New Evidence for Distinct Right and Left
Brain Systems for Deductive versus
Probabilistic Reasoning
Lawrence M. Parsons and Daniel Osherson1
Deductive and probabilistic reasoning are central to cognition but the
functional neuroanatomy underlying them is poorly understood. The
present study contrasted these two kinds of reasoning via positron
emission tomography. Relying on changes in instruction and psychological ‘set’, deductive versus probabilistic reasoning was induced
using identical stimuli. The stimuli were arguments in propositional
calculus not readily solved via mental diagrams. Probabilistic
reasoning activated mostly left brain areas whereas deductive
activated mostly right. Deduction activated areas near right brain
homologues of left language areas in middle temporal lobe, inferior
frontal cortex and basal ganglia, as well as right amygdala, but not
spatial–visual areas. Right hemisphere activations in the deduction
task cannot be explained by spill-over from overtaxed, left language
areas. Probabilistic reasoning was mostly associated with left
hemispheric areas in inferior frontal, posterior cingulate, parahippocampal, medial temporal, and superior and medial prefrontal
cortices. The foregoing regions are implicated in recalling and
evaluating a range of world knowledge, operations required during
probabilistic thought. The findings confirm that deduction and
induction are distinct processes, consistent with psychological
theories enforcing their partial separation. The results also suggest
that, except for statement decoding, deduction is largely independent of language, and that some forms of logical thinking are
non-diagrammatic.
Of the varied talents of the human brain, one of the most impressive is its ability to reason, that is, to reach new conclusions
(however tentative) on the basis of facts and suppositions already
in hand. As much as perception, language and motor behavior, it
is reason that allows us to interact successfully with the physical
and social environment. In spite of the centrality of reasoning, its
functional neuroanatomy is at present poorly understood and has
only recently begun to be examined via brain imaging.
The goal of the present study is to identify and contrast the
brain areas underlying deductive versus probabilistic reasoning.
Deduction underlies the intuition of necessity that accompanies
inferences like: ‘No politician is both lazy and corrupt, therefore
every politician is either not lazy or not corrupt’. Probabilistic
reasoning yields intuitions of likelihood (in contrast to certainty). For example, many people judge it likely that the ice
caps will melt before 2050 on the assumption of increased oil
consumption in the next 50 years.
Alternative perspectives on the psychology of deductive and
probabilistic thought suggest different hypotheses about the
functional neuroanatomy of reasoning. According to one school
of thought, deductive reasoning is grounded in the structure
of language (Braine, 1978; Rips 1994). Such a view makes it
plausible that logical thinking depends on left-hemisphere
language areas. [The dependence is often claimed explicitly, as
in Weston, p. 116 (Weston, 1999).] A rival perspective grounds
logical implication in the non-linguistic interpretations that
render sentences true or false (Johnson-Laird, 1994). Since the
interpretations are conceived as schematic in character, righthemisphere participation in logical thinking is foreseen [as in
Johnson-Laird (Johnson-Laird, 1994)]. Theories that conceive
probabilistic and logical reasoning as drawing on similar mental
operations (Johnson-Laird et al., 2001) raise the possibility of
similar neural substrates for the two kinds of thinking. This
ver y hypothesis is advanced in Cosmides and Tooby (p. 61)
(Cosmides and Tooby, 1996).
Some of the foregoing claims have been evaluated via neuroimaging studies of syllogistic reasoning. Using positron emission
tomography (PET) to record brain blood f low in men reasoning
about syllogistic stimuli, two studies observed distinct neural
activations for probabilistic versus deductive problems (Goel
et al., 1997; Osherson et al., 1998). But in one study deduction
was primarily in right posterior and right frontal brain areas
(Osherson et al., 1998) whereas in the other it was mostly in left
frontal and left temporal brain areas (Goel et al., 1997). The
latter finding was replicated in a follow-up experiment in which
men reasoned about categorical syllogistic, spatial relational and
non-spatial relational deductive problems (Goel et al., 1998).
Even for spatial relational syllogisms, Goel et al. observed
activations only in the left hemisphere (Goel et al., 1998). This is
unexpected in light of earlier reports of impaired reasoning on
spatial relational problems by patients with insult to the right
hemisphere (Caramazza et al., 1976; Hier and Kaplan, 1980,
Read, 1981; Grossman, 1982; Grossman and Haberman, 1987).
The inconsistent findings may result from different methods
of contrasting the activations associated with alternative forms of
reasoning. In Osherson et al., identical stimuli were used to elicit
both probabilistic and deductive reasoning; instructions and
preceding problems were used to ‘set’ the thought processes
applied to a given stimulus (Osherson et al., 1998). In Goel et al.,
distinct stimuli were employed to elicit the two forms of reasoning, thus leaving open the possibility that observed activations
depended as much on the stimuli as the reasoning task (Goel
et al., 1997). The discrepant findings might also be partly explained by the special character of syllogisms, which are known
to encourage a variety of reasoning strategies, including mentally
represented diagrams (Ford, 1995). It may be that subjects
in the different studies relied on diagrammatic reasoning to
different extents, leading to different activation levels in brain
areas hypothesized to underlie visual imagery, i.e. bilateral or
predominantly right posterior, frontal and prefrontal regions
(Jonides et al., 1993; Kosslyn, 1994; Courtney et al., 1998;
Parsons and Fox, 1998; Dehaene et al., 1999; Farah, 1999).
To reduce the role of strategies involving visual–spatial imagery, the present study employed tasks based on propositional
logic, which lends itself less readily to diagrammatic reasoning.
For example, from the premise If it is cold in Sacramento then
it is cold and rainy in San Francisco, propositional logic allows
© Oxford University Press 2001. All rights reserved.
Cerebral Cortex Oct 2001;11:954–965; 1047–3211/01/$4.00
Introduction
University of Texas Health Science Center at San Antonio, San
Antonio, TX and 1Rice University, Houston, TX, USA
deduction of the conclusion If it is not rainy in San Francisco
then it is not cold in Sacramento. A sophisticated reasoner
might discover a diagrammatic scheme for verifying the validity
of such inferences, but this seems less likely than for syllogisms.
We therefore constructed propositional logic stimuli each of
which could support both probabilistic and deductive reasoning
tasks, as well as a comprehension control task (involving detection of semantic anomaly). PET measures of regional cerebral
blood f low were then obtained while subjects performed the
three tasks with identical, visually presented stimuli. The
different kinds of reasoning (probabilistic, deductive, anomaly
detection) were psychologically ‘set’ by changing the instructions to subjects, and by embedding the scanned tasks within
longer series of problems involving reasoning of the desired
kind.
