conditions was
significant (F(1,34)=108.354, p=.000): group A was ranked as more active when
experimenter A stood in the room (t(17)=5.018, p=.000), and the pigeons in group B
were significantly more active when experimenter B was in the room (t(17)=3.413,
p=.003). Fig 4.2 is an example of the pigeons' behavior when experimenter A was in
the room. One can see high activity of group A pigeons and a base-line activity of
group B pigeons. In addition, we compared which group was more active, given
which experimenter was present in the room. When experimenter A was in the room
group A was more active (t(17)=11.175, p=.000), and vice versa for experimenter B
(t(17)=4.775, p=.000).
No difference was detected between the groups when an unfamiliar person was in the
room. When both experimenters were simultaneously present in the room, Group B
was ranked as more active (-1.234). Table 4.1 summarizes the results.
Result
Group A more active
Group B more active
Condition
No difference
between the groups
Experimenter A
94.44
0
5.56
Experimenter B
5.56
72.22
22.22
0
0
100
0
0
100
Both
None
Table 4.1 Distribution of activities in the two experimental conditions and the
controls, in percentages.
36
During the study, the pigeons increased their discrimination abilities. As the testing
progressed, the pigeons in each group increased its activity towards the experimenter
who fed them (Figure 4.3).
Fig 4.2 A frame from a movie in which experimenter A was in the room.
Group A
6
GroupB
Relative activity
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Time
Fig 4.3 Discrimination abilities during the experiment: the absolute ranked activity of
both groups when their corresponding feeder attended the room, during the 18
experimental sessions.
37
4.4 Discussion
This experiment compared the activity of two pigeons' groups towards two
experimenters who had different functional relevance to the birds. The pigeons
exhibited spontaneous behavior that matched the person present in the room: when
experimenter A was in the room, the group she fed, i.e., group A was more active;
when experimenter B was in the room, group B was more active.
By means of classical conditioning, we showed that pigeons are able to discriminate
humans in their real, 3-dimensional environment.
Recording the behavior of the pigeons in their home cages is a new way of measuring
discrimination behavior. This enables the pigeons to view life-size, 3-dimensional
humans in an environment that is natural to them. This was in contrast to previous
experiments that studied discrimination of 3-dimensional objects in the Skinner box,
where the natural parameter was absent and the physical size of the stimulus was
inevitably restricted (e.g., Spetch & Friedman, 2006). The method we applied was
similar to the one used in categorization studies with infants, where the infant's
looking time used as a measure of discrimination (Quinn et al., 1993;Needham et al.,
2006).
The pigeons did not merely look at real-life humans, but also attended to them. This
might have helped the pigeons to discriminate in the Skinner box. We concluded that
the pigeons might have acquired the 'Human' category before they had been trained
with human stimuli (experiment 1). If so, then during the discrimination training in
the Skinner box, the pigeons would only need to reorganize their already-present 3D
knowledge to the 2D knowledge, which possibly contains fewer features. Based on
visual cues only, pigeons were able to discriminate and some even categorized
between male and female pigeons (Nakamura et al., 2006). However, their
performances in this task are not perfect, presumably due to the lack of non-visual, or
ultraviolet cues (Nakamura et al., 2006). While discriminating humans from nonhumans in the Skinner box, pigeons attend to both local and global (elemental and
configuration) features (Aust & Huber, 2003). Unlike the real life, the pictures in the
Skinner box are static, the figures are smaller, do not contain non-visual cues and may
not be fully shown. With real-life stimuli, the same local/global features are preserved
38
but additional global-dimensional properties such as size differences or walking gait
of the experimenters becomes available. To all those cues pigeons may attend while
discriminating between the two experimenters.
To summarize, those results strengthen the conclusion of experiment 1. We were able
to show that pigeons were influenced by previous experience, or functionally relevant
exposure to human beings. The question whether familiarity is sufficient to facilitate
open-ended categorization is still open. Currently we are in the final stages of
investigating how specific past experience affects category formation. In this followup experiment, the pigeons' are tested in the Skinner box with photographs of the two
experimenters. We investigate if the pigeons discriminate faster pictures of the two
experimenters versus novel persons, and if the food reward which is differently
associated with the two experimenters affects the discrimination. This part of the
experiment is a direct way to examine information transfer from complex 3dimensional objects to their 2-dimensional representations.
39
5. Experiment 3:
Hemispheric interaction in complex discrimination tasks
5.1 Introduction
The last two studies added to the large repertoire of the pigeon’s high visual cognitive
abilities. A second line of previous experiments showed functional cerebral
asymmetry in the pigeon’s brain in various tasks. The current study combines those
two defining properties of the pigeon. An intriguing question is: how are the
monocular performances affected by binocular knowledge? Will the monocular
abilities and cerebral asymmetry change when the pigeons discriminate classes with
which it has different binocular strategies? How will the two hemispheres interact
when discriminating complex stimuli?
5.2 Materials and Methods
5.2.1 Subjects
The nine pigeons that were taught all three classes experiment 1, were the subjects of
the third experiment. Those pigeons learned the 'Imaginary cartoon' as a category by
rote, while they categorized in an open-ended manner the 'Human' class. The keeping
conditions and experimental regulations were as described in experiment 1 (3.2.1).
5.2.2 Apparatus
The Skinner box used in the current experiment was the one used in the first
experiment (3.2.2).
5.2.3 Stimuli
The 'Imaginary cartoon' and the 'Human' discrimination training stimuli were
employed.
40
5.2.4 Procedure
Monocular viewing was made possible using eye caps. A velcro ring was fixed to the
skin around the eyes using non-toxic glue. A cap could be attached to the ring,
blocking the viewing of a single eye and as a consequence one hemisphere. The
pigeons were adapted to the caps prior to the monocular testing sessions by wearing
them in their home cages. The animals wore a cap for about 25 minutes before each
testing session.
The schedule used was identical to the one used in experiment 1 (3.2.4).
All pigeons, except two, were tested 10 times alternately with each eye in a Go-NoGo
task, first with the ‘Imaginary cartoon’ then with the ‘Human class. With the
'Imaginary cartoon' class, one pigeon was tested 9 times with the RH, and another was
tested 8 times with the LH.
5.2.5 Analysis and Statistic
Performances were measured using the rho value.
Laterality Index compared between the performances of the LH and the RH,
measured in terms of the rho values, using the formula:
rho( LH ) − rho( RH )
rho( LH ) + rho( RH )
Values were in the range of -1 and 1. Laterality index=0 indicated similar
performances of both hemispheres. Laterality index=1 indicated total discrimination
by the LH, and a lack of discrimination by the RH.
41
One-sample t-test examined if the laterality index differed from zero.
The performances in the various conditions were compared using repeated measures
ANOVA and post-hoc Bonferroni corrections. In addition, to examine if the
monocular condition affected the general performances, we chose as a reference point
the average performances with the known stimuli in the six transfer tests8 of each
class, which were considered to represent high discrimination accuracy. Those values,
rho=.940±.054 with the ‘Imaginary cartoon’ class and rho=.921±.008 with the
‘Human’ class, were taken from experiment 1.
The results are reported in the format of mean ± SEM.
8
With the ‘Imaginary cartoon’ the pigeon were first tested monocularly and then with the transfer test.
With the ‘Human’ class they first tested with the novel stimuli, and only then monocularly. However,
this difference in testing order should not have affected the results. Transfer test is like an additional
discrimination training session with few novel stimuli. This test checks what the discrimination
strategy is, and does not induce a strategy. Furthermore, we did not see improvement in the
performances along the transfer tests, with both known and novel stimuli. Thus, the monocular
performances should not been affected from the previously conducted transfer test. As for the
‘Imaginary cartoon’, one may claim that monocular testing prior to the transfer test could improve the
pigeons’ performances in the transfer test. However, the pigeons were not able to pass the transfer test
with this class.
42
5.3 Results
With the ‘Imaginary cartoon’ class, the average performances in all sessions were
rho=.600±.070 with the RE/LH and rho=.609±.072 with the LE/RH (Figure 5.1).
Only three pigeons reached performances above .8 in three consecutive sessions (the
criterion used for passing the transfer test in experiment 1): two with the RH and one
pigeon with both hemispheres. The laterality index for the average performances with
the ‘Imaginary cartoon’ class was -.14 ±.164, and it did not differ significantly from
zero (t(8)=-.260, p=.801).
With the ‘Human’ class, the average performances in all the 10 sessions were
rho=.745±.039 with the RE/LH and rho=.723 ±.072 with the LE/RH (Figure 5.1).
Seven pigeons reached performances above .8 in three consecutive sessions: three
with the LH, two with the RH, and two with both hemispheres. On average, seven
sessions were needed to reach the criterion with the LH, and five sessions with the
RH. The laterality index for the average performances was .030±.169, and also this
index did not differ significantly from zero (t(8)=.639, p=.541). There was no
consistency regarding the hemisphere that was able to perform the discrimination with
high accuracy.
Figure 5.1 Average discrimination performances with the two classes, in the two
monocular conditions: RE/LH, LE/RH, and the reference binocular performances.
Error bars represent SEM. * represents p < .05.
