Logic, Language and the Brain

Introduction
The relation between language and other kinds of higher cognition is
a central question in Cognitive Science (Gleitman and Papafragou, 2005;
Levinson, 2003; Spelke and Tsivkin, 2001). The issue has been confronted
in a variety of domains, including mental arithmetic (Dehaene et al., 1999;
Varley et al., 2005), music (Brown et al., 2006), communicative competence
(Willems and Varley, 2010) and theory of mind (Varley and Siegal, 2000).
The present paper reviews the issue with respect to deductive reasoning.
For the purposes of the following review, our comparison of language
and deductive inference will rely on the thesis that the principal elements
Corresponding author
Email addresses: [e-mail] (Martin M. Monti),
[e-mail] (Daniel N. Osherson)
Preprint submitted to Brain Research May 5, 2011
of human linguistic capacity are embodied in structures proximal to the left
sylvian ssure (including inferior frontal and posterior temporal cortices; c.f.,
Monti et al., 2009). Indeed, while many aspects of communicative compe-
tence have been shown to rely on areas that fall outside of this region (Moro
et al., 2001; Rodd et al., 2005; Ullman, 2001), most neuroimaging studies
aimed at localizing the central components of language comprehension con-
sistently recruit perisylvian areas. These studies include evaluating semantic
equivalence of distinct sentences (Dapretto and Bookheimer, 1999), morpho-
logical processing (Sahin et al., 2006), detecting semantic roles (Bornkessel
et al., 2005), transforming sentence syntax (Ben-Shachar et al., 2003), and
comprehending discourse (Kuperberg et al., 2006; Virtue et al., 2006). (See
Willems and Varley, 2010 for a recent review comparing the neural substrate
of communicative versus linguistic capabilities.)
 

Background
Deductive reasoning is the attempt to draw secure conclusions from prior
beliefs, observations and suppositions. It has been the focus of vigorous
investigation within philosophy and psychology (e.g., Beall and van Fraassen,
2003; Johnson-Laird and Byrne, 1991; Osherson, 1975). While deduction is
often regarded as a central feature of human intelligence (Rips, 1994), some
forms of deduction (notably, transitive inference) have been reported in non-
human primates (e.g., Gillian, 1981), rats (e.g., Davis, 1992; Roberts and
Phelps, 1994), birds (e.g., Steirn et al., 1995; Bond et al., 2003) and sh
(Grosenick et al., 2007). In this report our focus will be deductive reasoning
in humans and its neural implementation in the adult brain.
Evidence regarding the neural organization of deductive reasoning ini-
tially relied on studies of neurological patients with focal brain lesions. This
literature suggests the involvement of lateral frontal and pre-frontal regions
in deduction, along with various temporal and parietal areas (Grossman
and Haberman, 1987; Langdon and Warrington, 2000; Stuss and Alexander,
2000). Clinical studies, however, have been hindered by task heterogeneity,
lack of precise localization, and limited replicability of ndings (Shuren and
Grafman, 2002). Only more recently have lesion studies allowed more precise
localization and de nition of the contribution of di erent brain regions to hu-
man reasoning (e.g., Goel et al., 2004, 2007; Reverberi et al., 2009b). Since
the advent of non-invasive neuroimaging techniques (e.g., positron emission
tomography, PET, and functional magnetic resonance imaging, fMRI) the
study of deduction in the healthy brain has illuminated a range of issues. De-
ductive reasoning has been compared to probabilistic inference (Goel et al.,
1997; Goel and Dolan, 2004; Parsons and Osherson, 2001) and grammatical
transformations (Monti et al., 2009), as well as to mathematical operations
(Kroger et al., 2008). Diverse logical domains have also been investigated.