In sum, the present study was designed to determine the
extent to which deductive and probabilistic reasoning rely on
the same brain regions, as well as to shed light on the role of
primary linguistic areas and of primary visual–spatial processing
areas in each type of reasoning.
Materials and Methods
In overview, subjects faced three tasks. The deduction task required
distinguishing valid from invalid arguments (a mix was given). The
probability task required judging whether the conclusion of an argument
was more likely to be true than false given the premises (based on pilot
studies, we were able to construct arguments that elicit a range of intuitions). The comprehension control task required detecting anomalous
content in premises or conclusion. We now provide details, starting with
a description of the kind of arguments used across the three tasks.
Materials
On each trial, subjects viewed an argument composed of two premises
followed by a conclusion. The first premise was shown for 3 s, then joined
by the second premise for a further 3 s, and then joined by the conclusion
for another 14 s. All arguments in the experiment were composed of sentences bearing on a man drawn randomly from a small Texas town known
to the subjects. The chosen man was denoted ‘he’. Sample arguments are
shown in Figure 1. In Figure 1a are examples of valid and invalid stimuli
from the deduction task. Figure 1b contains examples of stimuli from the
probabilistic reasoning task. In Figure 1c are examples of stimuli with and
without anomalous content from the comprehension control task. As
described below, six invalid arguments were evaluated on three separate
occasions: once for validity (deduction task), once for high probability
of the conclusion (probabilistic reasoning task), and once for anomaly
(comprehension control). Figure 1d shows all six of these arguments.
(The full set of stimuli is available at http://ric.uthscsa.edu/staff/
parsonsprojects/stimuli.html.)
On each PET scan, or task, subjects evaluated a sequence of five
distinct arguments. In the context of suitable instructions, the first two
arguments in a task were designed to exercise a specific kind of reasoning
(involving probability, deduction or comprehension). The subject was
only scanned while reasoning about the third and fourth arguments.
To summarize, a task was a sequence of five arguments of which just
the third and fourth were involved in scanning; the others were used to
establish ‘set’ and to exercise the desired form of reasoning.
Tasks were organized into ‘matched sets’. A matched set consisted of
a probability task, a deduction task, and a comprehension task with the
property that the scanned arguments (third and fourth) were identical
across the three tasks. Three matched sets were constructed, with no
overlap in the arguments composing them. The nine resulting tasks were
presented to the subject in random order under the constraint that
two tasks from the same matched set (hence, with identical scanned
arguments) not occur successively. We now describe in more detail the
three kinds of task figuring in each matched set.
In the probability task (Fig. 1b), subjects covertly judged whether a
conclusion was more likely to be true than false, assuming the truth of the
premises. All of the arguments in the probability task were (logically)
invalid. The information in the premises was therefore insufficient to constrain the conclusion to have either high or low probability. The judgment
was thus subjective in character, and required the reasoner to integrate
background knowledge (e.g. about jobs and recreation) not explicitly
presented in the argument. Of course, the reason for choosing only
invalid arguments for the probability task is that valid arguments are
probabilistically degenerate.
In the deduction task (Fig. 1a), subjects covertly judged whether the
conclusion followed logically from the premises. This judgment involves
the detection of logical necessity rather than probability. No background
knowledge is required for the reasoning since the information provided
by the premises suffices to determine whether the inference to the conclusion is logically valid. Approximately half the arguments in a given
deduction task were valid, the rest invalid.
In the language comprehension task (which served as a control),
subjects covertly judged whether there was anomalous content in any
premise or conclusion, with no need to judge the relationship between
statements (Fig. 1c). None of the arguments in the comprehension task
were valid.
The two arguments common to all three tasks in a matched set were
always invalid with no anomalous content (Fig. 1d). Only during performance with these common stimuli were subjects scanned. Identical stimuli
were therefore processed with different psychological ‘set’, eliciting
either deductive, probabilistic or semantic reasoning. Subjects were not
informed about the presence of identical arguments across different tasks,
and never remarked upon this fact spontaneously. Subjects were not
informed about the onset or offset of PET scanning, for which there was
no perceptible change in the immediate environment.
As noted, subjects were scanned during the deduction task only when
they were reasoning about invalid arguments. The reasoning mechanisms
engaged during scanning in the deduction task were likely to be the same
or similar to those involved in evaluating valid arguments. This is because
(i) subjects did not know when they would be scanned, (ii) scanned
arguments were surrounded by both valid and invalid arguments, and
(iii) subjects did not know prior to reasoning whether a given argument
was valid or invalid.
It is also worth emphasizing that a yes/no response was required
in the probability task, instead of a numerical judgment (see above).
Identical response options were thus available for the probability and
deduction tasks; all that differed was the kind of reasoning needed to
choose between them. (We elected not to use numerical responses in the
probability task because they would have introduced a confounding
asymmetry vis-à-vis deduction.) Observe as well that there were no
objectively correct answers to the probability questions. We decided not
to frame probability questions with objectively correct answers (e.g.
involving urns) because that would have converted them into disguised
deduction problems (e.g. requiring the calculation of posterior odds from
prior odds and likelihood). In contrast, the intuitions about chance at
issue in the present paper are based on subjective assessments that cannot be determined by logic alone. Subjective assessments are often the
focus of psychological research (Kahneman et al., 1982; Yates, 1990;
Johnson-Laird et al., 2001).
Our stimuli underwent extensive pilot testing to ensure that subjects
(i) could successfully perform the deduction tasks, (ii) felt intuitively that
the deduction and probability tasks required distinct kinds of reasoning,
(iii) could sustain reasoning to complete a judgment during the allotted
response time interval, and (iv) agreed with the experimenters and each
other about which arguments contained semantic anomaly. As a final
check, 23 Rice University undergraduates were asked to carry out all
nine tasks in front of a computer terminal, and then to answer follow-up
questions about their thought processes. The average accuracy rate for
the 15 deduction problems (which included eight valid arguments and
seven invalid arguments) was 86.4% (SD = 9.1). This is reliably better than
the 50% expected from mere guessing (P < 0.0001). There was significant concordance amongst the subject’s responses on the probability
trials (Kendall coefficient of concordance W = 0.11, χ2 = 36.2, P < 0.001).
Cerebral Cortex Oct 2001, V 11 N 10 955
Figure 1. Sample argument stimuli. (a) Two examples of valid arguments followed by
one example of an invalid argument, all used in the deduction task. (b) Three examples
of arguments used in the probabilistic reasoning task. (The first was constructed to elicit
a judgment of high likelihood for the conclusion; the second was constructed to elicit a
judgment of low likelihood; the third was intermediate.) (c) Two examples of arguments
with anomalous content followed by one without. (d) The six arguments that were
evaluated on three separate occasions, once for validity, once for high probability of the
conclusion, and once for anomaly.