43
First monocular session
Last monocular session
Average of 10 sessions
F(1,8)
p-value
F(1,8)
p-value
F(1,8)
p-value
Hemisphere
.007
.936
.115
.743
.017
.901
Class
7.269
.027
8.324
.020
6.269
.037
Interaction
.780
.403
1.876
.208
.190
.674
Table 5.1 Summary of the 2X2 ANOVA analysis.
Comparing the average discrimination performances of all 10 sessions, was done
using a 2 (hemisphere: right vs. left) X 2 (class: ‘Imaginary cartoon’ vs. ‘Human’)
repeated measures ANOVA. No significant main effect of hemisphere (F(1,8)=.017, p
=.901) as well as no significant interaction between hemisphere and class were found
(F(1,8)=.190, p=.674). There was a significant main effect of class (F(1,8)=6.269, p
=.037) with higher monocular performances with the ‘Human’ class (Figure 5.1,
Table 5.1). Similar p-values resulted when the monocular discrimination
performances in the first session or in the last session were analyzed (Table 5.1).
Fig 5.2 shows the monocular performances during the 10 monocular sessions, for both
classes. Learning effect was measured by a 10 (number of sessions) X2 (hemisphere)
X 2 (class) repeated measures ANOVA. Comparing performances along the sessions
revealed a significant learning effect (main effect of number of sessions:
(F(9,54)=3.377, p=.002), as well as a class main effect (F(1,6)=6.293, p=.046).
There was no significant hemisphere main effect (F(1,6)=.161, p=.702) as well as no
two-way and three-way interactions.
Interestingly a difference between the hemispheres was found when we compared the
first versus the last session only, employing a 2 (time: first versus last) X2
(hemisphere: right vs. left) X 2 (class ‘Imaginary cartoon’ vs. ‘Human’) repeated
measure ANOVA. A significant main effect was found for class (F(1,8)=12.879, p
=.007) and for time (F(1,8)=35.737, p =.000). No main effect for hemisphere was
found (F(1,8)=.038, p=.850) as well as no two-way interactions. The three way
interaction revealed a trend (F(1,8)=3.093, p=.117). Although it was only a trend, we
44
1
Rho value
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
7
8
9
10
LH-'Imaginary cartoon'
Session
RH-'Imaginary caroon'
Binocular-'Imaginary cartoon'
LH-'Human'
RH-'Human'
Binocular-'Human'
Fig 5.2 Learning effect: performances in the 10 monocular sessions, for both
hemispheres and for both classes. For reference, the average binocular transfer
performances are also plotted.
Fig 5.3 Learning effect: performances in the first versus the last monocular sessions.
Error bars represent SEM. * represents p < .05.
further analyzed the interaction using post-hoc Bonferroni correction. This is because
in brain-intact subjects, pigeons as well as humans, hemispheric asymmetry is harder
to detect than in brain damaged subjects (Yamazaki et al., 2007). With the 'Human'
class, the LH performances were significantly higher in the last session compared to
the first session (p=.003), this difference was not significant for the RH (p=.420). The
opposite occurred with the 'Imaginary cartoon' class. With this class, the RH and not
the LH performances were significantly higher in the last session compared to the first
one (RH: p=.028; LH: p=.334).
45
Finally, we were interested in evaluating how good the pigeons were in the first and
the tenth sessions. For this aim, we compared the performances in the first and last
monocular session versus the average transfer performances of each pigeon.
The 3 (condition: first LH session vs. first RH session vs. binocular transfer
performances) X2 (class) repeated measure analysis of variance showed the known
class main effect (F(1,8)=7.5, p=.026), a condition main effect (F(2,16)=21.786,
p=.000) and a trend towards interaction (F(2,16)=3.322, p=.062). Post-hoc tests for
the interaction revealed that with the 'Imaginary cartoon' both monocular conditions
were lower then the binocular one (LH: p=.001; RH: p=.002). With the 'Human' class,
such a difference was found only for the LH (LH: p=.008; RH: p=.090).
The 3 (condition: tenth LH session vs. tenth RH session vs. binocular transfer
performances) X2 (class) repeated measure analysis of variance showed the known
class main effect (F(1,8)=6.887, p=.030), a condition main effect (F(2,16)=4.820,
p=.023) and a marginally significant interaction (F(2,16)=3.388, p=.059). Post-hoc
tests for the interactions revealed that with the 'Imaginary cartoon' the performances
in the tenth monocular sessions were lower from the binocular ones. This was
significant for RH versus binocular viewings (p=.045), and a trend for LH versus
binocular (p=.055). With the 'Human' class, no significant difference between the
performances in the tenth monocular sessions and the binocular performances was
found (LH vs. binocular: p=.672; RH vs. binocular: p=.239).
46
5.4. Discussion
This study showed that initially the LH and RH performed equally when
discriminating known stimuli. The monocular accuracy increased with an increasing
number of sessions. In addition, with the ‘Imaginary cartoon’ class, the monocular
performances of both hemispheres were lower than with the ‘Human’ class.
Under monocular conditions, it became significantly easier for our subjects to
discriminate the ‘Human’ class, compared with the ‘Imaginary cartoon’ class. The
animals were first trained to discriminate the ’Imaginary cartoon’ class before they
were tested with the 'Human' class. Yet, we think it is unlikely that the better
monocular performances in discriminating the ’Human’ class were simply the result
of previous testing experience (i.e., testing order), either binocular9 or monocular. In
our opinion, this performance difference lies in the previous experience of the pigeons
with humans. In contrast to the ‘Imaginary cartoon’, which was novel, the pigeons
had daily contact with humans. This daily experience with actual humans might have
affected their performances with 'Human' stimuli, binocularly as well as monocularly.
Interestingly, this prior knowledge affected both hemispheres similarly.
It was once found that the pigeons' hemispheres differed in performances when the
pigeons attempted to monocularly discriminate known ‘Human’ exemplars, with the
LH having a category-based strategy, and the RH having a memory-based strategy
(Yamazaki et al., 2007). In the current study, we did not detect any disparity between
the performances of the two hemispheres in discrimination of known stimuli. The lack
of asymmetry in the current study was only at the performances level, and it did not
imply that the hemispheres employ similar computation methods. Upon close
examination, hemispheric differences were found. First, with the ‘Human’ class, with
which the pigeons achieved open-ended category binocularly, the LH improved more
during the monocular testing. With the class that was binocularly learned by rote, i.e.,
the ‘Imaginary cartoon’ class, the RH improved more during the monocular testing.
Thus, the improvement in monocular performances was in accordance with the
binocular abilities. Possibly, the LH learned and adapted better when encountering the
9
As was shown in the testing order control (control 2) in experiment 1, the pigeons failed an
‘Imaginary cartoon’ transfer test, which was done after the pigeons had acquired the ‘Human’ class.
47
‘Human’ class, and the RH learned better when computing the ‘Imaginary cartoon’.
Second, hemispheric difference was also revealed when we compared the
performances in the first and the last monocular session to the binocular performances
taken from experiment 1. In the first monocular session with the ‘Human’ class, the
LH but not the RH was significantly worse than under the binocular condition.
However, by the last monocular session, both hemispheres performed equally well as
under the binocular condition. This slower acquisition of the task by the LH is exactly
what Yamazaki and colleagues concluded. They observed that the RH performed
significantly better than the LH in the first tested session. However, when faced with
novel exemplars in the monocular transfer test, both hemispheres performed equally
well and did not exhibit any asymmetric function (Yamazaki et al., 2007). Together,
those results imply that the two hemispheres compute visual stimuli differently.
Nonetheless, the symmetry in hemispheric performances is an important finding that
might indicate the type of hemispheric interaction that occurred. Moreover, for both
classes, the initial monocular performances were lower than the binocular ones. This
cannot be attributed only to the presence of the eye cap since the pigeons were pretrained with eye-caps prior to the experiment. The pigeons had to discriminate
complex stimuli; therefore the task required advanced computation. We suggest that,
although the weighted contributions of each hemisphere to the binocular
performances might be different, parallel computation of both hemispheres is needed
to perform this task in the pigeon brain. More specifically, given that both local and
global features are known to be used in discrimination tasks (‘Human’ category: Aust
& Huber, 2003; Line drawings: Kirkpatrick-Steger et al., 1998), we propose that both
hemispheres are necessary for detecting complex objects in a rich background. A
connection between the extent of asymmetry and the task complexity was recently
suggested by showing decreased manual asymmetry when the complexity of a motor
task increased (Hausmann et al., 2004). Categorical-exemplar visual asymmetries can
be found in discrimination tasks of simple objects where local features might be
sufficient to identify them.
A few recent studies had similar conclusions. Binocularly tested chicks exhibited
higher selectivity towards food than they exhibited under monocular conditions (Prior
& Wilzeck, 2008). Moreover, lateralized chicks seemed to use information from both
hemispheres when tested monocularly (Chiandetti et al., 2005). In linguistic tasks,
48
human subjects performed better in bilateral presentation, i.e., presentation of a
stimulus to both visual hemi-fields. This phenomenon, known as the bilateral
redundancy gain, shows that parallel computation by both hemispheres is
advantageous (Mohr et al., 2007;Wlotko & Federmeier, 2007).
