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Running Head: Learning mechanism
Learning to use predictors in young children’s induction
Vladimir M. Sloutsky
and
Margie A. Spino
The Ohio State University
Please address correspondence to:
Vladimir M. Sloutsky
The Ohio State University
Center for Cognitive Science
21 Page Hall
1810 College Road
Columbus, OH 43210
Phone: (614) 688-5855
Fax: (614) 292-0321
Email: [email protected]
Source: Working paper
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Abstract
Can domain-general mechanisms explain the development of biological induction in young
children? To answer this question, 43 five-year-olds were presented with a learning (rule
discovery) task. The experiment included three between-subject learning conditions and
consisted of the Learning and Transfer phases. During the Learning phase, participants received
multiple feedback and no-feedback learning trials. On each learning trial, a participant was
presented with a Target picture and three Test pictures, such that one of the Test pictures was
perceptually identical to the Target, another had the same linguistic label, while the third shared
inheritance information. The participant was then told that each of the Test stimuli had a
particular biological property (e.g., blue blood vs. yellow blood vs. green blood), and was asked
to predict the blood color of the Target. In each of the three learning conditions, participant were
taught, by providing them with condition specific “yes/no” feedback, to use either perceptual
similarity, linguistic label, or inheritance as a predictor of the biological property in question.
Transfer trials using an altered task, took place no less than 60 minutes after the learning phase.
It was found that in each learning condition, most participants successfully completed learning
and retained this knowledge through the transfer phase, indicating that learning to use predictors
could be achieved without increasing domain-specific knowledge. At the same time, it was
easier to learn using perceptual similarity as a predictor of a biological property than it was to
learn using common label or inheritance information, which points to a pre-existing preference
for perceptual similarity as the basis of young children’s induction.
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LEARNING TO USE PREDICTORS IN YOUNG CHILDREN’S INDUCTION
Induction, or generalization of knowledge from familiar to novel entities or events, is a
critical component of human learning and cognition. Those familiar entities, which are used to
generalize from, are often referred to as the base of induction, whereas those novel entities,
which are used to generalize to, are referred to as the target. Induction is exemplified in the
extension of category membership to new members, word meaning to new objects, and
properties of a known entity to novel instances, as well as in assigning causes, predicting effects,
and formulating general rules. In fact, the centrality of induction for learning made one famous
statistician proclaim that induction is "the only process… by which new knowledge comes into
the world" (Fisher, 1935).
This research is focused on the induction of properties from known entities to novel
instances. Examples of such induction are (1) X1 has property Y, therefore X2 has property Y,
(2) Xs have property Y, Zs are like Xs therefore Zs have property Y, or (3) X1, X2, …, Xn have
property Y, therefore all Xs have property Y. The first type of induction has been defined as
specific induction, whereas the latter two have been defined as general induction (see Osherson,
Smith, Wilkie, Lopez, & Shafir, 1990, for a discussion). We focus here on specific induction.
Simple forms of induction, such as specific induction, seem to be a precondition rather than a
product of learning: indeed, before inducing that “knowledge is often generalizable,” one needs
to perform a number of knowledge generalizations. Thus, there is little surprise that even infants
are able to perform specific induction (Mandler & McDonough, 1996, 1998). For example, in
one experiment infants were habituated to a scene of a drinking bird. They later dishabituated to
a scene of a drinking airplane, but not to a scene of another drinking bird, thus indicating that
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they had generalized the ability to drink from one bird to another (Mandler & McDonough,
1996).
Furthermore, 3-4 year-old children were shown to exhibit a great deal of inductive
sophistication, distinguishing properties that could be generalized from those that cannot
(Gelman & Markman, 1986; Gelman, 1988; Gutheil, Vera, & Keil, 1998; Springer, 1992). For
example, children have exhibited knowledge that the property “has thick bones” could be
generalized from one bird to another, whereas “has a missing feather” cannot.
Finally, previous research found that children do use a variety of information cues when
performing induction. These cues include (but are not limited to) labeling information (Gelman
& Markman, 1986; 1987; Sloutsky & Lo, 2000b), kinship information (Gutheil, Vera, & Keil,
1998; Johnson & Solomon, 1997; Springer, 1992; 1996; Springer & Keil, 1989; Taylor, 1996),
and appearance (Gelman & Markman, 1987; Sloutsky & Lo, 2000b; see also Gelman & Medin,
1993; Keil, 1989; Smith & Jones, 1993, for reviews and discussions). For example, when
compared animals either share a label, belong to the same kin, or look alike, young children are
more likely to induce a biological property from one animal to the other than when they do not
share any of these properties.