Virtually all of the students spotted the anomalies introduced into the
arguments, with no false-positive responses.
The follow-up questionnaire included the query:
Did the logic and probability tasks differ only in how much time it took
you to decide? Or do you think that you used different reasoning in the
two tasks?
A ll 23 subjects said that ‘the two tasks required different reasoning’.
On the other hand, there was some role for deduction in probabilistic
reasoning. Thus, 12 students reported that ‘a considerable amount of
956 Distinct Brain Systems for Reasoning • Parsons and Osherson
my time on the probability questions was devoted to deduction’, and
11 reported that only ‘a small amount of my time on the probability
questions was devoted to deduction’. Thirteen of the students reported
that deductive reasoning was either ‘rather different’ or ‘very different’
from that for probability; 10 reported that the two tasks were ‘a little
different’; no one denied any difference at all.
The use of deduction in the probabilistic reasoning task was
expected: it is widely appreciated in the literature devoted to subjective
probability that deductive relations among propositions condition their
probabilities. [See presentations of the axioms for subjective probability
in, for example, Skyrms, p. 168 (Skyrms, 1986), or Earman, p. 36
(Earman, 1992).] Moreover, various psychological theories envision a role
for deduction in probabilistic contexts. This is true, for example, of Rips’
account of the psychology of deduction (Rips, 1994) (pp. 278ff.). Indeed,
Rips takes deduction to be the core of human cognitive architecture,
hence involved in most aspects of problem solving, planning and
categorization. It is therefore likely that the probability task recruited
elements of deductive reasoning.
Regarding the comprehension task, 19 out of the 23 subjects reported
thinking ‘about each statement by itself’, as requested in the instructions.
Four students admitted that they ‘tended to think about connections
between different statements’.
In summary, the design of our stimuli allows probabilistic and
deductive reasoning to be carried out over identical stimuli by changing
instructions. The resulting thought processes are intuitively distinct
although partially overlapping. The comprehension task requires merely
understanding the individual sentences of an argument, and elicits only a
slight tendency to link premises to conclusion.
Subjects
Ten healthy right-handed adults participated in the PET study after giving
written, informed consent. The five male and five female subjects ranged
from 23 to 43 years of age (means, 32 for the male group and 32 for the
female group). Subjects were screened to ensure no pre-experimental
training in formal logic.
Procedure
During the PET session, each subject performed three matched sets of
tasks (hence, nine tasks in all). In addition, they performed two replications of eyes-closed rest control. The three tasks in a given matched
set involved judgments of probability, deductive validity and semantic
well-formedness, as described above and illustrated in Figure 1. Before the
PET session, subjects received supervised practice in each task (using
stimuli not appearing in the experiment). During performance, subjects
lay supine in the PET instrument, with the head immobilized by a closely
fitted plastic facial mask. Stimuli were displayed on a 37.5 cm monitor
suspended 42.5 cm from the patient’s pupil. The first premise of each
problem was presented for 3 s, then joined by the second premise for
three more seconds, and then joined by the conclusion for 14 s (total
display/trial time, 20 s). At the conclusion of the PET session subjects
responded to a questionnaire eliciting introspections about the phenomenology of the three tasks.
For each task, the subject evaluated five arguments. During the third
and fourth arguments, brain blood f low was imaged. For each argument,
subjects were instructed to perform the task throughout its 20 s period;
they were to continue to double-check their judgment to ensure accuracy
if they finished before the stimuli were removed from view. The nine
tasks (each consisting of five arguments, of which the third and fourth
were scanned) plus the two rest conditions were administered in pseudorandom counterbalanced order such that (i) tasks from the same matched
set not occur in succession, and (ii) tasks 1–3 include one deduction,
one probability and one comprehension task, and similarly for tasks 5–7
and 9–11. Trials 4 and 8 were devoted to rest. Subjects reported their
judgments at the end of each task (hence, after all five arguments were
presented). As an aid to memory, the reports were made while the five
arguments in a given task were presented a second time.
Imaging
PET scans were performed on a GE 4096 camera which has a pixel
spacing of 2.0 mm, an inter-plane, center-to-center distance of 6.5 mm, 15
scan planes, and a z-axis field of view of 10 cm. Correction for radiation
attenuation was made by means of a transmission scan collected before
the first scan using a 68Ge/68Ga pin source. Cerebral blood f low was
measured with H215O (half-life = 123 s), administered as an intravenous
bolus of 8–10 ml of saline containing 60 mCi. At the start of a scanning
session, an intravenous cannula was inserted into the subject’s left
forearm for injection of each tracer bolus. A 30 s scan was triggered as
the radioactive tracer was detected in the field of view (the brain) by
increases in the coincidence-counting rate. During this scan, the subject
performed a task in one of the three conditions. Immediately following
this, a 60 s scan was acquired as the subject lay with his/her eyes closed
without performing a task. The latter rest PET images, in which taskrelated regional cerebral blood f low changes are still occurring in specific
brain areas, are combined with the task PET images in order to enhance
detection of relevant activations. Following the latter scan, subjects
performed one final (fifth) trial (without being scanned). A 10 min
inter-scan interval was sufficient for isotope decay (five half-lives) and
return to resting state levels of regional blood f low within activated
regions. Images were reconstructed using a Hann filter, resulting in
images with a spatial resolution of ∼7 mm [full-width at half-maximum
(FWHM)].
Significant changes in cerebral blood f low indicating neural activity
were detected using an ROI-free image subtraction strategy. High
resolution magnetic resonance imaging (MRI) scans were acquired for
each subject. Inter-scan, intra-subject movement was assessed and corrected using the Woods algorithm (Woods et al., 1993). Semi-automatic
registration of PET to matched, spatially normalized MRI (in Talairach
space) was performed using in-house spatial normalization software
implementing an algorithm employing a nine-parameter, affine transformation (Lancaster et al., 1995; Talairach and Tournoux, 1988).
The data were smoothed with an isotropic 3 × 3 × 3 Gaussian kernal to
yield a final image resolution of 8 mm. By use of moderate smoothing,
resolution is not sacrificed; control for false positives is provided by the P
value criteria used in later stages of the analysis stream employed here.
The data were then analyzed using the Fox et al. (Fox et al., 1988)
algorithm as implemented in the MIPS software package (Research
Imaging Center, UTHSCSA, San Antonio, TX). Intra-subject image averaging was performed within conditions and the resulting image data were
analyzed by an omnibus (whole-brain) test.
For this analysis, local extrema (positive and negative) were identified
within each image using a 3-D search algorithm (Mintun et al., 1989).