Di erent studies have focussed on deontic and social reasoning (Canessa
et al., 2005; Fiddick et al., 2005), propositional arguments (Monti et al.,
2007; Reverberi et al., 2007) along with categorical (Osherson et al., 1998;
Parsons and Osherson, 2001; Rodriguez-Moreno and Hirsch, 2009) and rela-
tional syllogisms (Knau et al., 2002, 2003; Fangmeier et al., 2006). Other
work has investigated the impact of familiar and unfamiliar information (Goel
et al., 2000; Goel and Dolan, 2001; Noveck et al., 2004), prior beliefs (Goel
and Dolan, 2003a), and emotional content (Goel and Dolan, 2003b) on the
neural implementation of deductive inference. Further investigations have
addressed the role in deduction of visual imagery (Knau et al., 2002, 2003),
spatial representation of propositions (Prado et al., 2010b), and alternative
modalities of stimulus presentation (Fangmeier and Knau , 2009; Rodriguez-
Moreno and Hirsch, 2009). But despite many important ndings, a decade
of neuroimaging has not produced agreement on which neural areas support
deductive reasoning. Consensus is also lacking about the role in deduction of
regions traditionally associated with linguistic competence. Indeed, until re-
cently, no two studies have exhibited similar patterns of activation across the
brain. As an example, the study of relational syllogisms has been reported
to recruit inferior frontal linguistic regions in the absence of any parietal
activation in one study (Goel et al., 1998), and bilateral parietal regions
in the absence of any inferior frontal linguistic activation in a di erent one
(Goel and Dolan, 2001). Overall, as pointed out in a recent review, all stud-
ies \implicate some combination of occipital, parietal, temporal and frontal
lobes, basal ganglia, and cerebellar regions in logical reasoning, and several
implicate all of these regions" (Goel, 2007, p. 438).
1.2. Sources of variance: Modes of reasoning and experimental design
The observed variance across studies may have several sources (c.f., Monti
et al., 2007; Reverberi et al., 2007; Rodriguez-Moreno and Hirsch, 2009). For
one thing, deductive reasoning should be regarded as a collection of processes
and representations that are di erentially triggered by speci c features of a
reasoning problem (e.g., di erent logical domains such as quanti ers versus
sentential connectives). Deduction also interacts with other cognitive systems
(e.g., for resolving cognitive con
ict when premises are known to be false).
We review these issues in the remainder of the present section.
 

Deductive domains
Deductive reasoning is often treated as a unitary process governing se-
cure inference across di erent types of logic. It is possible, however, that
propositional problems (based on connectives like \if . . . then . . . ", \and",
\not", Monti et al., 2007; Noveck et al., 2004), recruit di erent mecha-
nisms than those serving categorical syllogisms (based on quanti ers such as
\all", \none", \some", Goel et al., 1998, 2000; Rodriguez-Moreno and Hirsch,
2009). Yet other mechanisms may be involved in relational syllogisms (in-
volving relational terms like \. . . is taller than . . . ", Goel and Dolan, 2001;
Knau et al., 2003; Prado et al., 2010b). To illustrate the impact of these
distinctions on brain activations, relational problems may recruit posterior
parietal regions (e.g., Goel and Dolan, 2001; Knau et al., 2003) that are
known to underlie the kind of spatial representation evoked by transitive re-
lations like \taller than" (Kosslyn et al., 1995; Prado et al., 2010b). Such
regions would not be expected for connectives, which seem devoid of spa-
tial interpretation. Di erences between deductive domains have been noted
previously (Goel, 2007), but mostly on the basis of cross-study comparisons.
Only recently has the issue been addressed directly by comparing the neural
basis of alternative domains within the same participants executing the same
experimental task (Prado et al., 2010a; Reverberi et al., 2010).
 

 Stimuli
Another source of variability across studies stems from the specific fea-
tures of the stimuli employed. For example, some experiments employ \con-
tent free" stimuli (e.g., Knau et al., 2003; Monti et al., 2007; Reverberi
et al., 2007), allowing deduction to transpire free of interaction with back-
ground knowledge (e.g., \If there is a circle then there is a triangle", Prado
et al., 2010a). Other experiments employ statements that may con rm or
contradict stored information (e.g., \All poodles are pets" in Goel et al.,
2000; see also Goel et al., 1997; Osherson et al., 1998; Parsons and Osh-
erson, 2001). Compared to content free problems, premises or conclusions
that contradict knowledge might recruit mechanisms for con
ict reduction,
suppressing immediate rejection of the statements at issue; statements that
are con rmed by prior knowledge might recruit the obverse mechanism. The
impact of prior knowledge in reasoning has thus been the focus of several
studies (Goel et al., 2000; Goel and Dolan, 2003a; Reverberi et al., 2009a).