While it appears early in development, inductive inference does undergo important
developmental changes. First, several researchers demonstrated that young children are not
sensitive to statistical properties of the base sample, such as sample size and sample diversity
(Gutheil & Gelman, 1997; Lopez, Gelman, Gutheil, & Smith, 1992). In particular, young
children are equally likely to generalize a biological property from Test stimuli to the Target
when the Test stimulus included a single animal or when it included multiple animals (Gutheil &
Gelman, 1997; Lopez, et al., 1992; Sloutsky & Lo, 2000a). At the same time, adults are more
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likely to generalize properties from multiple examples (Gutheil & Gelman, 1997; Nisbett,
Krantz, Jepson, and Kunda, 1983; Osherson, et al., 1990), indicating that they are sensitive to
sample size. In addition, young children did not consider induction from several diverse
examples to be stronger than induction from several homogenous examples, whereas adults are
sensitive to sample diversity and they consider the former induction to be stronger than the latter
(Gutheil & Gelman, 1997; Osherson, et al., 1990). For example, adults, but not children,
considered the inference that lions have biological property X to be stronger when told that
rabbits and elephants have the property (i.e., a diverse base) than when told that rabbits and
hares have the property (i.e., a homogeneous base).
Previous research also examined relative contributions of various informational cues to
inductive inference by pitting these cues against each other, and developmental changes in these
contributions (e.g., Gelman & Markman, 1986; Sloutsky & Lo, 2000a; Springer, 1992). In a
typical task examining the relative contribution of information cues, a child is presented with a
triad of pictures, two of which are Test items, and one is the Target item. Test 1 has a particular
appearance A1 and a particular category label L1, while Test 2 has another appearance A2 and a
different category label L2. Finally, the Target shares appearance with Test 1 and the category
label with Test 2 (i.e., A1L2), and the child is asked to generalize a biological property from one
of the Test items to the Target. In a variation of this task, instead of a shared label, shared
inheritance has been introduced (Springer, 1992). This research showed that children are more
likely to rely on category label than on appearance (Gelman & Markman, 1986) and on
inheritance information than on appearance (Springer, 1992). In addition, in the course of
development, attentional weights of different informational cues changed with weights of
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inheritance increasing and weights of appearance decreasing (Sloutsky & Lo, 2000c). In
particular, preadolescents are more likely than young children to rely on inheritance information.
Finally, some researchers examined joined contributions of information cues by using stimuli
where cues are bundled together (e.g., Sloutsky & Lo, 2000b, 2000c). To examine the joint
contribution, children were presented with tasks where one Test stimulus shared several cues
with the Target, whereas another Test stimulus shared a single cue. For example, Test 1 shared
inheritance and appearance with the Target, whereas Test 2 shared the label with the Target. It
was found that young children were more likely to generalize biological properties from a Test
stimulus that shared several information cues with the Target than from the one that shared only
one information cue, regardless of the type of cues (Sloutsky & Lo, 2000b, 2000c). At the same
time, regardless of the number of shared cues, preadolescents and adults invariably relied on
inheritance as the most predictive source of information. It was concluded, therefore, that young
children integrated multiple cues (or features) when performing inductive inference, whereas
preadolescents and adults relied on a single source of information that they deemed most
predictive. These findings are consistent with other research indicating that processing develops
from holistic to dimensional (Shepp, 1978; Shepp & Swartz, 1976; Smith, 1989a, 1989b).
In short, previous research found that (a) young children are not sensitive to statistical
properties of the base sample, such as sample size and sample diversity and (b) linguistic labels
and kinship information more readily drive young children’s induction than perceptual similarity.
It was also found that induction undergoes important developmental changes: (1) young children
use multiple sources of information, while preadolescents and adults rely on a single most
predictive source, and (2) preadolescents are more likely to rely on inheritance information in
induction of biological properties than young children.
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These developmental transitions further indicate that in the course of development, weights
of different information cues change, with weights of some cues (e.g., inheritance) increasing
and other cues (e.g., similar appearance) decreasing. However, while changes themselves are
documented (Sloutsky & Lo, 2000c), reasons for these changes remain unclear. The goal of
current research is to examine these reasons.