Each set of local extrema data was plotted as a frequency histogram (for
visual inspection). Then, a beta-2 statistic measuring kurtosis and a beta-1
statistic measuring skewness of the histogram of the difference images
[change distribution curve (Fox and Mintun, 1989)] were used as
omnibus tests to assess overall significance (D’Agostino et al., 1990).
Critical values for beta statistics were chosen at P < 0.01. The beta-1 and
beta-2 tests, which are implemented in the MIPS software in a manner
similar to the use of the gamma-1 and gamma-2 statistics (Fox and Mintun,
1989), improve on the gamma statistic by using a better estimate of the
degrees of freedom (Worsley et al., 1992).
The null hypothesis of omnibus significance was rejected, so a post
hoc (regional) test was done (Fox et al., 1988; Fox and Mintun, 1989).
In this algorithm, the pooled variance of all brain voxels is used as the
reference for computing significance. This method is distinct from
methods which compute the variance at each voxel, but is more sensitive
(Strother et al. 1997), particularly for small samples, than the voxel-wise
variance methods of Friston et al. (Friston et al. 1991) and others. In this
analysis, a maxima and minima search is conducted to identify local
extrema within a search volume of 125 mm3 (Mintun et al., 1989). Cluster
size was determined based on the number of significant, contiguous
voxels within the search cube of 125 mm3. The statistical parameter
images were converted to z-values by dividing each image voxel by the
average standard deviation of the null distribution. P values were assigned
from the Z distribution. Only Z values greater than 2.96 (P < 0.001)
are reported. The critical value threshold for regional effects (Z > 2.96,
P < 0.001) is not raised to correct for multiple comparisons (e.g. the
Cerebral Cortex Oct 2001, V 11 N 10 957
number of image resolution elements). This is because omnibus statistics
were established before post hoc analysis. The scanning methods used at
the Research Imaging Center have been described previously (Raichle et
al., 1983; Fox et al., 1985; Fox and Mintun, 1989; Lancaster et al., 1995).
Gross anatomical labels were applied to the detected local maxima
using a volume-occupancy-based, anatomical labeling strategy as implemented in the Talairach DaemonTM (Lancaster et al., 2000), except for
activations in cerebellum which were labeled manually with reference to
an atlas of the cerebellum (Schmahmann et al., 1999).
Anatomical MRI scans were performed on an Elscint 1.9 T Prestige
system. The scans employed 3D Gradient Recalled Acquisitions in the
Steady State (3D GR ASS), with a repetition time of 33 ms, an echo time of
12 ms, and a f lip angle of 60° to obtain a 256 × 256 × 256 volume of data
at a spatial resolution of 1 mm3.
Results
Our analysis of the brain blood f low data was designed to locate
the active regions that were common to the two kinds of
reasoning. In addition, it aimed to uncover the regions (if any)
that were distinctive to reasoning compared to linguistic processing. A third goal was to determine whether the distinction
between probabilistic and deductive reasoning interacted with
left versus right hemispheric processing. Finally, we sought
specific contrasts (if any existed) between the brain sites active
for probabilistic versus deductive reasoning. Following a discussion of these four topics, we describe three additional analyses.
Note that in the descriptions, figures and tables that follow
we have excluded spurious activations that resulted from
subtractions of deactivations. (In other words, all activations
were verified by subtracting the rest condition from the relevant
reasoning task.)
Activations Common to the Two Kinds of Reasoning
We found that many brain areas were significantly activated
when the comprehension control task was subtracted from
the probabilistic and deductive reasoning tasks (P < 0.001).
However, only 8% of these areas were active in both contrasts
despite the fact that the two reasoning tasks involved identical
stimuli. The common, overlapping activation is in precuneus or
posterior cingulate cortex [Brodmann area (BA) 31]. On an
alternative analysis, in which the comprehension control task is
subtracted from the average of the two reasoning tasks, again
only the latter area is active. The lack of overlap in significant
activity detected for the two tasks suggests that probabilistic and
deductive reasoning were performed via different underlying
neural mechanisms. This inference is consistent with subjects’
introspective reports suggesting that different cognitive processes were used in the two tasks (see Materials and Methods).
Reasoning versus Language Processing
In the next analysis, we focus on the possible contribution of
language processing areas to the activations elicited in the
reasoning tasks. When the comprehension task is subtracted
from the probability task, there is no activity in or near Broca’s
area (in BA 44/45), sub-Broca’s area (in BA 47), or Wernicke’s
area (in BA 22/21). These three areas are known to contain
primary left-hemispheric language sites (Petersen et al., 1989;
Mazoyer et al., 1993; Stromswold et al., 1996; Price, 1998).
Likewise, none of these areas show activity when comprehension is subtracted from the deduction task. Apparently, the
purely linguistic load of the two reasoning tasks does not exceed
what is necessary to spot semantic anomaly among the premises
and conclusion of an argument. The foregoing contrasts with
comprehension argue against interpreting the reasoning activations as ‘spillover’ from overloaded language areas, a phenomenon that appears to occur with the comprehension of sentences
of increasing complexity (Just et al., 1996). We return to this
issue in the Discussion.
Hemispheric Interaction with Type of Reasoning
We next sought to characterize the difference in the location of
regions activated in the two reasoning tasks and found a relationship between task and cerebral lateralization. All activations
discussed in this analysis were present in both (i) the direct
contrast between probability and deduction (either probability
subtracted from deduction or the reverse) and (ii) the subtraction of the comprehension task from the reasoning task in
question.
When the probability task is subtracted from the deduction
(or ‘logic’) task, 65% of all significantly activated voxels in the
brain (excluding cerebellum) (P < 0.001) were in the right
hemisphere. This contrast also showed lesser activations in left
cerebral hemisphere but only in visual areas (BA 18, 31 and 39)
and certain subcortical structures. To confirm the validity of this
relationship, we conducted the following alternative analyses.
We found the same pattern even when the threshold for
statistical significance for the contrast is considerably relaxed
(P < 0.05). The pattern also is present when a logical image
analysis (in which every voxel is categorized as unique to one
condition or the other, or common to both) is applied both to
deduction minus rest and to comprehension minus rest, using
a relaxed threshold of statistical significance (P < 0.05). The
outcomes of these alternative analyses indicate that we are
unlikely to have failed to detect activation in language areas that
could be associated with deduction.
Continuing our examination of lateralization and reasoning,
when deduction is subtracted from probability, 59% of all
significantly activated voxels in the brain (excluding cerebellum)
(P < 0.001) were in the left hemisphere. The activation in the
right cerebral hemisphere was again only in areas likely to be
involved in the control of attention and in some subcortical
structures. The same pattern appears even when the threshold
for statistical significance for this contrast is relaxed (P < 0.05).