Another stimulus-e ect on deduction might arise from the use of \sim-
ple/e ortless" versus \complex/e ortful" inferences (as in Goel et al., 2000;
Reverberi et al., 2007 on the one hand, and Monti et al., 2007; Rodriguez-
Moreno and Hirsch, 2009, on the other). Finally, a last stimulus di erence
relates to the use of di erent kinds of invalid arguments. Speci cally, we
may distinguish invalid arguments whose conclusions may or may not be
true given the premises from invalid arguments whose conclusions are con-
tradicted by the premises. Some investigators (Goel et al., 2009, 2007) arm
that the rst kind of invalidity, rather than the second, promotes the recruit-
ment of right-lateralized pre-frontal regions in deductive reasoning.
 

 Baseline Task
A critical source of variability between studies relates to di erences in
the baseline tasks used to isolate deductive reasoning. In standard (blood
oxygenation level dependent) neuroimaging, the absolute metabolic response
to a task of interest is dicult to interpret. More informative is the di er-
ence between the task of interest and a \baseline" condition. In high-level
activities such as reasoning, the choice of baseline task is fundamental to
ltering-out ancillary non-deductive processes that participate in the task of
interest. Such ancillary tasks include, for example, reading the stimuli and
distinguishing premises from conclusion. The issue is illustrated by Knau
et al. (2003) who used rest intervals as a baseline for analyzing reasoning
about three-term series. The nonspeci c nature of rest makes it dicult to
assess whether the left posterior temporal activations elicited by the infer-
ential task in this study re
ect reading of the (verbal) stimuli or an active
involvement of linguistic resources in deductive inference.
Beyond rest, some baseline tasks may require only super cial processing
of stimuli. The baseline appearing in Goel et al. (1997), and Goel et al.
(1998), for example, required subjects to determine how many of the three
sentences in a given argument had people as subject. The (minimal) amount
of linguistic processing required by this baseline is likely to be less than
reading the same sentences in view of inferential reasoning; for this reason,
the observed activations in linguistic regions are dicult to interpret. In
Goel et al. (2000), the baseline task resembled deduction trials but included
a conclusion unrelated to the premises. To illustrate, compare the following
deductive trials, each with two premises and one conclusion:

Inference
All poodles are pets.
All pets have names.
[Therefore] All poodles have names.
Baseline:
All poodles are pets.
All pets have names.
[Therefore] No napkins are white.
The obvious presence of extraneous materials in the baseline is sucient to
mark its invalidity, without need of processing the full argument. Moreover,
the slow sequential presentation of each premise and conclusion (at 3 sec-
onds intervals), allows deduction to take place upon display of the second
premise prior to seeing the conclusion (as acknowledged by Goel et al., 2000,
p. 506). Since participants could not know, in advance of seeing the conclu-
sion, whether a given trial was a reasoning or a baseline trial, this design may
thus subtract essential elements of deductive reasoning from deduction trials,
while not adequately ltering reading activations (c.f., Monti et al., 2007).
It is not surprising, then, that this contrast highlighted linguistic activations
(in left inferior frontal gyrus, BA 44) for both \contentful" and \content-less"
 

inferences
A di erent approach was adopted in Osherson et al. (1998), and Parsons
and Osherson (2001), where the very same materials were presented to par-
ticipants for inductive and deductive inference (along with a semantic task).
The two forms of reasoning are both likely to elicit thorough reading of the
materials, thus canceling-out activations due to pure reading and understand-
ing. However, the restriction of comparisons to invalid trials (supporting both
deductive and probabilistic reasoning) may have a ected the generalizability
of the results (c.f. Goel, 2007). At the opposite end of the spectrum, some
baseline tasks may hide important elements of deduction. In Monti et al.