Theoretically, this transition from integration of multiple features to induction based on a
single feature could stem from a variety of sources. First, this transition could stem from the
increase in the domain-specific biological knowledge: preadolescents learn in biology class that
inheritance is a causal determiner of anatomical and physiological properties, whereas a common
label or similar appearance are not causal determiners. Alternatively, the transition could stem
from a domain-general mechanism of probabilistic learning. In this case, even if they do not
know causal connections between inheritance and biological properties, common inheritance
could be more strongly associated with common biological properties than common label or
common appearance, because the former co-occur more frequently than the latter. As children
grow, they accumulate more evidence supporting these associations, and, as a result, these
associations get stronger. Finally, the transition could stem from a combination of the domainspecific knowledge of the causal importance of inheritance and domain-general knowledge of
conditional probabilities or contingencies (cf. Cheng, 1997).
In this research, we specifically focus on the role of domain-general factors in the
development of induction. In particular, we attempt to train young children to rely on a single
cue (predictor) without explaining the causal importance of this predictor for inducing biological
properties (i.e., without introducing domain-specific knowledge). Recall that in prior research
young children were shown to rely on multiple cues (as opposed to a single cue) when the cues
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were bundled, or to rely on common label or inheritance when these cues were pitted against
appearance (Sloutsky & Lo, 2000b, 2000c). Note that inheritance, linguistic labels, and
perceptual similarity have different causal status with respect to induced biological properties.
While inheritance could be construed as a direct cause of biological properties, appearance
cannot be construed as a cause of biological properties. At the same time, linguistic labels could
be construed either as a marker of an essence, which in turn causes biological properties, or as
cue co-varying with biological properties in a non-causal manner. Furthermore, unlike
inheritance, which is specific to the domain of biology, common appearance and common labels
frequently co-vary with hidden properties (e.g., an object’s function) across various domains. If,
regardless of the causal status of the predictor, learning to use each of these cues as predictors
would be successful, this would indicate that knowledge of causal relations between the predictor
and predicted property is not necessary for successful induction. Successful learning would also
indicate that young children have requisite attentional capacities to focus on a learned
information cue, while ignoring the rest. Of course, because domain-specific knowledge is not
actively manipulated in current research, successful learning would be unable to either confirm
or eliminate possible contributions of domain-specific knowledge in the development of
inductive inference.
In this research, we used a paradigm that made use of both separate and bundled information
cues. In the Learning phase, three cues (i.e., inheritance information, label, and appearance)
were pitted against each other, and participants were trained to base their induction on a single
cue through the use of “yes/no” feedback. In the transfer phase, two information cues (e.g.,
appearance and label) were bundled together and pitted against the learned cue (e.g.,
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inheritance). Participants were presented with both implicit and explicit feedback learning trials
and with no-feedback transfer trials.
This paradigm affords answers to several important questions. First, what are attentional
weights of different information cues when these cues are non-bundled and pitted against each
other? Second, can these weights be readjusted in the course of learning without an increase in
domain-specific knowledge? Third, do potential causal links between predictors and induced
biological properties support learning? And fourth, can feature-integration be overridden, that is,
can reliance on a single source of information observed in preadolescents and adults be learned
by young children? To answer the posed questions, we analyzed learning across the three types
of cues, focussing on the number of trials to criterion and the quality of transfer.
Answers to the posed questions are critically important for understanding mechanisms of
knowledge acquisition as well as changes in these mechanisms in the course of development and
learning. These answers are also important for better understanding of the nature of young
children’s induction, which could be a function of knowledge-independent attentional
mechanisms (Sloutsky & Lo, 2000b; Smith & Jones, 1993), knowledge-dependent naïve theories
(Gelman & Coley, 1991; Gelman & Medin, 1993; Gelman, Coley, & Gottfried, 1994), or a
combination of both.