Next, we would like to discuss the observed increase in performance during the
monocular testing. Testing over a few sessions increased the monocular
performances; perhaps with repeated testing, each hemisphere learned to attend to
more features. The performances in the tenth monocular session were as high as the
binocular ones with the ‘Human’ class. Regarding the ‘Imaginary cartoon’ class, the
monocular performances were lower than the binocular ones. This difference was
significant for the LH and was a trend for the RH. It is likely that with more training
sessions the pigeons will be able to reach high monocular performances with both
hemispheres. As a final point, we investigated how this monocular training would
affect the binocular behavior. Will the pigeons use only one hemisphere to
discriminate after they have been tested monocularly? From the current data, we can
only speculate on this. Possibly both hemispheres will be still used binocularly.
Binocularly, the limited pool of processing resources is distributed between the
hemispheres (Holtzmann & Gazzaniga, 1982). In this case each hemisphere will have
fewer resources to use for the computation, which might cause a decrease in its
performances (Hellige et al., 1988). Thus, only by combining both hemispheres, will
the pigeons be able to compute the stimuli with high accuracy.
In summary, no asymmetry was found in the discrimination performances with either
class, but we have uncovered some evidences showing that each hemisphere might
contribute differently to the binocular performance. Moreover, previous experience
helped the pigeons to discriminate even monocularly. We further suggest that precise
discrimination of highly complex visual stimuli requires analysis using both
hemispheres.
49
6. Experiment 4:
Metacontrol computation as a strategy to solve binocularly
conflicting discrimination
6.1 Introduction
The previous study demonstrated a task to which both hemispheres are necessary.
Hemispheric interaction lies on a scale. On one edge, one hemisphere can dominate a
task; the process that determines which hemisphere will dominant a task is called
'metacontrol'. On the other extreme, the hemispheres' interaction creates a novel way
of processing that none of the hemispheres possess (emergence). The aim of this
fourth experiment was to see whether one end of the continuum, i.e., metacontrol, can
also be found in pigeons. For this purpose, we used a simple color discrimination task
that is known to have only mildly lateralized processing (Skiba et al., 2000;Prior &
Güntürkün, 2001). Studying metacontrol in the pigeon can help us better understand
the mechanisms that determine hemispheric dominance. Is metacontrol task
dependent, species dependent, or an active process?
6.2 Material and Methods
6.2.1 Subjects
14 pigeons were the subjects of this study. Five were naïve and the rest had
participated in former experiments (experiments 1 and 3). Since the current
experiment studied hemispheric relationship in a simple discrimination task,
participating in former studies should not have affected the present results. The five
naïve pigeons were autoshaped to peck on a lighted pecking key, as described before.
Afterwards, eye rings were put around their eyes, and they were monocularly trained
in a VI20 schedule, in order to make them familiar with wearing and working with an
eye cap.
The experiment was conducted according to the German law regularities for
prevention of cruelty to animals, and the keeping conditions were identical to the ones
described previously (3.2.1).
50
6.2.2 Apparatus
The Skinner box in this study was the one previously used (3.2.2, 5.2.2).
6.2.3 Stimuli
The stimuli used were 5(w) x 2.8(h) cm2 rectangles. Training stimuli were half
colored: the Red and Green stimuli were colored in their upper half, while the Cyan
and Magenta stimuli were colored in their lower half (Figure 6.1). For seven pigeons,
Red and Cyan were Gos and Green and Magenta were NoGos, and vice versa for the
other eight pigeons. Those stimuli were learned monocularly.
Test stimuli were a combination of a Go training color with a NoGo belonging to the
other color pair (i.e., either Red-Magenta or Green-Cyan, see Figure 6.1).
6.2.4 Procedure
The schedule used in the discrimination training as well as in the test phase was
identical to the one used in the previous experiments, (3.2.4, 5.2.4).
Monocular discrimination training
The pigeons were trained monocularly in a color discrimination task. Each
hemisphere was trained to discriminate a different color pair. The pigeons were
divided into four groups, which differ in the pair each hemisphere was trained with as
well as the reward value of each color:
(1) Four pigeons were trained in a Red/Green (Go/NoGo) color discrimination with
the LH and a Cyan/Magenta discrimination with the RH.
(2) Four pigeons were trained in a Green/Red discrimination with the LH and a
Magenta/Cyan discrimination with the RH.
(3) Three pigeons were trained in a Cyan/Magenta discrimination with the LH and a
Red/Green discrimination with the RH.
(4) Three pigeons were trained in a Magenta/Cyan discrimination with the LH and a
Green/Red discrimination with the RH.
Each of the two hemispheres was tested alternately.
The discrimination criterion was rho≥.9 in two out of three consecutive sessions, for
both hemispheres.
51
Test session
The test stimuli were either Go color learned by the LH combined with a NoGo color
trained by the RH (LH-Go vs. RH-NoGo), or a Go color trained by the RH combined
with a NoGo color trained by the LH (RH-Go vs. LH-NoGo).
The binocularly seeing test session contained the four monocularly-learned stimuli:
LH-Go (the Go color learned by the LH), LH-NoGo, RH-Go, RH-NoGo; and the two
test stimuli: LH-Go vs. RH-NoGo and RH-Go vs. LH- NoGo. Each of the six stimuli
appeared 8 times. The stimuli were presented in a random order that changed among
the pigeons. The test stimuli were not reinforced.
6.2.5 Analysis and Statistic
Performances were calculated using the rho value.
Laterality index indicated if binocularly there was a knowledge gap between the LHlearned and the RH-learned color information. It was measured using the rho values
obtained from the binocular discrimination of the monocularly–learned color pairs.
The laterality index was calculated by the following formula:
rho( LHGo) − rho( RHGo)
rho( LHGo ) + rho( RHGo )
Where laterality index=1 indicated total discrimination of the LH-learned color pair,
and a lack of discrimination of the RH-learned color pair.
Figure 6.1 Experimental stimuli:
A. Stimuli used for the Red-Green discrimination.
B. Stimuli used for the Cyan-Magenta discrimination.
C. Test stimuli.
52
Dominance index indicated the type of hemispheric interaction during the conflicting
situation. The dominance index was computed by the following formula, using the rho
values calculated from the performances with the test stimuli:
rho( LHGo Vs. RHNoGo) − rho( RHGo Vs. LHNoGo)
rho( LHGo Vs. RHNoGo) + rho( RHGo Vs. LHNoGo)
rho(LH-Go vs. RH-NoGo) is the rho value for the number of times the pigeon pecked
on [LH-Go vs. RH-NoGo] relative to the number of pecks on [RH-Go vs. LH-NoGo].
rho(RH-Go vs. LH-NoGo) is the rho value for the number of times the pigeon pecked
on [RH-Go vs. LH-NoGo] relative to the number of pecks on [LH-Go vs. RH-NoGo].
Since we did not assign values for ties in the Mann-Whitney U-test, the sum of those
two rho values does not necessarily equals one. For an example of the calculation, see
Appendix 2.
The dominance index values were divided into three groups (Figure 6.2):
(i) RH dominance (-1≤dominance index≤-.6).
(ii) No dominance (-.6< dominance index<.6).
(iii) LH dominance (.6≤dominance index≤1).
One sample t-test was used to calculate if the laterality index and the dominance index
differ from zero. Using paired t-test we compared the performances of the two
hemispheres. 2 (session: last monocular session vs. binocular session) X 2
(Hemisphere: RH vs. LH) repeated measures ANOVA analyzed the performances in
the binocular session. Pearson correlation was used to correlate the laterality index
with the dominance index.
Means values are reported in the format of mean ± SEM.
Figure 6.2 Dominance scale.
53
6.3 Results
6.3.1 Monocular discrimination training
The discrimination criterion was attained when performances reached rho≥.9 in two
out of three consecutive sessions, for both hemispheres. Since reaching criterion
depends on both hemispheres, we report here the number of sessions needed before
each hemisphere reached rho≥.9 for the first time. On average, the pigeons needed
7.643±1.261 sessions (ranged from 4 to 18) with the LH, and 5.786±1.331 sessions
(ranged from 1 to 22 sessions) with the RH. Nine pigeons achieved high performance
more quickly with their RH, four with the LH, and one pigeon needed equal number
of sessions with both hemispheres. Still, this difference in acquisition speed was not
significant (t(13)=-1.050, p=.313).
The average performances in the last training session were rho=.954±.007 and
rho=.957±.012 with the LH and the RH, respectively. The hemispheres did not differ
in their performances in the last training session (t(13)=-.178, p=.862).
6.3.2 Test session
During the binocular test session the pigeons were confronted with three color pairs:
the two monocularly-learned pairs and the test pair.
Subject
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Laterality index
-0.31959
-0.33333
-0.07563
0
0.078431
-0.00813
0.066667
0.036364
0.072165
0.009009
0.555556
0.024
0.015873
0.076923
Dominance index
-1
-0.92982
-0.79661
-0.2
-0.15152
-0.12903
-0.03704
0
0.220339
0.381818
0.6
0.741935
0.894737
1
4
Table 6.1 Laterality index and dominance index values. Dominance values which
show RH- or LH- dominance are marked in Bold.