(2007), for example, complex deductive inferences were contrasted with sim-
pler (but linguistically matched) ones. Compared to the complex inferences,
the simple inferences may be expected to require similar amounts of ancil-
lary processes, particularly reading and encoding of the stimuli. But on the
other hand, it is possible that some essential element of deduction is not
sensitive to load, and may thus be obscured by the deductive-load design
(Kroger et al., 2008). While this is indeed a possibility, it appears to be
contradicted by the fact that the same pattern of results was later replicated
using a non-deductive baseline (Monti et al., 2009, x7, appendix). In an in-
teresting evaluation of the claim that logic is based on syntactic processing,
Kroger et al. (2008) compare complex inferences to simpler ones, and logic
inferences to arithmetic processing. In this design, the arithmetic task can
be expected to appropriately lter out working memory and reading related
activity from the logic trials. However, arithmetic operations are syntactic
in nature, and may thus obscure any trace of syntactic processing from the
 

deductive trials
2. The neural basis of deductive reasoning: A working hypothesis
In a recent series of fMRI experiments we have aimed at re-evaluating
(a) the neural basis of (propositional) deductive inference; and (b) the role
of linguistic resources in deduction (Monti et al., 2007, 2009). In particular,
we have focused on deductive inference-making in the absence of factors such
as familiarity of materials (Goel et al., 2000), presence of emotionally salient
contents (Goel and Dolan, 2003b), use of heuristic strategies (Reverberi et al.,
2009a), and con
ict between the truth value of the conclusion statement and
the validity of the inference (Goel and Dolan, 2003a). The impact of all
these factors on reasoning has been the focus of the pioneering work by
Goel and colleagues (see Goel, 2007, for an overview); here, they are set
aside. As we will describe below, across di erent sets of stimuli, semantic
content of arguments, and baseline tasks our results replicate with remarkable
precision. In addition, these ndings have been independently replicated and
extended to other modes of logic (i.e., categorical syllogisms) employing a
di erent set of stimuli, baseline task, experimental procedure and modality
of presentation (i.e., visual versus auditory; Rodriguez-Moreno and Hirsch,
2009). Finally, recent brain lesion data also lends support to our ndings
(Reverberi et al., 2009b). We now review the experimental methods that
were employed in the experiments, and then sketch a working hypothesis
about the neural substrate of deductive reasoning.
2.1. Method
In the two experiments described in Monti et al. (2007), we addressed
points (a) and (b) above by contrasting the neural activations elicited by
complex inferences with those elicited by simpler, but linguistically matched,
inferences (see Table 1). From a cognitive perspective, complex and simple
deductions are expected to recruit the same kind of mental operations, but in
di erent number, repetition or intensity. If linguistic structures are involved
in the inferential process, complex deductions should recruit them signi -
cantly more than simpler ones. On the other hand, if the role of language
is con ned to initial encoding of stimuli, simple inferences can be expected
to require similar levels of reading compared to their complex counterparts.
This expectation is reinforced by the fact that our simple and complex infer-
ences were matched for linguistic complexity (compare the statements com-
prising the simple and complex arguments in Table 1). Therefore, should any
language-related activation be apparent, it can not be considered to re
ect
di erences in initial reading or comprehension (for a discussion on linguistic
versus logical complexity, see Noveck et al., 2004).
Please insert Table 1 about here.
The experiment described in Monti et al. (2009) addressed the role of
linguistic resources in deduction using a di erent technique than the load
method of Monti et al. (2007). Speci cally, Monti et al. (2009) compared
logic inferences based on sentential connectives (i.e., \if . . . then . . . ", \and",
\or", \not") to linguistic inferences based on the syntax and semantics of
ditransitive verbs (e.g., \give", \say", \take"). In this design, logic and
linguistic arguments were each compared to a matched baseline in which,
the very same sentences evaluated for inferential validity were also evaluated
for grammatical well-formedness. The presence of \catch trials" containing
grammatical anomalies assured full reading of stimuli in baseline trials. (See
Table 2 for sample stimuli.) If logical inference is based on mechanisms of
natural language that go beyond mere reading for meaning then the compar-
ison of each type of inference to its matched baseline should uncover common
activations in regions known to underlie linguistic processing. (For the latter
regions, see Ben-Shachar et al., 2003, 2004; Bookheimer, 2002; Bornkessel
et al., 2005; Friederici et al., 2006; Grodzinsky and Friederici, 2006; Kuper-
berg et al., 2006; Virtue et al., 2006.)