EXPERIMENT
The experiment included three between-subject learning conditions and consisted of the
Learning and Transfer phases. During the Learning phase, each participant received up to 10
learning trials. On each learning trial, a participant was presented with a Target picture and three
Test pictures of imaginary animals, such that one of the Test animals was perceptually identical
to the Target, another had the same linguistic label, while the third shared inheritance
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information. After that, the participant was told that each of the Test stimuli had a particular
biological property (e.g., blue blood vs. yellow blood vs. green blood), and was asked to predict
the blood color of the Target. In each of the three learning conditions, participants were given
different feedback. In the Inheritance condition, they were given positive feedback if they
induced properties from the Test sharing inheritance and negative feedback if they induced
properties from Tests sharing other cues. At the same time, in the Label or Perceptual Similarity
conditions they were given positive feedback if they induced along the same label or the same
appearance respectively but negative feedback for inducing properties from Tests sharing other
cues. Note that on each trial, participants were presented with different pictures, labels, and
biological properties. Therefore, within each learning condition, the task of learning was to
formulate a general rule (e.g., if a Test and the Target share the label, they also share biological
properties) from a number of specific observations (e.g., Two items labeled “a Pofa” had green
blood).
Participants
Forty-three preschool children (mean age = 5.0; SD = 0.29 years; 20 boys and 23 girls)
participated in this experiment. These participants were recruited from daycare centers located
in upper middle class suburbs of Columbus, Ohio, and were selected on the basis of returned
permission slips.
Design
The experiment had a mixed design with Learning condition as a between-subject factor, and
Experimental phase as a within-subject variable. The Learning condition had three levels: (1)
Label (L), (2) Perceptual Similarity (PS), and (3) Inheritance (INH), with participants being
randomly assigned (stratifying by gender) to one of these conditions. During learning,
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participants were taught to rely on one information cue (i.e., Label, Perceptual Similarity, or
Inheritance) for inducing biological properties. The experiment included two within-subject
Experimental phases: (1) Learning and (2) Transfer.
Materials
Materials consisted of line-drawing pictures, labels, and biological properties. The
experimenters developed 120 line-drawing pictures of animals (4 stimuli * 10 trials * 3
conditions = 120 stimuli) for the Learning Phase; 108 of these pictures were used in the Transfer
phase (3 stimuli * 12 trials * 3 conditions = 108 stimuli). The pictures were constructed so as
not to resemble any actually existing animals. To further avoid confounds with existing
knowledge about specific animals, artificial labels were used. These auditorily presented labels
consisted of short two-syllable words presented as count nouns (e.g., a Guga, a Pofi, a Boto,
etc.). The biological properties to be generalized from one of the Test stimuli to the Target
referred to different colors of blood and bones (e.g., red bones vs. brown bones vs. white bones).
Procedure
Each child was tested individually in a room outside the classroom. The Learning and
Transfer phases were conducted on the same day; the Transfer phase was begun no less than 60
minutes after completing the Learning Phase. This between-phase interval was adopted due to
the amount of time required to complete each Phase (approximately 20 minutes) and so as not to
overwhelm the children; children returned to their classrooms during this interval. The
Experiment was administered on a Dell Inspiron 3500 laptop computer using the program
Superlab 2.0 (Cedrus Corporation, 1999).
Learning Phase. The Learning phase consisted of a maximum of 10 learning trials with each
trial using four stimuli (a Target stimulus and three Test stimuli). Each of the Test stimuli shared
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one information cue with the Target, while differing on the other two cues (see Figure 1). As
shown in Figure 1, one of the Test stimuli looks like the Target, another shares a label with the
Target, whereas the third shares inheritance information with the Target. Each trial used
different labels, pictures, and biological properties. The positioning of the Test stimuli relative
to the target was varied across trials. Participants were asked to generalize an unobservable
biological property (e.g., blood color) from one of the Test stimuli to the Target. After making
the generalization, participants were presented with one of three types of feedback—implicit
feedback, explicit feedback or no feedback (see Figure 2). As shown in Figure 2, on trials 1 to 5,
participants’ responses were followed by implicit feedback (either “That’s right!” or “Try
again.”). “That’s right!” feedback was provided only when a participant made a conditionconsistent response (e.g., induction based on a shared label in the Label Condition), otherwise
feedback “Try again” was provided.
On Trials 6 and 7, participants received explicit feedback. After making a response on these
trials, participants were told whether or not their response was correct (i.e., condition consistent)
and what was the correct response. For example when inducing on the basis of label in the label
learning condition, a participant would be told, “That’s right, the correct one is the one with the
same name.” On the other hand, when inducing on the basis of another cue in the label learning
condition, a participant would be told, “Try again.” On Trials 8 through 10, participants were
presented with no feedback after making their generalizations.