54
The binocular performances with the monocularly-learned color discrimination were
rho=.848±.043 (range: from rho=.5 to rho=1) with the color pair learned by the LH,
and rho=.838±.053 (range: from rho=.25 to rho=1) with the color pair learned by the
RH. The laterality index did not differ significantly from zero (average=.014±.055,
t(13)=.258, p=.800, for the exact values see Table 6.1).
The pigeons performed the color discrimination better in the last monocular viewing
session compared with the binocular viewing session (F(1,13)=12.240, p=.004). The
performances were independent of which hemisphere learned the tasks (hemisphere
main effect: F(1,13)=.009, p=.926, interaction: F(1,13)=.029, p=.866).
Seeing the test stimuli: [LH-Go vs. RH- NoGo] and [RH-Go vs. LH- NoGo], the
pigeons were faced with a conflicting situation. For every test trial, the pigeons had to
decide according to which monoculalrly-learned color pair, i.e., hemisphere, they will
react. Hemispheric dominance was determined by the pigeons' relative performances
with the two test stimuli. Behaviorally, the pigeons confronted the conflicting
situation with a range of possible strategies (Table 6.2).
RH dominance
No dominance
LH dominance
3
7
4
Number of
pigeons
Table 6.2 The distribution of hemispheric dominance.
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Figure 6.2 Complete distribution of hemispheric dominance.
55
Three pigeons showed RH dominance, four displayed LH dominance, and in seven
pigeons both hemispheres contributed similarly for solving the conflicting task. A
more precise distribution of dominance index can be seen in Figure 6.2. Seven
pigeons had a negative dominance index, six pigeons had a positive dominance index
and one pigeon's dominance index was equal to zero. Across the group, the
hemispheric dominance did not differ significantly from zero (mean dominance
index=.042±.173, t(13)=.245, p=.810, for the exact values see Table 6.1).
When comparing the pigeons' binocular performances with the monocularly-learned
discrimination versus the conflicting discrimination, a significant correlation was
found between the laterality index to dominance index (r=.653, p=.011, see Figure
6.3). This correlation resulted from the binocular performances with the LH-learned
discrimination. A significant correlation between the binocular discrimination of the
LH-learned color pair and the test performances was found (r=.696, p=.006). No such
correlation was found with the RH-learned pair (r=.432, p=.123).
Figure 6.3 Comparison between the laterality index and the dominance index:
A. All data points. B. Binocular performances with the LH-learned color pair
correlated with the dominance index. C. Binocular performances with the RH-learned
color pair correlated with the dominance index.
56
6.4 Discussion
Metacontrol refers to the existence of a choice mechanism that determines which
hemisphere will control a task (Urgesi et al., 2005). The concept of metacontrol was
demonstrated in healthy humans (Hellige et al., 1988), in split brain patients (Levy &
Trevarthen, 1976), and in split brain monkeys (Kavcic et al., 2000). However, this
phenomenon has not been well documented and the exact mechanisms are unclear.
We studied metacontrol in pigeons which are highly visual animals (Güntürkün,
2000) and in which information from one eye is channeled almost completely to the
contralateral hemisphere (Güntürkün, 2003). With each hemisphere, the pigeons were
taught a different color discrimination task. As previous studies (e.g., Skiba et al.,
2000), we found no difference in acquisition time between the hemispheres. After
reaching the discrimination criterion, the pigeons were binocularly tested with those
two monocularly learned discriminations along with the two conflicting color stimuli.
The performances with the conflicting stimuli revealed a range of hemispheric
interactions: from pure dominance of the RH, through equal contribution of both
hemispheres, to pure dominance of the LH. This range of hemispheric dominance,
correlated with the binocular discrimination of the LH-learned color pair.
Hemispheric interaction exists along a continuum (Banich, 1995): from metacontrol
(dominance of one hemisphere), through parallel computation, to dual computation
that forms new understandings. Our results showed a distribution of the possible
strategies among the individuals. In half of the pigeons, both hemispheres contributed
similarly to the task. We are not able to conclude what was the exact computation
those pigeons used; they could have used parallel computation as well as a
metacontrol mechanism which alternates dominance between the hemispheres. In
contrast, in the other half of the pigeons, one hemisphere, either the RH or the LH,
dominated the decision, i.e., metacontrol strategy. (Kavcic et al., 2000) studied two
split brain monkeys in conflicting discrimination. They found that the monkeys, like
our pigeons, used a range of different strategies to solve the task: One monkey
showed LH dominance, and the other showed similar performances by the two
hemispheres. In another study, two split brain human patients were examined. One
exhibited metacontrol and the other made use of both hemispheres (Weekes et al.,
1997). Our results are important from an evolutionary point of view: the continuum of
57
hemispheric interaction is not limited to the mammalian lineage. Furthermore, the
finding of hemispheric interaction distribution show that having a lateralized brain, as
the pigeon has, does not lead to a specific type of hemispheric interaction.
Among the pigeons that exhibited metacontrol strategy, half exhibited RH-dominance
and the other displayed LH-dominance. In the study by Hellige et al. (1988), although
most of the subjects employed LH-dominance, few showed RH-dominance. What
determines which hemisphere will take control? Surprisingly, in humans, the
dominant hemisphere is not always the specialized one (Levy et al., 1972;Levy &
Trevarthen, 1976). Which hemisphere will control the task is affected by dynamic
factors. Task properties such as hemispheric stimulation timing (Urgesi et al., 2005)
or the task instructions (Levy & Trevarthen, 1976); as well as the chosen inputprocessing strategy (Lazarus-Manika & Hormann, 1978) can affect which hemisphere
will be the dominant one. In the current study, performances in the conflicting
situation were significantly correlated with the binocular discrimination of the
monocularly-learned color pairs (laterality index), or more specifically with the
binocular discrimination of the LH-learned color pair. The better the LH-learned color
pair was discriminated binocularly, the more the LH dominated the decision in the
conflicting stimuli. What can this correlation mean? In order to answer this question
we need to look in greater detail at the pigeons’ binocular performances with the
monocularly-learned color discrimination.
Interestingly, the pigeons discriminated the monocularly-learned colors better under
monocular compared with binocular viewing. In humans, such a difference was
explained by resource competition. The brain has a fixed amount of resources
(Holtzmann & Gazzaniga, 1982). Since under binocular viewing condition both
hemispheres compute, at least partly, the stimuli, the resources are distributed
between them (Hellige et al., 1988). This bi-hemispheric competition for limited
resources would then lead to less accurate performances. One would logically assume
that the binocular discrimination of the LH-learned color pair is computed mainly by
the LH (see Appendix 3 for discussion regarding the laterality index). If so, this would
imply that the LH is more sensitive to the reduction of resources. When the LH was
not affected by the decrease in the available resources, it both discriminated well the
LH-learned color and dominated the conflicting discrimination task. Thus, in the
58
current experiment the inherent abilities of the LH influenced hemispheric
dominance. When the LH performed poorly in the monocularly-learned color
discrimination task, the RH controlled the performances in the conflicting task.
Unlike the LH, the RH dominance degree was not affected by the divided resources.
Sharing resources between the hemispheres does not always lead to a decrease in
binocular performance. As was suggested in experiment 3, hemispheric cooperation,
especially when each hemisphere computes different aspects of the stimuli, will
improve the binocular performances. However, in the case of the current study, each
hemisphere is competent for color discrimination of different color pairs, and they
might interfere with each other.
The topic of the current study was cerebral hemispheric interaction. For this purpose,
pigeons were confronted with a discrimination task that had a different solution
according to each hemisphere. The pigeons solved the task either by complete
dominance of one hemisphere, i.e., metacontrol, or by similar contribution of both
hemispheres. Metacontrol is not fixed, not according to the species, and not according
to the task, but might be determined according to the intrinsic abilities of the LH.
When the LH is not influenced by resource limitation in the binocular condition, it
will dominate the binocular task.
59
7. General Discussion
This thesis evaluated cognitive mechanisms in the pigeon brain. We specifically
addressed
questions
regarding
categorization
mechanisms
and
hemispheric
interaction.
7.1 Summary of the experimental findings
7.1.1 Experiment 1: Category formation: borders and mechanisms
In the first experiment, pigeons were trained with three different classes: the novel
and artificial 'Imaginary cartoon' class, the known and natural 'Human' class, and the
artificial and presumably known 'Human cartoon'. The pigeons could correctly
discriminate only known ‘Imaginary cartoon’ stimuli that they had previously
encountered i.e., the pigeons classified the 'Imaginary cartoon' by rote, or used a
memory-based strategy. Eighty percent of the pigeons achieved open-ended
categorization with the 'Human cartoon' class. All pigeons exhibited categorization in
an open-ended manner with the known and natural 'Human' class. A comparison of
the discrimination accuracy of the three classes revealed that the performances with
the novel 'Imaginary cartoon' stimuli were significantly lower than those with the
novel stimuli from the two other classes. In a control study, naïve pigeons were taught
the ‘Human cartoon’ class and then were able to discriminate stimuli from the
‘Human’ class. This picture-photograph transfer indicates that the pigeons see a
correspondence between 2D objects and their drawings. Taken together, these results
indicate that the novelty value affects category formation in pigeons. Previous
knowledge from the natural world facilitated the pigeons’ ability to construct a
category.