The pattern also holds in the logical image analysis of probabilistic reasoning minus rest, and comprehension control minus
rest, with a relaxed statistical threshold (P < 0.05).
To further confirm the foregoing pattern of dissociated
activation and hemispheric specialization for deductive versus
probabilistic reasoning, we conducted an additional analysis. In
a logical image analysis of both probabilistic reasoning minus
rest and deduction minus rest, even when a relaxed threshold of
statistical significance is employed (P < 0.05), we once again
found the same interaction of task versus hemisphere.
Figures 2–8. Grand mean PET–rCBF increases (P < 0.001) for deduction minus probabilistic reasoning (in green–blue Z-score scale) and probabilistic reasoning minus deduction (in
yellow–red Z-score scale) overlaid onto subjects’ mean anatomical MR images (greyscale) in coronal planes.
Figure 2. Deduction-specific rCBF increases in right middle temporal gyrus (BA 21, arrow).
Figure 3. Deduction-specific rCBF increases in right inferior frontal cortex (BA 44) and probability-task-specific rCBF increases in left inferior frontal cortex (BA 47).
958 Distinct Brain Systems for Reasoning • Parsons and Osherson
Figure 4. Deduction-specific rCBF increases in right basal ganglia (caudate nucleus, arrow).
Figure 5. Deduction-specific rCBF increases in right amygdala; there were no probability-specific activations in amygdala.
Figure 6. Probability-task specific rCBF increases in left inferior frontal cortex (BA 47).
Figure 7. Probability-task specific rCBF increases in left posterior cingulate cortex (BA 31).
Figure 8. Probability-task specific rCBF increases in left parahippocampal cortex (BA 36).
Cerebral Cortex Oct 2001, V 11 N 10 959
Table 1
Local maxima in regions demonstrating significant rCBF increases (P < 0.001) during deductive
and probabilistic reasoning
Gyrus or regiona
Brodmann Talairach coordinatesc
areab
x
y
z
Deduction minus probabilistic reasoning
Right hemisphere
Middle temporal
21
Anterior cingulate
24
Inferior frontal
44
Caudate
Amygdala
Thalamus
Midbrain
Temporoparietal
39
Fusiform
37
Anterior cingulate
24
Middle frontal
9
Cuneus
18
Medial frontal
10
Cerebellum
Superior semilunar (crus I)
Gracile (VIIB)
Left hemisphere
Midbrain
Thalamus
Lingual
18
Lateral globus pallidus
Thalamus
Sub-gyral
39
Posterior cingulate
31
Cerebellum
Gracile (VIIB)
Posterior quadrangle (VI)
Probabilistic reasoning minus deduction
Right hemisphere
Anterior cingulate
24
Anterior cingulate
24
Globus pallidus
Uncus
28
Cerebellum
Superior semilunar (crus I)
Inferior semilunar (crus II)
Left hemisphere
Inferior frontal
47
Insula
Posterior cingulate
31
Parahippocampal
36
Medial frontal
9
Inferior frontal
47
Sub-gyral
35
Paracentral lobule
5
Midbrain
Sub-gyral
6
Cerebellum
Posterior quadrangle (VI)
Inferior semilunar (crus II)
Gracile (VIIB)
Extent
(mm3)
Z-score
51
18
53
4
17
16
8
34
49
18
36
6
2
–27
–7
16
10
–9
–15
–27
–69
–45
–15
41
–92
51
–9
37
17
4
–15
9
1
21
–11
37
24
16
7
312
120
96
72
64
32
32
24
16
16
16
8
8
4.89
3.99
3.44
3.64
3.49
3.64
3.39
3.69
3.49
3.24
3.19
3.14
3.04
32
32
–64
–47
–34
–19
136
32
3.94
3.99
–4
–10
–7
–15
–13
–32
–1
–37
–9
–82
4
–17
–53
–41
–18
2
–2
5
1
11
40
216
112
32
24
24
16
8
5.04
3.74
3.69
3.49
3.24
3.29
3.29
–23
–20
–66
–54
–33
–14
104
40
3.59
3.39
9
5
13
22
–13
23
–2
8
39
20
4
–20
144
104
88
72
3.61
3.66
4.11
3.86
23
45
–72
–50
–21
–29
56
24
3.46
3.26
–26
–32
–5
–26
–6
–26
–35
–10
–9
–34
29
20
–47
–43
47
15
–9
–30
–25
–11
–7
7
29
–5
35
–18
–24
50
–11
33
152
144
136
80
24
16
16
8
8
8
3.61
3.81
4.31
3.96
3.31
3.26
3.21
3.26
3.21
3.11
–12
–40
–36
–56
–44
–62
–15
–25
–34
32
24
8
3.56
3.51
3.11
a
In parenthesis is the equivalent anatomical label of Schmahmann et al., based on that of Larsell
and Jansen (Larsell and Jansen, 1972; Schmahmann et al., 1999).
b
Designation of Brodmann areas is approximate and based on a brain atlas.
c
Brain atlas coordinates (Talairach and Tournoux, 1998) are in millimeters along left–right (x),
anterior–posterior (y) and superior–inferior (z) axes.
Specific Sites for the Two Kinds of Reasoning
We now turn to direct contrasts between the two reasoning
tasks. Once again, all activations in the present analysis were
detected in both (i) the direct contrast between probability and
deduction and (ii) the subtraction of the comprehension task
from the reasoning task in question.
960 Distinct Brain Systems for Reasoning • Parsons and Osherson
Deduction Minus Probability
In the contrast subtracting probabilistic from deductive
reasoning (P < 0.001), the largest activation by far was in right
middle temporal cortex (BA 21) (Fig. 2, Table 1). This activation
(51, –27, –9) is just below a region homologous to one of the
principal language areas of the left hemisphere (Wernicke’s
area). Although the exact boundaries of Wernicke’s area are
unknown, tasks thought to activate the region elicit peak
intensity responses near a region homologous to that activated
here. For example, Wernicke’s area has been reported to be at
(–48, –32, 6) in a recent report of a study of verb generation
(Xiong et al., 2000), and to be at (–54, –41, 8) in a meta-analysis
of four papers on word reading (Fiez and Petersen, 1998).
Another major focus was detected in right inferior frontal
gyrus (BA 44) (Fig. 3). This activation (53, 16, 17) is adjacent to
a region homologous to the other principal left language area
(Broca’s area). Again, although the precise boundaries of Broca’s
area are unknown, tasks which appear to activate it produce
peak intensity responses near a region homologous to that
activated here. Thus, in the average peak activation of 10 studies
of language production tasks, Broca’s area is at (–44, 10, 13)
(Petrides et al., 1993, 1995; Becker et al., 1994; Price et al.,
1994; Bookheimer et al., 1995; Buckner et al., 1995; Hirano et
al., 1996, 1997; Braun et al., 1997; Petersen et al., 1988).