On each of the implicit and explicit feedback trials, if a participant responded in a manner
inconsistent with a respective learning condition (e.g., a participant in the Inheritance condition
basing induction on perceptual similarity), the Test stimulus that led to an erroneous response
was removed. The participant was then asked to perform induction with the two remaining Test
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stimuli. If the participant again responded in a manner inconsistent with a respective learning
condition, the Test stimulus that led to an erroneous response was removed again, and the
participant was then asked to perform induction with the one remaining Test stimulus. In the
latter case, the participant had no other choice but to perform induction in a manner consistent
with the respective learning condition. After making a condition-consistent response,
participants received either implicit or explicit feedback and proceeded to the next trial. When
condition-consistent responses were given on three consecutive learning trials, the Learning
Phase was discontinued. If three consecutive condition-consistent responses were not obtained
by Trial 5, then on Trials 6 and 7 participants were given explicit feedback. Those participants
who achieved three consecutive condition-consistent responses by Trial 10 proceeded to the
Transfer phase. Those participants who did not achieve that criterion did not participate in the
Transfer phase. Note that more than 85% of participants in each condition did achieve the
learning criterion.
Transfer Phase. Participants who made three consecutive condition-consistent responses by Trial
10 of the Learning Phase participated in the Transfer phase that started no less than 60 minutes
after completing the Learning Phase. The Transfer phase consisted of 12 trials. On each of these
12 trials, participants were presented with three stimuli (a Target and two Test stimuli). On some
trials, one Test stimulus shared a learned property with the Target (e.g., the label), whereas
another Test stimulus shared one of the remaining properties (e.g., appearance) with the Target.
We will refer to these trials as T-L-1, with T referring to the Target, L referring to a learned cue,
and 1 referring to another property pitted against the learned one. On other trials, one Test
stimulus shared a learned property with the Target (e.g., the label), whereas another Test
stimulus shared both of the remaining properties (e.g., appearance and inheritance) with the
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Target. We will refer to these trials as T-L-2, with T referring to the Target, L referring to a
learned cue, and 2 referring to two properties pitted against the learned one. Each participant
received 8 T-L-1 transfer trials (4 trials for each of the two remaining properties), and 4 T-L-2
transfer trials. An example of the T-L-2 combination in the Perceptual Similarity Condition (PS
vs. L + Inh) is given in Figure 3. After completing the experiment, each participant was
debriefed. They were reminded that this was just a game and were told that in real life animals
are like the ones that gave birth to them.
Memory Check. On Trial 6 of the Learning Phase and on Trial 11 of the Transfer Phase a
memory check was conducted to ensure that children remembered the cues associated with each
Test. The participants were asked to indicate which stimuli shared a label, shared inheritance
information, and possessed a certain biological property. No feedback as to the accuracy of the
response was provided.
Results
Results of the Memory Check indicated that the majority of the children were able to
remember the stimuli that shared a label, shared inheritance information, and possessed a certain
biological property on at least one of the two memory check trials. Of those who participated in
the Transfer Phase, 83% passed the memory check for labels, 94% passed for inheritance, and
97% passed for biological properties. All the two children who did not pass the memory check
for labels were in the PS learning Condition. In fact, all the children in the Label Condition
passed the memory check for labels, and all the children in the Inheritance Condition passed the
memory check for inheritance.
In what follows, we first analyze participants’ performance in the Learning Phase, followed
by analyses of their performance in the Transfer phase. First, we deemed it necessary to examine
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initial feature preferences by analyzing participants’ first and second choices on the very first
trial, i.e., prior to any effects of training. Results indicate that on the first learning trial across all
three conditions, approximately 81% of all children chose the stimulus that was perceptually
similar (PS) to the Target as their first induction choice (above chance, Confidence Interval from
63% to 92%, p < .05). Given PS as a first choice, 67% (not different from chance) of the
children used L as a second choice while 33% (not different from chance) chose INH as a second
choice1. In other words, before training, children had a strong preference for using PS to
perform inductions with L and INH being chosen at chance levels as the second choice.
The second goal of our analysis was to examine whether this initial preference can be
overridden and whether the ease of learning depends on the Learning Condition. To achieve this
goal, we compared learning indicators, such as the number of learning trials to criterion and the
number of feedback trials, across the Learning Conditions (see Table 1). The number of learning
trials to criterion was subjected to a one-way ANOVA with Learning Condition as a factor.
There was a main effect of Learning Condition, F(2, 35) = 59.82, MSE = 1.66, p <. 0001. Post
hoc Bonferroni tests pointed to the following differences in the number of learning trials across
the Learning Conditions: L > INH > PS, all ps < .01. The analysis indicated that more trials were
needed to acquire Label as a predictor than the other cues, and the least number of trials were
needed to acquire PS as a predictor, with inheritance falling in between.