Please insert Table 2 about here.
2.2. Activations for sentential logic
As can be seen in Figure 1, across all three experiments the deductive
contrast involving sentential connectives uncovered a highly consistent set of
areas (shown in green).
Please insert Figure 1 about here.
The convergence is especially noteworthy in light of the methodological dif-
ferences between the experiments. These include:
i. baseline task (deductive versus non-deductive)
ii. logical form (e.g., 3-statement versus 2-statement deductions)
iii. lexical content of arguments (e.g., features of an imaginary face, fea-
tures of an imaginary geometric object versus nonsense words, and the
tokens `X,Y,Z' | see Tables 1 and 2)
iv. analysis methodology (namely, use of a \matching windows" analysis
in Monti et al., 2007 versus analysis of the full inference-making period
in Monti et al., 2009).
What unites the three studies is principally their common use of senten-
tial connectives. A similar pattern of activation was recently reported by
Rodriguez-Moreno and Hirsch (2009). The latter investigation, moreover,
di ered in experimental design from our own. In particular, the deductions
in Rodriguez-Moreno and Hirsch (2009) were based on categorical statements
rather than sentential connectives. Also, the lexical content of their state-
ments described familiar scenarios about which participants were likely to
have prior beliefs (e.g., \Every politician likes wildlife.", \Some grown-ups
make snowmen."), unlike the belief-neutral stimuli used in our experiments.
In addition, deductive activations were isolated using a novel non-deductive
working memory baseline. Finally, the pattern of activations they described
was uncovered independently of the modality of stimuli presentation (i.e.,
visual versus auditory).
 

Language and logic
Across di erent presentation modalities, lexical contents of arguments,
and experimental procedures, none of the four experiments discussed above
reported activations in regions typically associated with language process-
ing. On the one hand, greater deductive complexity is not associated with
greater activity in linguistic regions. (But greater reading load, in the ab-
sence of any deduction, is associated with increased response of posterior
temporal linguistic regions; see Monti et al., 2007, online supporting mate-
rials, x2). On the other hand, the (equal) level of activation of linguistic
resources prompted by simple and complex deductions is no greater than
that prompted by reading for grammatical evaluation (Monti et al., 2009) or
later recall (Rodriguez-Moreno and Hirsch, 2009). In addition, when directly
compared, the neural mechanisms underlying logic inferences dissociate from
the neural mechanisms underlying linguistic operations, which have been well
documented in the past (Ben-Shachar et al., 2003, 2004; Bornkessel et al.,
2005; Friederici et al., 2006). The rst set of results hinges on the nding
that language regions are not engaged by deductive reasoning, nor are they
modulated by deductive complexity. While these ndings are negative, they
are dicult to reconcile with the suggestion that language plays a central
role in deduction (Polk and Newell, 1995) and that the latter is derived from
the former (Quine, 1970). Furthermore, our second set of results consist of
a double dissociation, observed at the full brain and ROI levels. This dis-
sociation is consistent with the idea that logic arguments are unpacked into
non-linguistic representations and then submitted to a mentally represented
deductive calculus that is independent of language (Parsons and Osherson,
2001). In the context of deductive reasoning, linguistic resources may be
required to decode verbally presented arguments, but beyond this stage, the
inferential processes is carried out in extra-linguistic regions. In contrast,
when inferences rely on aspects of language processing (Kuperberg et al.,
2006; Virtue et al., 2006; Monti et al., 2009), linguistic resources appear to
be sucient for inference-making.