Two other activated foci of moderate cluster size and intensity
in this contrast were in right caudate nucleus (Fig. 4) and right
amygdala (Fig. 5). Other activations seen in the subtraction of
probability from logic were generally smaller in size and intensity. They include foci in right dorsolateral and medial prefrontal
cortex (BA 9, 10), right anterior cingulate cortex (BA 24), and
right temporoparietal cortex (BA 39). Other right hemisphere
activation were observed in thalamus, fusiform cortex (BA 37),
midbrain and occipital cortex (BA 18). There were mostly small
left hemisphere foci in midbrain, thalamus, posterior cingulate
(BA 31), globus pallidus, temporoparietal (BA 39) and occipital
cortex (BA 18). The majority of the other activations are
associated with attentional or visual functions. Lastly, there were
moderate bilateral posterior cerebellar foci.
Probability Minus Deduction
A different picture emerges of the areas specific to probabilistic
reasoning. When deductive reasoning is subtracted from probabilistic reasoning, there are a number of large and intense
activations in the left hemisphere areas of inferior frontal (BA 47)
(Fig. 3 and 6, Table 1) and insular cortex, as well as posterior
cingulate (BA 31) (Fig. 7), parahippocampal (BA 36) (Fig. 8),
medial temporal (BA 35), and superior and medial prefrontal
(BA 9) cortex.
Subtraction of logic from probability also revealed left hemispheric foci of moderate cluster size and intensity in subgyral
lateral frontal (BA 6) and temporal (BA 35) cortex, midbrain
and paracentral cortex (BA 5). The same contrast revealed right
hemisphere foci, mainly in anterior cingulate (BA 24), globus
pallidus and uncus (BA 28). Most of these areas are related
to attentional or visual functions. Again, there were bilateral
posterior cerebellar foci.
Comparison of Male and Female Reasoners
We also examined for the first time the possibility that there are
differences in the neural systems supporting reasoning for male
and females and found no significant differences in activation
patterns between the groups. Both show the same degree of
right hemispheric prevalence for deduction and left for probability. It must be borne in mind, however, that this comparison
is based on a sample size that is relatively small for comparisons
of PET data (n = 5 in each group).
Overall Activation and Task Difficulty
In order to further quantify the differences between deductive
and probabilistic reasoning, we examined the amount of overall
activity for each task. In total, there was a third more logicspecific activation foci than probability-specific ones in the
direct contrasts between deduction and probability. The greater
extent of deductive activations is unlikely to result from more
vigorous use of the system underlying deduction compared to
probability because eight of the 10 subjects judged probability
to be the most difficult of the three tasks in a post-experimental
questionnaire. (Likewise, 17 out of 23 subjects in our pilot study
judged probability to the most difficult of the three tasks.)
Measures of Task Performance
Overall accuracy on the deduction problems, assessed against
the criterion of standard logic, was 72% (69% for the scanned
trials), which is comparable to logical performance without time
stress (Rips, 1994), and is reliably better than chance (P < 0.001,
binomial test). Regarding the probability task, across all trials the
subjects judged 41% of the conclusions to be more likely than
not (45% for the scanned trials). They also agreed with the
experimenters’ judgement 72% of the time. There is no objective
standard of accuracy for the probability task since the laws of
probability do not impose any particular value for the arguments
we used. Thus, the best measure of the quality of the stimuli is
the extent to which the subjects agreed with one another. The
subjects’ judgements were indeed concordant (Kendall
coefficient of concordance W = 0.349, χ2 = 39.1, d.f. = 14, P <
0.001). Responses to the semantic comprehension test were
virtually perfect.
Discussion
In the following discussion, we offer preliminary hypotheses
about the neurobiology of deductive and probabilistic reasoning.
To underline the provisional nature of our conclusions, let us
affirm at the outset that the present results do not allow us to
infer with certainty either the specific function of the activated
areas, or even whether particular activations are essential to
reasoning or just incidental. Compounding the ambiguity is the
paucity of prior neurological or neuroimaging data about inferential reasoning (limited to the few, highly suggestive papers
cited in the Introduction). Further studies will thus be required
to assess the hypotheses we now present.
Dissociated Activations for the Two Types of Reasoning
A variety of brain areas were activated by the deduction and
probability tasks. A few were active for both kinds of reasoning
but many more were active uniquely for one or the other. Our
results thus give evidence for a dissociation between the brain
areas specifically activated for deductive versus probabilistic
reasoning with propositional arguments. Although no single set
of control conditions or contrasts is perfect in all respects, the
same deduction-specific and probability-specific areas were
indicated in all three possible contrasts, namely, each reasoning
task minus rest, each task minus the language comprehension
control, and in direct contrasts between the two reasoning tasks.
Moreover, the activation common to deductive and probabilistic
reasoning is largely restricted to precuneus and posterior
cingulate cortex (BA 31). While the function of the latter areas is
still under investigation, some studies suggest its involvement in
aspects of attentional processing (Petrides et al., 1993; Shallice
et al., 1994). If so, then the brain regions activated by both
reasoning tasks may represent no more than similar attentional
demands.
The dissociation we observed supports psychological theories
that enforce a partial separation between the two reasoning processes (Braine, 1978). By the same token, the findings suggest
that it is inaccurate from the point of view of functional
neuroanatomy to claim that humans judge logical truth as a
limiting case of probability assessment, i.e. that they use the
same cognitive processes for deduction and probabilistic reasoning, as has been suggested (Johnson-Laird, 1994; Johnson-Laird
et al., 2001). We now offer further remarks about each form of
reasoning.
Probabilistic Reasoning
Recall that in probabilistic reasoning, assessing the likelihood of
the conclusion requires integrating information that goes beyond what is available in the premises (otherwise, the reasoning
would bear on deductive validity instead of probability). Consistent with this requirement, probabilistic reasoning activated
a set of brain areas that appear to be involved in recalling and
evaluating a range of world knowledge. For example, there was
strong activation in left inferior frontal areas, which have been
implicated in the retrieval of semantic information as well as the
use of working memory (Demb et al., 1995; Petrides et al., 1995;
Rushworth et al., 1997; D’Esposito et al., 1998). The posterior
cingulate was also activated during the probability task. The
function of the posterior cingulate is still debated but it has
been associated with either attention or long-term episodic
memory (Grasby et al., 1993; Petrides et al., 1993; Price et al.,
1994; Shallice et al., 1994). In addition, probabilistic reasoning
activated parahippocampal and medial temporal areas. These
regions have been convincingly associated with declarative,
semantic memory (Damasio et al., 1996; Brewer et al., 1998;
Wagner et al., 1998). Finally, responses were observed in medial
prefrontal cortex, which may be involved in executive attention
(Petrides et al., 1993; Baker et al., 1996; Prabhakaran et al.,
1997; Waltz et al., 1999).