7.1.2. Experiment 2: 3-dimensional spontaneous discrimination
As a follow-up to the conclusion of experiment 1, this experiment investigated
whether pigeons could relate to humans in their every-day environment. In order to be
influenced by the 3-dimensional environment, the pigeons must at least partly attend
to the environment and its features. In the second study, the activity of two groups of
pigeons, each fed by a different experimenter, was recorded. A significant difference
between the two groups was found with regard to the particular experimenter who
60
was present in the room. More specifically, the pigeons increased their activity
towards the experimenter who fed them as he or she entered the room, thus
demonstrating their ability to spontaneously discriminate between everyday-life, 3dimensional objects.
7.1.3 Experiment 3: Hemispheric interaction in complex discrimination tasks
Next, we tested the individual hemispheric ability in discrimination tasks. The
pigeons were tested ten times with each hemisphere with the two classes: 'Imaginary
cartoon' and 'Human'. The monocular performances with the 'Human' class were
better than those with the 'Imaginary cartoon', showing that previous experience
altered also the individual hemispheric performances. No difference between the
performances of the two hemispheres was detected in discriminating either class, and
the initial monocular performances were inferior to the binocular ones. These results
suggest that in order to discriminate complex stimuli, the pigeons had to employ both
hemispheres. With both classes, both hemispheres increased their discrimination
accuracy during the course of monocular training. An interesting trend was found
when we closely analyzed the performances in the tenth session versus the
performances in the first session: with the ‘Human’ class the performance of the LH
was considerably increased, whereas with the ‘Imaginary cartoon’ the RH mainly
increased its performance. During the monocular testing sessions, the improvement in
discrimination accuracy was according to the binocular strategy. Collectively, these
results show that both hemispheres are needed for the discrimination task and that
each hemisphere computes different aspects of the stimuli.
7.1.4 Experiment 4: Metacontrol computation as a strategy to solve binocularly
conflicting discrimination
The final study delved further into the effect of knowledge and brain lateralization.
Specifically, we wondered whether knowledge affects hemispheric dominance in an
asymmetrical manner. For every pigeon, each hemisphere was trained with different
color discrimination and therefore had a different experience. Subsequently, the
pigeons were binocularly examined with each of the two monocularly learned color
discriminations and additional test stimuli that combined a Go color according to one
hemisphere and a NoGo color according to the other, thereby omitting the availability
61
of a coherent solution. In this way the pigeons were confronted with a conflicting
situation. No hemispheric difference was found in the monocular acquisition of the
task. The binocular performances with the two monocularly learned color
discriminations were similar and significantly worse than the monocular ones. With
the conflicting situation test stimuli, half of the pigeons responded to both stimuli,
indicating that they similarly used both hemispheres to solve this task. The other
pigeons based their discrimination on one hemisphere only, demonstrating for the first
time metacontrol computation in birds. Within this group, half employed LHmetacontrol, and the other half used RH-metacontrol meaning that no hemisphere
solely dominated the task. In addition, the results offered an insight as to how the
dominant hemisphere is determined. The binocular performances with the
monocularly learned discrimination positively correlated with the conflicting stimuli
performances, especially for the LH. The LH dominated the binocularly viewed task
only when the LH-learned color pair was well-discriminated under binocular viewing.
We explained this result by referring to the limited pool of processing resources. Only
when the LH was not affected by shared resources between the hemispheres, did it
dominate the performances in the conflicting task.
62
7.2 Categorization mechanisms and the effect of knowledge
High importance is attached to the study of categorization in animals. By studying
animals, we can reveal the most basic mechanisms, as well as to contribute to debates
within the Human literature. One aspect of the debate is the importance of language
for category formation.
Language- having a vocabulary
Some researchers have suggested that having the name of objects is necessary to solve
cognitive tasks that require differentiating between different objects (Xu & Carey,
1996). Indeed, labeling an object with a word facilitates the categorization process:
verbal labels, as opposed to non-verbal cues, helped adult humans to learn categories,
even though this information was redundant (Lupyan et al., 2007). Verbal labels also
assisted categorization in 18-month-old infants (Booth & Waxman, 2002). However,
word cues helped 14-month-old infants only when the function of the named category
was initially demonstrated to them. Thus, one can conclude that language aids
category formation, however, its effect is later manifested in development. Moreover,
language is not the only factor that aids category formation in humans. A study
supporting this notion showed that 4-month-old infants, who are too young to have
much receptive vocabulary, were shown to segregate objects (Needham, 1999),
implying some sort of understanding about the nature of the objects.
Animals do not possess an open-ended communication system as humans do (Hauser
et al., 2002). Yet animals were shown to form open–ended categories (e.g., Herrnstein
and Loveland, 1964), suggesting that having vocabulary is not a prerequisite for
category formation. In this case, comparative studies aid in understanding cognitive
mechanisms of humans.
If not language, then what are the main mechanisms that facilitate category
formation? Two other mechanisms were suggested to facilitate category formation in
humans10, do they also aid category formation in non-human organisms?
10
Researchers studying humans do not differentiate in their studies between concepts and categories.
Yet most of the experiments studied categories (Solomon et al., 1999): “…we will refer to a concept
as a mental representation that is used to meet a variety of cognitive functions. The most commonly
studied function has been categorization, a process by which mental representations (or concepts) are
used to classify entities”.
63
Similarity
The similarity between exemplars might affect categorical learning in the sense that
perceptually similar exemplars will be grouped together. Similarity was shown to aid
categorization tasks in human adults (Cooke et al., 2007) and early in development
(Sloutsky, 2003), and it is thought by some to be the first strategy that facilitates
category formation (Quinn et al., 1993;Eimas & Quinn, 1994) 11. For example, infants
3 to 4 months old were shown to differentiate between cats and horses (Eimas &
Quinn, 1994).
It is likely that pigeons also use similarity when performing categorization tasks.
Pigeons learn categories faster than pseudo-categories (Wasserman et al., 1988). In
pseudo-categories the exemplars are divided into arbitrary groups and the animals
cannot use similarity in order to discriminate between the groups. Nevertheless,
similarity cannot be the main factor that gives rise to a category in pigeons. In our
study the pigeons were unable to categorize in an open-ended manner the ‘Imaginary
cartoon’ class, although all the Go stimuli were similar to each other because they all
contained the same character.
Knowledge
Many studies have demonstrated that past experience and even brief exposures can
assist category formation in humans. Shape recognition by young infants at 4.5
months of age was influenced by a few seconds of exposure (familiarization trials) to
complex shapes (Quinn & Schyns, 2003). Human infants, 8.5 months old, who had
every-day life experience with a key-chain, referred to a novel key and a novel keyring as belonging together (Needham et al., 2006). The amount of exposure is critical:
7-month-old infants did not identify the key and the ring as belonging together. The
cognitive abilities of 7-month-old infants are not vastly inferior to those of 8.5-monthold infants, but the 7-month-old infants had less daily-life experience with key-chains
(Needham et al., 2006). Beyond children, previous knowledge also facilitates category
learning in human adults (Murphy & Allopenna, 1994;Kaplan & Murphy, 2000). For
11
The role of similarity in category formation was debated a few years ago (for a review, see
Goldstone, 1994). Some researchers claim that similarity is too general and that explaining
categorization by means of similarity is begging the question. In contrast, other researchers argue that
similarity is a constrained property, for example, by the perceptual system or by stimuli factors, since
common features are regarded as more important than distinctive features. Those researchers think that
similarity can explain category formation.
64
example, in a study by Murphy and Allopenna (1994), human adults learned two
categories containing either arbitrarily grouped features or coherently grouped
features (i.e., there is a theme that connects the features; an example of such a theme
is “underwater building”). The categories with the meaningful feature combination
were learned faster. In addition, with a theme, subjects formed the category using a
few feature dimensions, whereas without a theme they relied on only one feature type,
thus, changing the structure of the learned category (Kaplan & Murphy, 2000).
Knowledge plays an important role in categorization processes, not only in humans.
Infant rhesus monkeys have a broader 'food' category than adults. With the
accumulation of experience about objects in their environment, their ‘food’ category
becomes narrower (Santos et al., 2001).
Experiments 1 and 2 in the current thesis demonstrated that pigeons can form an
open-ended category of a complex and even artificial class if they have previous
experience with this class. Pigeons interact with humans and even spontaneously
attended to them and their defining features (experiment 2). Pigeons that had previous
experience with humans were able to form an open-ended category of 'Human'. The
pigeons also acquired open-ended category of 'Human cartoon', a class that they
referred to as similar to the 'Human' one12 (experiment 1). We concluded that pigeons
can form 2-dimensional complex pictorial categories of a class if they had some
previous experience with it.
In humans, previous knowledge was found to influence category formation in four
ways (Heit, 1997): (1) prior knowledge influences the initial representation of a
category (before intensive training), (2) it helps select which features to attend to
when learning a new category, (3) it helps interpret the features, and (4) it causes
overall facilitation of categorical learning when the general information about
categories, as the category structure, matches the one of the newly learned category.