These findings were further supported in a related analysis of the mean number of feedback
trials required to reach the learning criterion. These numbers were also subjected to a one-way
ANOVA with Learning Condition as a factor. Similar to the previous analysis, there was also a
significant main effect of Learning Condition, F(2, 40) = 58.46, MSE = 0.24, p < .0001. Again,
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post hoc Bonferroni tests for multiple comparisons indicated significant differences among all
conditions: L > INH > PS, all ps < .05.
These findings indicated differences in how easily children learned to respond in a conditionconsistent manner depending upon condition. As previously stated, children had a strong
preference for using PS as their first choice on Learning Phase trial 1; as shown here, children
were quickest to reach criterion and consequently required fewer feedback trials when they were
in the PS condition. While preferences indicated that as a second choice L and INH were chosen
at statistically equivalent frequencies (with a slight preference for L), the L condition required
the greatest number of learning trials to reach criterion as well as the greatest number of
feedback trials (see Table 1).
To compare the speed of learning across the conditions, we also analyzed the number of
choices consistent with the respective learning condition across learning trials. These data are
presented in Figure 4. Data in the figure indicate that while in the PS condition, participants
reliably answered in the condition-consistent manner from the outset of learning; in the L and
INH conditions they were at chance until receiving an explicit feedback. Data in Figure 4 also
indicate that the initial preference for perceptual similarity can be overridden: In all three
conditions, at the end of learning, the number of condition consistent responses was significantly
above chance.
We also examined whether or not the likelihood of reaching the learning criterion depended
upon the learning condition. It was found that in both PS and INH conditions, there were more
children who met the learning criterion than those who did not (both Confidence Intervals from
60% to 99%, ps < .05). In the Label Condition 75% of children met the criterion; in the
Perceptual Similarity Condition 100% of children met criterion, and in the Inheritance Condition
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93% met criterion. These proportions did not differ significantly, χ2 (2) = 4.72, p = .09.
Although the lack of differences between the Label condition and the other two conditions is
likely to be due to a low power, the proportions of children reaching the criterion in the PS and
INH condition was very close. In other words, even though children may have been quicker to
learn in the PS condition compared to the INH condition, the proportion of children in each
condition who reached criterion by the end of learning was statistically equivalent. Possible
reasons for the relative ease of learning in the PS condition are addressed in the discussion
section. In short, by the end of learning the majority of children in each condition learned the
rule and used condition-consistent features to perform inductions.
Therefore, these results suggest that (a) children have initial preferences for using PS as a
predictor, (b) the initial preference can be overridden, (c) PS learning condition was the easiest,
and (d) L was the most difficult cue to acquire predictive status. These findings are novel and
important: taken together they indicate that changing from PS to L may be more difficult than
changing from PS to INH.
During the Transfer Phase it was examined whether children in their induction continued
relying on this single learned cue when this cue was pitted against the other cues, as well as a
bundle of these other cues. In addition, does the reliance on the learned cue differ across the
conditions? Overall, there were 83% of condition consistent choices in the L condition, 73% in
the INH condition, and 99% in the PS condition, all above chance, all ts > 2.1, ps < .05. To
further examine this issue, a two-way mixed ANOVA with learning condition as a factor and
combination (T-L-1 vs. T-L-2) as a repeated measure was performed. The analysis indicated the
main effect of learning condition missed significance, F(2, 35) = 2.69, p = .081, and neither main
effect of the combination nor the interaction approached significance, both Fs < 1. Therefore,
Learning mechanism -- 18
when a cue was learned, participants were equally likely to rely on this cue when it was pitted
either against a single cue or against several cues.
Discussion
Results indicated four main findings. First, before training, young children displayed a
strong preference for perceptual similarity as the source of their induction. Second, their initial
preferences can be overridden with knowledge-independent training: In the transfer phase
participants consistently relied on the learned source of prediction. Third, learning was
successful in all three conditions, regardless of the predictor. And finally, learning in the
Perceptual Similarity condition was the easiest, whereas learning in the Label condition was the
most difficult.