 

 The neural basis of deductive inference
With respect to the network of regions recruited by logic inference, we
hypothesize that it divides into three sets of functionally distinct areas, as
follows:
Core regions. We propose that \core" regions encompass areas in left ros-
trolateral (BA 10) and medial prefrontal (BA 8) cortices. They underlie
construction of the derivational path that allows successive logical opera-
tions to convert premises into conclusion (in the case of validity). Indeed,
rostrolateral cortex has been often reported for tasks requiring integration
of information (Christo et al., 2001; De Pisapia and Braver, 2008; Kroger
et al., 2002) and embedded operations (van den Heuvel et al., 2003; Ramnani
and Owen, 2004). Similarly, the mesial superior frontal cortex has been as-
sociated with the selection and coordination of multiple subgoals (Koechlin
et al., 2000) as well as tasks requiring multiple rules to transform an initial
state into a nal one (Volz et al., 2005). A recent patient study provides
convergent evidence on the core role in deduction of medial prefrontal cor-
tex (Reverberi et al., 2009b). Patients with left lateral, medial, and right
lateral prefrontal lesions were tested on deductive reasoning. Left prefrontal
lesions impaired the ability to assess the validity of inferences in proportion to
working memory de cits; in particular, patients with spared working mem-
ory performed similarly to healthy volunteers. Memory-impaired patients,
however, retained the ability to judge the relative complexity of deductions,
signaling a spared appreciation for the logical structure of the inferences.
In contrast, lesions in medial prefrontal cortex impaired both the ability to
assess the validity of inferences (even in the presence of spared working mem-
ory) and to judge their complexity. The authors interpret these ndings as
demonstrating the importance of medial prefrontal cortex for \identify[ing]
and represent[ing] the overall structure of the proof necessary to solve a
deductive problem" (Reverberi et al., 2009b, p. 1113). As this discussion
highlights, these two prefrontal regions cannot be thought of as exclusive
to deductive inference. Rather, we believe they re
ect processes that lie at
the heart of deduction, but that may well characterize other (non-deductive)
aspects of human cognition.
Content-Independent Support Areas. The remaining prefrontal and parietal
regions consistently activated across studies (i.e., left BA 6, 7, 8, 9, 40, 47
and BA 6 medial) are proposed to be implicated in representing the struc-
ture of arguments, re
ecting a general cognitive support role (e.g., working
memory). Previous literature has implicated these regions in functions in-
cluding allocation of spatial attention (Tanaka et al., 2005), manipulation of
information in working memory (Hanakawa et al., 2002), maintaining serial
structure of motor sequences (Jubault et al., 2007), maintaining compound
rules across delays (Bunge et al., 2003), representing numerical and spatial
information (Pinel et al., 2001), and serial updating of verbal information
(Tanaka et al., 2005). (See Monti et al., 2007, for a complete discussion.)
Content-Dependent Support Areas. The last component of our proposed net-
work is comprised of content speci c support regions, which we hypothesize
to be implicated in bu ering information about the lexical content of logic
arguments. To illustrate, consider the logical form underlying the top two
arguments in Table 1 (where \!", \_" and \" stand for \if : : : then : : :",
\or" and \not", respectively). The form in question is:
11
(p _ q) ! r
r
q
The variables \p; q; r" can be replaced with any sentences without a ecting
the validity of the argument. In Monti et al. (2007) we tested the notion of
content-dependent support regions by comparing brain activation in response
to the same logical forms when di erent lexical contents replaced the variables
(see Table 1). When the sentences concerned a hypothetical house additional
activations were detected in the parahippocampal gyrus, whereas when they
concerned a hypothetical face additional activations were detected in the
inferior temporal cortex. These localizations are consistent with what is
known about the neural representation of faces and houses. The neural
localization of such additional activations is proposed to vary according to
the speci c lexical contents in play.

 Alternative hypotheses
In contrast to the language-independent view of deductive reasoning sketched
above, at least two other hypotheses have been defended. According to one,
the role of linguistic regions in deduction depends on whether the inferences
concern familiar or unfamiliar materials (Goel and Dolan, 2001; Goel, 2007).
According to the other hypothesis, the substrate of language embodies de-
ductive operations for all stimulus material (Prado et al., 2010a; Reverberi
et al., 2007, 2010). We examine these ideas in turn.
3.1. Material-dependent networks for deductive reasoning
Goel et al. (2000) was the rst to observe that the neural basis of de-
ductive reasoning is a ected by the semantic contents of an argument. More
speci cally, the presence of materials that are familiar and conceptually co-
herent with the reasoner's knowledge was proposed to engage a language
based fronto-temporal \heuristic" system. Conversely, inferences with un-
familiar, nonconceptual or incoherent materials was proposed to engage a
\formal/universal" bilateral parietal system (Goel, 2007). The data in sup-
port of this idea, however, ar open to question. First, left inferior frontal
linguistic regions (particularly, BA 44) are consistently activated by both
\familiar" and \unfamiliar" arguments (c.f., Goel et al., 2000, Table 2, p.