We think that for the 'Human' class, the previous experience with 3D humans helped
the pigeons select which features to attend to. The features to which the pigeons
attended while being in their home environment might be those features that also
12
Can it be that the pigeons saw no difference between those two classes? We did not check directly
whether the pigeons can discriminate between humans and human cartoons, yet the fact that the
pigeons had to train with the ‘Human cartoon’ class before reaching the acquisition criterion indicates
that those two classes were perceptually different to the pigeons.
65
appeared in the 2D 'Human' class: static, visual features with relative (not absolute)
size.
The ways in which knowledge affects category formation in the pigeon, as well as the
amount of knowledge that is needed in order to facilitate category formation are not
yet clear. In the current thesis the animals were not only exposed to humans, but also
interacted with them: humans touched the pigeons, held them and most importantly,
fed them; thus human were functionally important to them. This high importance of
humans to pigeons may explain the fast acquisition time of the classes containing 2D
humans: 14 and 12 sessions were needed until the discrimination criterion was
reached with the 'Human' and 'Human cartoon', respectively. Familiarity per se might
also influence category formation, but this is still an open question.
The third experiment showed that the effect of past experience extended to the
monocular viewing condition. The monocular performances by both hemispheres
were better with the 'Human' class compared with the 'Imaginary cartoon' class. The
'Imaginary cartoon' was not novel at the time of the monocular testing, since the
pigeons were trained to discriminate this class in the Skinner box. Nonetheless, the
pigeons' previous experience with the 'Human' class was much more extensive. The
pigeons interacted with 3D humans before being tested in our experiment. Possibly,
experience with the discriminated class provided knowledge about both local (LHbased computation) and global (RH-based computation) stimuli. Due to this
experience, each hemisphere alone could detect more human-related features,
increasing its discrimination ability. Thus, experience could simultaneously increase
the individual ability of the two hemispheres.
In short, the first two studies indicate that categorization mechanisms are conserved
throughout evolution. Prior experience was found to facilitate category formation in
humans, adults, and infants, as well as in monkeys and pigeons: linguistic (human)
and non-linguistic (pigeon) organisms. This lends support to the argument that
language is not the key factor that facilitates category formation (Infants: Needham et
al., 2006; Rhesus monkeys: Phillips and Santos, 2007).
66
7.3 Categorization border
Pigeons have been shown to categorize in an open-ended manner various stimuli,
natural as well as artificial; some examples are 'Tree', (Herrnstein, 1979), and 'Car'
(Bhatt et al., 1988). Can the pigeons acquire every class in an open-ended manner?
Which factors restrict their ability? In the first experiment an answer was suggested
for this question. The pigeons in this experiment could form an open-ended category
of a complex, natural and known class and of a complex, artificial, and known class,
both contained a rich background. In sharp contrast, none of the pigeons established
an open-ended category with the 'Imaginary cartoon' class. Does the 'Imaginary
cartoon', i.e., a class that is complex, artificial, novel and contains a rich background
represent the categorization border of the pigeons? In order to answer this question we
will analyze each of the components that compose the 'Imaginary cartoon' class:
complexity and rich background, artificiality, and novelty.
Complexity and rich background: Complexity means that each stimulus is composed
of many features. Although it may seem that complex stimuli are harder to
discriminate, the opposite might be true. Simple shapes and orientations are hard for
the pigeons to discriminate (Ditttrich, L., personal communication; Kirsch et al., in
press). Possibly, complex stimuli are easier for the pigeons to discriminate since they
can attend to many features.
One may intuitively suspect that the background of a stimulus may complicate the
task since the pigeons need to additionally perform figure-ground separation.
However, it was shown that pigeons do not confuse a background with an object
('Human' class: Aust and Huber, 2001). Moreover, since previous knowledge helps
the categorization process, it might be that the background aids the task because it
puts the objects into a familiar context.
To summarize, pigeons can form open–ended categories with classes containing
complex objects and rich backgrounds.
Artificiality13: Pigeons are also able to categorize in an open-ended way artificial
stimuli (Lazareva et al., 2004; 2006). Impressively, the pigeons could discriminate
13
We did not refer to artificial polymorphous stimuli in our discussion on artificial classes as well as
on novel classes, since their feature organization is not biological but synthetic. Those stimuli are even
hard for human subjects. See Lea et al., 2006 for a more elaborate discussion.
67
artificial stimuli at their basic level (car, chair) as well as at their superordinate level
(artificial stimuli). Lazareva et al. used stimuli with a uni-colored background. The
current study showed that pigeons can categorize a complex, artificial class with a
rich background, i.e., the 'Human cartoon' class.
Novelty: To our knowledge, pigeons were not tested before with a totally novel
class15,14. Our results show that a novel class is difficult for the pigeon to categorize in
an open-ended manner, and that (3-dimensional) past experience facilitates category
formation. Knowledge is an important factor, yet we cannot exclude the existence of
other mechanisms. Those mechanisms might facilitate categorization of novel objects.
However, the novel class tested in the current study was also complex, artificial and
contained a rich background. We think that the combination of all four elements is
especially hard for pigeons; and that this combination constitutes the categorization
border of the pigeons. Human studies suggest that previous knowledge helps in
feature selection (Heit, 1997). When an unknown complex object with a rich
background is shown to the pigeons, it is possible that the pigeons are unable to
conclude which features compose the figure and which compose the background. The
pigeons simply do not know to which features they should attend. When the stimulus
is in addition artificial, it is even more difficult to select the relevant features. Natural
stimuli might possess a general organization (e.g., the golden ratio), which might
facilitate their learning, even when they are novel, complex, and contain a rich
background. The studies by Lazareva and colleagues support this idea. They tested the
pigeons not only with artificial classes, but also with two natural classes: flower and
humans. Categorizing natural stimuli at the superordinate level ('natural') was easier
for the birds than categorizing the artificial stimuli at the superordinate level. Like us,
they explained those results by suggesting that the natural stimuli are less diverse
(Lazareva et al., 2006).
14
See a comment on the study by Herrnstein & DeVilliers (1980) in the introduction.
68
7.4 Hemispheric performance in complex discrimination tasks
Next, we investigated the relation between the cerebral hemispheres during complex
discrimination tasks. The third experiment compared individual hemispheric ability in
discriminating two types of stimuli: the novel and artificial 'Imaginary cartoon' class
that binocularly was learned by rote versus the known and natural 'Human' class that
was categorized in an open-ended manner. As summarized in the introduction, a
dichotomy in feature computation was found both in humans and in birds: the LH is
superior in detecting the local features of a stimulus and RH is sensitive for global
features (e.g., Evert and Kmen; 2003). Previous studies have also suggested
hemispheric specialization in discrimination tasks: the LH is inclined towards the
processing of categorical knowledge, and the RH is responsible for memory-based
computation (e.g., Kosslyn et al., 1999). How do the two hemispheres interact during
computation of a binocularly viewed task? This is an important, relatively
understudied question. Hints that both hemispheres are being used during binocular
tasks were shown in both human and bird studies. In light incubated chicks, both
hemispheres contribute to a food-location-task, even when the task is done
monocularly seeing (Chiandetti et al., 2005). Similarly, the two hemispheres seem to
compute spatial tasks in humans (Stephan et al., 2007). In addition, involvement of
both hemispheres was shown in linguistic tasks (Wlotko & Federmeier, 2007;Mohr et
al., 2007). This parallel computation might be especially important in cases when the
hemispheres compute different aspects of the stimuli. Regarding object recognition,
detection of both local and global features might be required to discriminate complex
objects in a complex visual scene. Experiment 3 suggested exactly that. Our results
showed no difference between the two hemispheres' capabilities in discriminating
either class. Moreover, the initial monocular performances were lower than the
binocular ones, especially with the 'Imaginary cartoon' class. Indicating that in order
to discriminate highly complex stimuli both hemispheres are needed. It is important to
mention that although the hemispheres performed with similar accuracy, this does not
imply that the hemispheres computed the same aspects of the stimuli. We found hints
showing that each hemisphere might compute differently the binocular task.
Comparing the development of hemispheric performances over time showed that the
improvement in discrimination accuracy was according to the binocular strategy of
the tested class. When the binocular strategy was categorical the LH improved, and
69
when binocularly the pigeons employed a memory strategy, the RH improved.
Interestingly, a recent study found similar results (Prior & Wilzeck, 2008). Prior and
Wilzeck showed that both hemispheres had to be engaged in order to generate food
preference in chicks. Under binocular viewing the chicks displayed a preference to
one type of food. Monocularly, however, such a preference was available only in the
first days of age. This initial monocular advantage was mostly with the LH, showing a
difference between the hemispheres, and suggesting that the LH might contribute
more to the binocular food preference.