Results of the experiment indicate that young children have an initial cue preference, with the
majority of children relying on perceptual similarity as a source of their induction. This finding
is worth noting because it runs counter to the majority of prior research. Recall that most of
prior research indicates that, when perceptual similarity is pitted against other sources of
information (e.g., labels or kinship information), young children are less likely to rely on
perceptual similarity than on other sources of information (Gelman & Markman, 1986; Springer,
1992; see also Gelman & Medin, 1993 for a discussion). We think that this divergence from
prior research has several potential reasons. First, some previous studies have used perceptual
stimuli exhibiting little variability (see Smith & Jones 1993, for a discussion), and if an
independent variable exhibits small variance, its contribution is destined to be small. For
example, Springer (1992) used stimuli that were very similar overall (see Figure 5). Although
Test B was claimed to be less similar to the Target than Test A, these differences are negligible
compared to differences typically observed between different individuals or between different
Learning mechanism -- 19
species. Note that when two depicted objects A and B are compared, similarity or difference
may refer to (a) picture A and picture B, (b) individual A and individual B, and (c) category A
and category B. For example, two photographs of the same person writing a letter and reading a
book are dissimilar with respect to (a), but not with respect to (b) or (c). It seems that Springer’s
(1992) Test A and Test B, depicted in Figure 5, are also dissimilar only with respect to (a), but
not with respect to (b) and (c). However, starting around 30 months of age, children start
interpreting pictures as symbols depicting objects (DeLoache & Burns, 1994), hence performing
similarity judgments across objects depicted by pictures, not across pictures themselves.
Therefore, it is very likely that children in Springer’s (1992) experiments interpreted both Test A
and Test B as depicting individuals that are very similar between themselves and to the Target,
thus further decreasing contribution of perceptual information to their induction. Thus, it is likely
that studies using highly similar perceptual stimuli underestimate the contribution of perceptual
similarity to children’s induction. At the same time, current research used stimuli exhibiting
large perceptual variability pointing to differences between pictures, individuals, and, in all
likelihood, categories. This larger variability of PS allows a better estimation of perceptual
factors in induction.
Another potential factor is the task difficulty. There are reasons to believe that due to a
larger number of information sources and choice options (three instead of two), our task has
greater demands than those used in previous research. For example, while both inheritance and
labeling information was given auditorily and thus had to be remembered, perceptual
information was always visually present. Even though children exhibited good memory for
presented information, a larger memory load might have contributed to the task difficulty. At the
same time, it seems reasonable to expect that with an increase of task difficulty, people should
Learning mechanism -- 20
fall back on a default mechanism. If this is true, then perceptual similarity is an important
default mechanism in young children’s induction (see Keil, 1989 for related arguments).
Another important finding was that these initial preferences could be overridden: the majority
of the young children learned to rely on a single (predetermined) cue when performing
inductions. Furthermore, after learning (in the Transfer phase) all groups of participants
continued relying on the learned cue. Young children learned to rely on a particular feature in
the course of 6-7 learning trials, with only 2 of which providing explicit feedback. Even when
explicit feedback was given to a child, it was still the child’s task to discover a general rule.
Therefore, it seems likely that young children have a rather powerful knowledge-independent
(and thus domain-general) learning mechanism, allowing them to determine which source (or
sources) of information better predicts unobservable properties. These children may have
various default mechanisms (i.e., feature-integration, in the case of bundled features, and
perceptual similarity in the case of separate features), but they ultimately learn to rely on a
feature that better predicts unobservable properties. It is also worth noting that in the Transfer
phase young children continued using the learned predictor in a relatively novel task (recall that
in the Transfer phase task features were bundled, as opposed to the Learning phase task where
features were separate). Therefore, (a) knowledge of causal relations between the predictor and
predicted property is not necessary either for successful induction or for successful rule
discovery, and (b) young children have requisite attentional capacities to focus on a single
information cue. If used as a microgenetic model of a developmental course, this experiment
elucidates a possible developmental scenario in which biological induction could be a product of
a domain-general mechanism.
Learning mechanism -- 21
One issue that cannot be conclusively addressed by this research is reasons for differential
difficulty of learning exhibited by young children. Recall that there were differences in
overriding the feature preference – changing from PS to L appeared to be more difficult than
changing from PS to INH. This differential difficulty of learning could stem from a variety of
sources. In particular, while inheritance information was binary (either gave birth or did not),
labeling information had three levels (one label shared with the Target and two different labels).