Deduction
In contrast to probabilistic reasoning, deduction does not
depend on general knowledge, but only on recognition and use
of the logical structure spanning premises and conclusion. Our
PET data indicated that, in distinction to probabilistic reasoning,
deductive inference primarily activated a set of right brain
areas, the major ones being proximal to homologues of the left
hemisphere structures responsible for language processing (i.e.
Wernicke’s area and Broca’s area). No such areas in right hemisphere were active for probabilistic reasoning.
To date, few functions have been attributed to the two
principal regions we observed for deduction. All have involved
higher-order linguistic tasks such as maintenance of thematic
coherence (St George et al., 1999), discourse management
(Beeman and Chiarello, 1998), and interpretation of context
(Bottini et al., 1994; Just et al., 1996; Shammi and Stuss, 1999).
We believe that the right brain areas specifically active for
deduction are distinct from those supporting such higher-order
language functions. This is because thematic coherence and
discourse management are likely to be equally required for evaluating identical arguments under probabilistic versus deductive
instructions. Yet the foci observed in those areas were present
only for deduction. We therefore suggest that the activated
areas support logical reasoning. This suggestion is consistent
with reports that right frontal patients have specific difficulties
Cerebral Cortex Oct 2001, V 11 N 10 961
making inferences and interpreting propositions joined or modified by logical connectives (Grossman and Haberman, 1987;
Beeman and Chiarello, 1998).
Deduction also specifically produced an activation of moderate intensity and cluster size in the right caudate nucleus.
Basal ganglia (e.g. caudate) connect through thalamus (also
activated) to frontal cortex, and may mediate working memory,
executive strategy and rule-based learning (A lexander et al.,
1990; Middleton and Strick, 1991; Gabrieli, 1995; Rao et al.,
1997; Poldrack et al., 1999). It has been suggested that left basal
ganglia support left hemisphere grammatical rule processing
(Damasio and Damasio, 1992; Ullman et al., 1997). If so, we may
speculate that right basal ganglia support rule-based deduction
in right frontal areas. This interpretation is consistent with the
obser vation that Parkinson’s disease patients (who have dysfunctional basal ganglia) have specific difficulties making logical
inferences (Natsopoulos et al., 1997).
Thus, the involvement of the right basal ganglia, in conjunction with right brain areas in or near those homologous to
Wernicke’s and Broca’s areas, is consistent with the possibility
that there is a logic-specific network that in some respects
mirrors the language-specific network. In other words, deduction may be supported by a system of structures in the right
hemisphere that is analogous to the structures supporting
language in the left. Each system would allow for the successive
transformation of mental representations according to encoded
rules (linguistic rules on the left, deductive rules on the right).
Deduction also produced a major focus of activation in right
amygdala. The amygdala has been implicated in the processing
of emotion, most often fear, aggression, reward and risk (Hyman,
1998; LaBar et al., 1998; Kahn et al., 2000). In particular, it may
play a role in learning to avoid neutral stimuli that are paired with
aversive events (Sananes and David, 1992; Romanski et al., 1993;
Cahill et al., 2001). The amygdala is suspected more generally
of emotionally tagging learned associations (McGaugh et al.,
1996), for example, recognizing the value of stimuli that predict
positive reinforcers (Cador et al., 1989; Everitt et al., 1989).
The activation of the right amygdala (but not the left) in the
deduction minus probability contrast suggests its connection
to deductive reasoning, which activated predominantly right
hemisphere structures. We speculate that an emotional basis
for such activation is provided by the ‘A ha!’ phenomenology
reported for the deduction task in post-experimental interviews.
Indeed, 70% of our PET subjects noted sudden insight during the
deduction task whereas 80% described probabilistic reasoning as
involving the gradual stabilization of judgement. Consistent with
the lack of an ‘Aha!’ during probabilistic reasoning, no activation
was detected in either the left or right amygdala in the subtraction of deduction from probability. These observations reinforce
the hypothesis that the two kinds of reasoning are fundamentally
different. Deductive reasoning might thus involve a pleasurable
release from tension as the subject suddenly perceives the logical
status of an argument [as in other problem-solving settings
(Davidson, 1995)]. Such an interpretation is consistent with
studies that suggest a role of such structures as amygdala in
interactions between emotion and higher cognitive processes
like decision making (Damasio, 1994). Of course, the introspective ‘Aha’ is not guaranteed to signal a significant brain event.
Its correlation with deductive (but not probabilistic) reasoning
and with right (but not left) amygdala activation nonetheless
makes it plausible that amygdala activation can be triggered by
sudden insight achieved during deduction.
In addition, deduction activated medial and dorsolateral
prefrontal cortex. These areas (which are still being vigorously
962 Distinct Brain Systems for Reasoning • Parsons and Osherson
explored with a variety of methods) have been associated with
executive attention and strategy functions, including controlling
or monitoring the contents of working memory (Petrides et al.,
1993; Posner and Dehaene, 1995; Baker et al., 1996; Fiez et al.,
1996). Moreover, there were deduction-specific responses in
temporoparietal and anterior cingulate areas, which have been
associated with selective and sustained attention and with
response selection (Corbetta et al., 1995; McCarthy, 1996).
There was no significant activation for deductive reasoning in
right hemispheric areas associated with visual–spatial processing, i.e. posterior parietal (BA 40) and lateral prefrontal (BA 46)
cortex (Kosslyn, 1994; Parsons and Fox, 1998; Carpenter et
al., 1999; Dehaene et al., 1999; Farah, 1999). This absence of
activation is consistent with the fact that 80% of the PET subjects
indicated on the post-experimental questionnaire that they did
not generate visual diagrammatic representations of the stimulus
information when performing the deductive problems. The use
of stimuli from propositional logic seems therefore to have
served its intended purpose of limiting recourse to diagrammatic
strategies when reasoning deductively.
Finally, there were bilateral posterior cerebellar foci during
deduction that were different from the bilateral posterior cerebellar foci seen during probabilistic reasoning. In the absence
of overt motor behavior, apart from eye movements, these
activations in neocerebellum are likely related to support processes for sensory or cognitive activity during the reasoning
tasks (Akshoomooff et al., 1997; Hallett and Grafman, 1997;
Parsons and Fox, 1997; Desmond and Fiez, 1998).