Yamazaki and colleagues found a difference between the hemispheres in
discrimination tasks, but this difference was mainly marginally significant (Yamazaki
et al., 2007). It seems that the asymmetry in category-versus exemplar computation is
not as strong in pigeons as it is humans. Perhaps, the higher hemispheric
specialization in humans, compared with birds, is related to the higher amount of
fibers connecting between the hemispheres, since high amounts of inter-hemispheric
connections were suggested to result in larger functional hemispheric differences in
humans (Hellige et al., 1998). It might be that with invasive measurements, like
blockage of high-order visual areas (e.g., blockage of the left- or right- entopallium
using Tetrodotoxin) one can better reveal hemispheric asymmetry in complex tasks in
pigeons. Blockage of visual areas is not identical to the blockage of the visual input,
i.e., monocular occlusion. When a hemisphere does not receive a direct input due to
occlusion of the contralateral eye, it can still be engaged in the computation through
indirect inter-hemispheric information transfer. On the other hand, even when visual
input is available, a blocked area will not be involved in the computation.
Comparably, in split brain human patients laterality is more pronounced than in
normal humans subjects (Yamazaki et al., 2007).
In summary, these results indicate no category-exemplar hemispheric asymmetry in
absolute performances; however, the specific computation seems to differ between the
hemispheres. A more pronounced asymmetry may be found using invasive measures.
70
7.5 Hemispheric interaction continuum and complexity of stimuli
Although the cerebral hemispheres show various forms of asymmetrical computation,
the two hemispheres need to interact in order to form a holistic perception of the outer
world. Hemispheric interaction can be described along a continuum: this continuum
starts at metacontrol computation, through parallel computation to information
integration that produces novel way of information processing (Banich, 1995). It
seems that the hemispheric interaction continuum is not unique to humans, an
organism that has corpus callosum. In the current thesis, the same pigeons15 exhibited
different forms of hemispheric interaction under different conditions: experiment 3
suggested that the pigeons used parallel computation of both hemispheres to
discriminate the complex stimuli; in experiment 4 some of those pigeons exhibited
metacontrol.
What determines which type of hemispheric interaction will occur? The answer to this
question is unknown and was proposed as one of the challenges of the 21st century
(Banich & Weissman, 2000). What is known is that hemispheric interactions are
dynamic. A set of experiments by Banich and others (e.g., Belger and Banich, 1992;
Wiessman and Banich 2000; Hochman & Eviatar, 2006) suggested that the task's
complexity affects hemispheric interaction. In a simple task in which subjects had to
judge the physical identity of letters, a within-hemisphere computation was
preferable. In a more complex task, when subjects had to judge letters according to
their name, across-hemispheric computation improved the performances. In our
studies, the task was always a Go-NoGo discrimination. However, the complexity of
the stimuli varied between experiments 3 and 4. In experiment 4 the pigeons were
tested in a color discrimination task. On the other hand, in experiment 3 pigeons had
to discriminate complex stimuli. Our data suggest that discrimination of simple
stimuli, e.g., color can be done solely by one hemisphere, whereas discrimination of
complex stimuli (‘Imaginary cartoon’) requires parallel computation of both
hemispheres16:
For simple discrimination, one hemisphere is necessary and sufficient:
15
The same group of pigeons participated in experiments 1, 3, and 4; experiment 4 included five
additional pigeons.
16
In experiment 3, the pigeons were trained binocularly and were tested monocularly, whereas the
opposite was true in experiment 4. Nevertheless, combining those experiments can teach us about
hemispheric interaction in simple and complex discrimination.
71
The binocular performances in experiment 4 support this claim. The binocular
performances in the monocularly-learned color discrimination were significantly
worse than the monocular performances with the same color stimuli. In this case,
having more computational surface impaired the performances, since in the binocular
viewing condition, the computation resources were divided between both
hemispheres, whereas under the monocular condition the resources were used by a
single hemisphere.
In addition, former studies indicated that information transfer between the
hemispheres occurs under monocular viewing conditions in color discrimination tasks
(Diekamp et al., 1999;Skiba et al., 2000). If such is the case, then one hemisphere can
potentially contain all the required knowledge and can perform alone the
discrimination.
One aspect of inter-hemispheric computation is metacontrol. In metacontrol, the task
is performed by one hemisphere, which dominates the binocular performances. In
experiment 4 each hemisphere learned a different color pair. Binocularly, the pigeons
had to decide how to behave towards stimuli containing intermingled colors with
different contingencies. The pigeons were not forced to use only one hemisphere,
especially since the test stimuli did not provide any reward. Nevertheless, half of the
pigeons computed the test stimuli via metacontrol: in half it was LH-metacontrol and
in the other half it was RH-metacontrol. For those pigeons one hemisphere was
sufficient to perform the conflicting situation task. Experiment 4 also suggested how
the dominant hemisphere might be determined. When the LH was not affected by the
decrease in available resources, LH-metacontrol computation occurred. (Holtzmann &
Gazzaniga, 1982)
For complex discrimination, parallel computation is necessary and sufficient:
In experiment 3, the pigeons were tested monocularly with the 'Imaginary cartoon'
and the 'Human' classes. We suggested that both hemispheres are needed and are used
in parallel during this binocular complex discrimination task. If one hemisphere
would have been responsible for the discrimination, we would have observed initial
monocular performances with one of the hemispheres that is almost as high as the
binocular performances.
Parallel computation is necessary since possibly both local and global features are
needed for the task (Aust & Huber, 2003). The LH is special in detecting local
72
features, and the RH is better at recognizing global features. Combining the capacities
of both hemispheres permits attendance to more features. This in turn may improve
the discrimination performances under binocular conditions. (Hirnstein et al., 2008).
In short, we hypothesize that with simple color stimuli, one hemisphere is sufficient,
whereas with complex memorized stimuli such as the 'Imaginary cartoon' stimuli,
both hemispheres are necessary to perform the task.
73
7.6 General outlook and future directions
Collectively, all four studies described in this thesis offer an insight into behavioral
and cerebral mechanisms in discrimination tasks. We have shown that experience can
alter the behavior of pigeons in various ways, under binocular as well as under
monocular conditions. We hypothesize that in order to discriminate complex stimuli,
both hemispheres are needed since hemispheric ability is not strong enough to allow
one hemisphere to control the task.
A few questions arose from the current study:
1. Exposure versus active experience: can mere exposure facilitate category
formation in the pigeon?
2. Nature versus nurture? Is the extensive and rapid ability of the pigeons to
categorize humans is born due to the long historical relationship between the
pigeon and humans?
3. Will we find hemispheric asymmetry in discrimination tasks when high visual
areas are blocked?
4. How do different amounts of binocular prior knowledge (from exposure
through a few discrimination trials to categorical knowledge) affect the
performances of the cerebral hemispheres? Does a change in hemispheric
asymmetry take place along with a change in the experience level (see Smith
et. al., 2005, for such a trend in human subjects)?
5. How does the contribution of each hemisphere to the binocular computation as
well as hemispheric interaction change due to monocular training (see 5.4)?
6. What other mechanisms determine the extent of hemispheric interaction?
When is metacontrol determined? How do the two hemispheres interact to
form metacontrol? Is it indeed an inhibitory mechanism?
As a final comment, we would like to note the importance of these results from an
evolutionary perspective. We have shown that there are broad similarities between
pigeons and humans. The categorical mechanisms seemed to be conserved across
species (Cook & Smith, 2006). For example, prior knowledge facilitates
categorization in humans (Heit, 1997) and in pigeons (the current study).
74
As for the continuum of hemispheric interaction, we demonstrated that is not
restricted solely to the linguistic organism-human; the pigeon, a highly lateralized
organism lacking the corpus callosum has also various types of hemispheric
interactions.
“One by one, the characteristics that we have held to be uniquely human have been
found in other species. Perhaps the only uniquely human characteristic is this: So far
we know, we are the only creature that spends time trying to prove its superiority over
other creatures. The rest of the animal kingdom treats the matter with indifference.”
(Chance P. (2003) Learning and behavior)
75
Appendix 1
The questionnaire distributed in experiment 2
Age: __________ Gender:___________
Pigeons' Activity Experiment
Instructions:
You will see 39 short videos, 10 seconds each.
Each video shows 12 pigeons in their home cages. The pigeons belong to two groups:
group A and group B. Each group contains 6 pigeons. Pigeons from group A appear
in the left side of the video, pigeons from group B appears in the right side of the
video.
A scheme describing the division of pigeons to groups:
A
A
B
B
A
A
B
B
A
A
B
B
You need to rate, in the table below, the activity of each group using a scale from 1-6
whereas 1 means no activity at all and 6 means highest activity. After each video there
is a 3 seconds break in which you can fill in the table.
In order to have a reference point, you will be shown an example video which shows
activity=1 of both pigeons' groups.
Thank you!
Video no.
Group A Activity
Group B Activity
1
1
2
3
4
5
6
1
2
3
4
5
6
2
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
.
.
.
39
76
Appendix 2
Example for dominance index calculation in one session, for a single pigeon
The stimuli [RH-Go vs. LH-NoGo] and [LH-Go vs. RH-NoGo] appeared eight times
during one session. Table A.1 shows the number of pecks one pigeon pecked on each
stimulus during its eight presentations. For convenience, [LH-Go vs. RH-NoGo] will
be named A, and [RH-Go vs. LH-NoGo] will be named B.