Therefore, there were larger cognitive demands in using label as predictor than in using
inheritance as predictor. It is also possible that it is difficult for a child to separate linguistic
label and perceptual similarity (things that look identical typically have same names), thus
increasing the difficulty of learning in the label condition. Of course, other explanations are
possible, and additional research is needed to distinguish among these possibilities.
Taken together these results lend support to the suggestion that a domain-general attentional
mechanism plays an important role in induction. Recall that three possible reasons for a young
child’s transition from feature-integration to single-feature induction were posited: (1) an
increase in domain-specific biological knowledge, (2) a domain-general attentional mechanism,
or (3) a combination of the previous two. In this experiment it was found that young children
could be taught to rely on a single feature to make inductions without explaining the importance
of the feature as a predictor. Thus, induction based on a single predictor was learned without
increasing domain-specific biological knowledge about the causal importance of this predictor.
Therefore, in this experiment, increasing domain-specific biological knowledge was not
necessary in order to invoke the transition from multiple- to single-feature induction in young
children.
Learning mechanism -- 22
While these findings add to our understanding of induction and its development, and,
thereby, to our understanding of knowledge acquisition and its development, many questions
remain. How robust is the learning? Would young children continue using the learned predictor
after a longer period of time after learning? Could these findings be replicated using different
predictors and different domains, such as with chemical properties of solutions, or with artifacts?
And if so, is there an across domain transfer?
These questions have to be answered in future research. At the same time, the reported study
allows us to make the following conclusions. First, given the increased task demands, young
children are more likely to rely on perceptual similarity when performing induction than they are
to rely on linguistic labels and inheritance information. Second, the initial reliance on a predictor
can be overridden in the course of learning regardless of the predictor, and a newly learned
predictor can be used in a novel task administered a short time after the original task. Third, it
was easiest to learn using appearance as a predictor, and most difficult to learn using labeling
information. Fourth, knowledge of causal relations between the predictor and predicted property
is not necessary for successful induction. And, finally, young children have requisite attentional
capacities to focus on a learned information cue, while ignoring the rest.
Learning mechanism -- 23
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Author Note
Vladimir M. Sloutsky, Center for Cognitive Science and Margie A Spino, Department of
Psychology and Center for Cognitive Science. This research has been supported in part by
grants from the James S. McDonnell Foundation and the National Science Foundation to the first
author. We would like to thank Aaron Yarlas for his helpful comments and suggestions.
Correspondence concerning this article should be addressed to Vladimir M. Sloutsky, The
Ohio State University, 21 Page Hall, 1810 College Road, Columbus, OH 43210, USA, E-mail:
[email protected].
Learning mechanism -- 28
Table 1
Means and standard deviations for learning indicators broken down by learning conditions
Learning Indicators
Learning Condition
Mean and SD of
Mean and SD of
Trials to Criterion
Feedback Trials
Label
9.00 (0.60)
2.00 (0.00)
Perceptual Similarity
3.42 (0.79)
0.00 (0.00)
Inheritance
7.36 (1.90)
1.47 (0.83)
Learning mechanism -- 29
Figure Captions
Figure 1. Example of stimuli used in the Learning Phase.
Figure 2. Overall design of the experiment.
Figure 3. Example of stimuli used in the Transfer Phase.
Figure 4. Proportion of condition consistent responses by serial position of the Learning trial and
learning condition. Note: ** Above chance, p < .01; + below chance, p < .05. FB1 = the first
trial with explicit feedback. FB2 = the second trial with explicit feedback.
Figure 5. Example of stimuli used in Springer (1992) experiments.
Learning mechanism -- 30
Figure 1
Learning mechanism -- 31
Figure 2
LEARNING PHASE
T1
T2 T3 T4 T5
Implicit Feedback
TRANSFER PHASE
T6 T7
Explicit Feedback
T8 T9 T10
No Feedback
T1
to
T12
No Feedback
Learning mechanism -- 32
Figure 3
Learning mechanism -- 33
Proportion of condition-consistent
choices
Figure 4
1 **
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
+
0.1
0
1
**
**
**
**
**
**
Label
PS
Inh
2
3
4
5
6 (FB 1) 7 (FB 2)
Learning Trials
8
9
Learning mechanism -- 34
Figure 5
Learning mechanism -- 35
Footnotes
1
Recall that in Learning trials children were presented with three Test stimuli, and that,
therefore, chance equals 33%, whereas when making their second choice, children chose
between two remaining Test stimuli, thus making chance level equal to 50%.