Deduction Across Individuals and Logical Forms
Our results held equally for men and women. Gender invariance is noteworthy in view of possible differences in cerebral
organization and lateralization between the sexes (Gur et al.,
1999; Kimura, 1999).
The findings reported here are consistent with our previous
study of men using syllogisms (Osherson et al., 1998). Both
studies showed right hemispheric dominance for deduction, left
for probabilistic reasoning. The specific right hemispheric areas
activated for deduction were more anterior in the present study,
however, perhaps ref lecting the predominantly non-spatial
reasoning elicited by propositional stimuli. The syllogistic
stimuli in Osherson et al. seemed to recruit more visuospatial
reasoning, ser ved by posterior parietal–occipital regions
(Osherson et al., 1998). Unlike studies by Goel et al. (Goel et al.,
1997, 1998), neither of our studies revealed deduction-specific
activations in left frontal and left temporal brain areas, just as
neither study by Goel et al. documented the deduction-specific
right hemisphere activations that we have found. Further experiments are required to clarify the reason for these discrepancies,
but one important factor might be the use of distinct stimuli for
probabilistic and deductive reasoning in the Goel et al. studies,
unlike the strictly identical stimuli employed here. Use of distinct
stimuli for the two tasks leaves open the possibility that
activation differences are partly driven by differences in stimuli
rather than reasoning.
Deduction and Language Processing
We now examine the implications of our data for the relation
between reasoning and language. To begin, it is worth
considering an interpretation of the results that is alternative to
the functional hypotheses discussed above. It assumes that
comprehension and deduction are similar processes with
common neural bases, and that the greater activation in the right
hemisphere during deduction ref lects the additional memory
load of the latter. A straightforward version of this ‘spillover’
hypothesis is that deductive reasoning loads left hemisphere
language areas beyond capacity, so other support areas (e.g.
right hemispheric ones) are recruited to carry out aspects of the
task [see Just et al. for evidence of such spillover in a different
linguistic context (Just et al., 1996)].
The hypothesis of spillover is contradicted, however, by the
fact that no activations are seen (even at a relaxed statistical
threshold) in left language areas when the comprehension
control task is subtracted from deduction. The latter contrast
involves both location and intensity, and indicates that no excess
intensity of activity is observed in left language areas beyond
what is required to spot anomalous semantic content during the
control task. In other words, the spillover hypothesis predicts at
least as much activation in the primary language areas as in the
right hemispheric regions that are recruited during overloading,
yet, only the right hemisphere regions appear when the comprehension task is subtracted from deduction. It is also of interest
that the comprehension task was consistently rated as easiest by
our subjects. Hence, spillover during deduction would not be
expected to allow left hemisphere activations to be erased by
subtraction of the activations for the comprehension task.
On the basis of our data, we are led to conclude that deductive
reasoning is localized in brain areas far from the principal language centers in left hemisphere. This raises the possibility that
logical competence is largely independent of natural language
processing, except for statement decoding. Although there is
evidence that right hemisphere areas are involved in many
higher-order language functions, as discussed earlier, the
primary locus of linguistic analysis is left hemispheric (Petersen
et al., 1989; Mazoyer et al., 1993; Stromswold et al., 1996; Price,
1998). Our findings thus contradict the belief often expressed in
the cognitive sciences and philosophy that deductive reasoning
is derivative to linguistic processing (Quine, 1970; Polk and
Newell, 1995). This belief is sustained by the plausible thesis
that logic licenses inferential relations among statements, and
that statements must be embedded in a structured language if
they are to have the kind of grammatical properties that permit
deduction [such properties as being conditional in form, having
quantifiers with determinate scope, etc.; see Fodor (Fodor,
1975)].
The language that supports deduction, however, need not be
a natural language like English, burdened as it is by ambiguity
and ellipsis. Deduction (as well as other forms of reasoning)
might rather be performed in a format that is antecedent to
natural language, the latter being acquired for the purpose of
expressing meanings that exist prior to their linguistic expression. If logic and language are independent in this sense, then
logic might be available to prelinguistic infants and other animal
species – a prediction that is still without adequate test, in our
opinion. [Consistent with our hypothesis, the presence of complex reasoning in a profoundly aphasic patient with extensive
lesions in left hemisphere language areas is reported in Varley
and Siegal (Varley and Siegal, 2000).]
Deduction Without Visualization
The brain activations seen here for deductive reasoning were
largely outside regions known to serve spatial visualization. This
finding suggests that at least some forms of logical thinking
are non-diagrammatic, as well as being independent of natural
language processing. Albeit non-linguistic, such forms of logical
thinking may nonetheless be rule-based, and rely on a calculus of
transformations carried out via right hemisphere mechanisms
over formal structures retrieved by left hemisphere language
areas. Such formal structures would be relatively coarse representations of the sentences from which they are abstracted since
only the logical particles (and, if, etc.) need to be registered,
along with facts about the identity and distinctness of phrases
occurring across premises and conclusion.
Conclusion
We conclude by summarizing the working hypotheses that
emerge from our data. We postulate the existence of a logicspecific network in the right hemisphere comparable to the
language-specific network in the left. Both involve temporal,
frontal and basal ganglia structures. Just as linguistic rules are
encoded in the left hemisphere, deductive rules are encoded in
the right. According to our hypothesis, each system allows for
the successive transformation of mental representations specific
to its function. The two circuits interact when the transformations for deduction are carried out via right hemisphere
mechanisms over formal structures retrieved by left hemisphere
language areas. The latter structures would be coarse representations of the sentences from which they are abstracted since
only their logical structure would be retained.
We hypothesize that probabilistic judgement is achieved via
non-linguistic left hemisphere areas that are involved in the
recall and evaluation of world knowledge. Note that in contrast
to the reliance of deductive reasoning on coarse linguistic
representations, probabilistic judgement must rely on the fine
detail of sentences, since ever y word contributes to overall
plausibility. Our hypotheses are thus consistent with the
conjecture that right hemisphere regions are specialized for
processing relatively coarse aspects of stimuli whereas left
hemisphere regions are favored for fine aspects. A variety of
empirical findings support this broader conjecture [see Ivry and
Robertson for a review of the evidence (Ivry and Robertson,
1998)].
Notes
Supported by grants from R ice University and the Research Imaging
Center. We thank Mario Liotti and three anonymous reviewers for
insightful and ver y helpful comments on earlier versions of the
manuscript. We also thank Michael J. Martinez for assistance with the
conduct and analysis of the study.
Address correspondence to Lawrence M. Parsons, Director, Cognitive
Neuroscience Program, Division of Behavioral and Cognitive Sciences,
Directorate for Social, Behavioral, and Economic Sciences, National
Science Foundation, 4201 Wilson Boulevard, Arlington, VA 22230, USA.
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