A
0
7
9
13
14
16
17
19
B
0
0
0
6
7
17
18
20
Table A.1
Stages in dominance index calculation:
1. Rank order the pecks, in ascending order (Table A.2).
Stimulus
A
B
B
B
B
A
B
A
A
A
A
A
B
B
A
B
Pecks
0
0
0
0
6
7
7
9
13
14
16
17
17
18
19
20
Table A.2
2.a For each A, count how many B's have lower score, i.e., number of pecks
(equivalent to counting how many B's appear before every A while excluding ties).
2.b For each B, count how many A's have lower score, i.e., number of pecks,
(equivalent to counting how many A's appear before every B while excluding ties).
See table A.3.
Stimulus
A
B
B
B
B
A
B
A
A
A
A
A
B
B
A
B
Preceding scores
0
0
0
0
1
4
1
5
5
5
5
5
6
7
7
8
Table A.3
3.a Calculate u-values by summing how many B's in total were in front of all the A's.
0+4+5+5+5+5+5+7 = 36
U-value for [LH-Go vs. RH-NoGo] vs. [RH-Go vs. LH-NoGo] is 36.
77
3.b Sum how many A's in total were in front of all the B's.
0+0+0+1+1+6+7+8 = 23
U-value of [RH-Go vs. LH-NoGo] vs. [LH-Go vs. RH-NoGo] is 23.
4. Calculate rho values by dividing the u-values by the product of number of A's and
B's.
Rho value of [LH-Go vs. RH-NoGo] vs. [RH-Go vs. LH-NoGo] is 36/64.
Rho value of [RH-Go vs. LH-NoGo] vs. [LH-Go vs. RH-NoGo] is 23/64.
5. Place those values in the dominance index formula.
36 23
−
64 64 = .220
36 23
+
64 64
78
Appendix 3
Laterality index mechanisms: Hemispheric interaction in the binocular
discrimination of the monocularly-learned color pairs
It is likely that in our experiment, both hemispheres had some knowledge about the
two monocularly-learned color pairs. Clearly, each hemisphere knew the color pair it
was trained with. Familiarity with the other pair might have been made available due
to inter-hemispheric information transfer (Green et al., 1978), and may have occurred
over the mere course of a few hours (Skiba et al., 2000). Thus, one possibility is that
both hemispheres participated in the binocular discrimination of the two monocularlylearned color discriminations.
Another theoretical possibility is that one hemisphere dominated completely the
binocular discrimination (metacontrol), since it had the knowledge about the two
color discriminations: one learned directly and one learned via transfer. In this case, it
is logical that the same hemisphere that dominated the binocular performance with the
monocularly-learned discrimination will also dominate the test performance.
Although we found such correlation between the laterality index and the dominance
index, this correlation was restricted to the performances with the LH-learned
discrimination. RH-dominance with the test stimuli did not indicate any RHdominance in the monocularly-learned discrimination.
Hemispheric transfer mechanisms are unknown. It is unknown if the information
transfer to the naïve hemisphere occurs passively during the training period of the
other hemisphere, or whether the information transfers only when the naive
hemisphere requires this information, i.e., when being tested in the task. We think that
although transfer occurs, it is likely that the information which the hemisphere learned
directly, as opposed to the information that might have transferred to it, is the one
which dominates the hemisphere's behavior. In other words, the hemisphere which
originally learned a particular discrimination, will dominate this task binocularly.
Regardless of the computation mechanism, the laterality index indicates if binocularly
there was a knowledge gap between the LH-learned and the RH-learned color
information.
79
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Declaration
This dissertation has been completed and written independently without
external assistance. Furthermore, the thesis has never been submitted in
this or a similar form to any other institution of higher education. The
“Guidelines for Good Scientific Practice” (Leitlinien guter wissenschaftlicher
Praxis und Grundsätze für das Verfahren bei vermutetem
wissenschaftlichen Fehlverhalten) according to § 9, Sec. 3 of the
„Promotionsordnung der International Graduate School of Neuroscience
der Ruhr-Universität Bochum“ were followed.
Signature
Date
93
Acknowledgments
This dissertation would not be possible without the contributions from the following
people:
First, I would like to thank Onur Güntürkün for the opportunity to conduct this
research. Onur was not only an engaging mentor who is always supportive and
cheering with a big smile, but also helped me to mature into a full-fledged scientist,
and prepared me for the scientific journey ahead.
I would also like to extend my gratitude to Martina Manns for scientific advices,
fruitful discussions, manuscript corrections; and especially for her infinite patience
and willingness to answer even the smallest and most stupid question with a smile.
To my fellow colleagues at the Biopsychology, and especially to Nina Patzke, Carlos
Valencia-Alfonso, Josine Verhaal, Qian Xiao and Lars Dittrich, who was also the
second feeding experimenter in experiment 2, for their help and immense support
throughout this time.
To Gregor Schöner, for each of the inspiring multidisciplinary discussions.
To the International Graduate School of Neuroscience (IGSN) for providing an
excellent international environment and broad-based training, and for supplying the
funds required for this research.
To the Ruhr University Research School for additional funding.
To my parents whom the fact that they are over the sea did not prevent them for
helping, supporting and motivating me 24-7, 365 days a year.
To Benedict Ng for constant help, for middle-of-the-night-emergency-thesis-reading,
for helpful discussions, and especially for believing in me.
To my devoted friends.
And lastly to
611, 616, 620, 623, 624, 625, 703,704, 706,
740, 760, 761, 762, 768, 773, 818, 891, 892
my lovely pigeons who are the true heroes of this study.
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Curriculum Vitae
Personal details:
Name: Ruth Adam
Date of birth: 25/09/1981
Place of birth: Haifa, Israel
Contact information:
MPI for Biological Cybernetics
NWG Noppeney
Spemannstraße 41
72076 Tübingen
Email: [email protected]
Primary and high school education:
1987-1993
Primary Education: 'Hare'ali Ha'ivri' School in Haifa.
1993-1999
Secondary Education: 'Hare'ali Ha'ivri' School in Haifa,
graduated with honors.
University Education:
10/2001-2004
Bachelor of Science in Cognitive Science and Biology under
the "Etgar” program, the Hebrew University of Jerusalem.
2002-2003
Studied the Turing Test. The Racah Institute of Physics, the
Hebrew University of Jerusalem, under the supervision of
Professor Sorin Solomon.
2004-09/2005
Master of Arts (magna cum laude) in Cognitive Science, the
Hebrew University of Jerusalem. Thesis title: "Artificial
enhancement of creativity, finding creativity patterns in art".
Supervisors: Professor Sorin Solomon, the Hebrew University
of Jerusalem and Doctor Ester Adi-Japha, Bar Ilan University.
07/2004
Participated in the "Model for Complex Systems in Human and
Social Science" summer school at the Ecole Normale
Superieure de Lyon.
95
08/2002-09/2005
Research assistance at the Neurobiology Department, the
Hebrew University of Jerusalem, under the supervision of
Doctor Binyamin Hochner. Studied the reaching movement of
regenerating arms of octopuses and probability learning in
octopuses.
09/2005-01/2008
PhD student in the International Graduate School of
Neuroscience (IGSN), Ruhr University Bochum, Germany.
Additional academic activity:
Symposium Organization:
04/2007 Animal Cognition: Studying the minds of avians (Bochum, Germany).
Reviewer:
Journal of Ethology
Talks:
11/2005 Warsaw University. Title: Creativity templates in art.
11/2005 Warsaw School of Social Psychology . Title: Creativity templates in art.
05/2007 Vienna University. Title: Pigeons’ ability to form new categories.
11/2007 Society for Neuroscience Annual Meeting 2007, San Diego. Title: Past
exposure helps pigeons to categorize artificial and complex stimuli.
02/2008 Keio University, "Rational animal, Irrational man" symposium. Title: The
discriminating brain: hemispheric interaction and visual tasks in the pigeon.
Other:
02/2000-10/2001
Military service: served as N.C.O teacher in the IDF.
96
Publications:
Published Abstract:
1. Adam R., Sumbra G., Flash T., Hochner B. and Yekutieli Y. (2004) Motor
control of a regenerated arm of the octopus, Neural Plasticity (abstracts of the
13th Annual Meeting of the Israel Society for Neuroscience), 12(1).
2. Adam R., Manns M. and Güntürkün O. (2006) To what extent can pigeons
learn new categories? Neural Plasticity (abstracts of the 15th Annual Meeting
of the Israel Society for Neuroscience), Vol. 2007(1).
3. Adam R., Manns M. and Güntürkün O. (2007) Pigeons and Pikachu: failure to
learn new category, Göttingen Meeting of the German Neuroscience Society
2007, T28-4A.
4. Adam R., Manns M. and Güntürkün O. (2007) Hemispheric symmetry in
exemplar and category computation, the 5th European Conference on
Comparative Neurobiology.
5. Adam R., Manns M. and Güntürkün O. (2007) Past exposure helps pigeons to
categorize artificial and complex stimuli, Soc. Neurosci. Abstr., 341.4
Papers:
1. Adam R., Hersberg U., Schul Y., Solomon S. (2004) Testing the Turing Test Do men pass it? IJMPC, 15(8), 1041-1047.
2. Adam R., Hersberg U., Schul Y., Solomon S. (2004) Do humans pass
the Turing Test? PhysicaPlus Online, 4.
97