508; see also Goel and Dolan, 2003a,b, 2004). It is therefore dicult to
qualify just one of these networks as \linguistic." Second, posterior tempo-
ral activation (likewise conceived as language-related) is highly inconsistent
across investigations. In Goel and Dolan (2003a), for example, the only tem-
poral activation reported falls in the left anterior pole, a region more than
6 centimeters away from the (classical) linguistic focus found in Goel et al.
(2000). In Goel and Dolan (2004) contentful syllogisms including statements
likely to elicit the reasoner's prior beliefs (e.g., \All animals with 32 teeth
are cats.") do not appear to recruit temporal cortex at all, while activating
left inferior frontal linguistic regions (BA 44) together with posterior pari-
etal cortex (BA 7), a pattern similar to that expected for inferences over
non-familiar contents. A similar lack of temporal activation, accompanied
by activations in posterior parietal cortex, is also seen in other investigations
with contentful syllogisms that are likely to engage prior beliefs (Rodriguez-
Moreno and Hirsch, 2009). Overall, it is possible that content plays some
role in selecting the neural underpinnings of reasoning; however, familiarity,
coherence of materials, and prior beliefs do not seem to be critical to the
involvement of language areas in deduction.

 Deduction as a language based process
The idea that deductive inference is language based (for all argument
content) has been advanced several times (e.g., Goel et al., 1997, 1998; Goel
and Dolan, 2004), and has received recent support (Reverberi et al., 2007,
2010). Employing a novel experimental design, Reverberi et al. (2007) found
that the integration of premises for simple propositional inference elicits left
lateralized frontal (BA 6, 44) and parietal (BA 40) activations. Importantly,
the frontal cluster falls within regions typically associated with language pro-
cessing, supporting the idea that linguistic operations may be fundamental to
deductive inference. This result was later extended to categorical syllogisms,
which were found to recruit the same sections of the inferio frontal gyrus
along with the same focus within left BA 6 (Reverberi et al., 2010). Unlike
Reverberi et al. (2007), however, parietal regions were only found to be ac-
tive for categorical syllogisms Overall, inference in the two studies elicited
signi cant activation of the left inferior frontal gyrus (in BA 44 and BA 45).
Using the same experimental procedure employed by Reverberi and col-
leagues, Prado et al. (2010a) compared the neural basis of inferences based on
propositional connectives to inferences based o relational terms. The authors
reported two main ndings. First, inferences based on connectives recruited
di erent cortical areas than inferences based on transitive relations. This
tive syllogisms, furthering the notion that deduction should be conceived as a
heterogeneous collection of cognitive processes sensitive to the speci c logical
vocabulary on which the inferences rely (e.g., sentential connectives, quan-
ti ers or relational terms, Monti et al., 2009). Second, Prado et al. (2010a)
interpret activation in left inferior frontal gyrus, speci c to connectives, as
supporting the view that linguistic/syntactic processes contribute to some
domains of deduction. The latter conclusion, however, may be unwarranted.
Indeed, the activation described falls in a (medial) region of the inferior
frontal gyrus, very di erent from the (lateral) sections typically implicated
in syntactic tasks (e.g. Bornkessel et al., 2005; Dapretto and Bookheimer,
1999, c.f., Grodzinsky and Friederici, 2006; Kaan and Swaab, 2002; Price,
2000). The medial region reported by Prado et al. (2010a), often referred to
as the \deep frontal operculum" (Anwander et al., 2007), has been proposed
to serve articulatory planning (Price, 2000). When this function is controlled
for, there is little if any involvement of this area in syntactic tasks (Rogal-
sky and Hickok, in press). Similarly, while the deep frontal operculum has
been implicated in retrieval and maintenance of (spatial and verbal) abstract
rules (Bunge et al., 2003), this is believed to be in connection with subvocal
rehearsing (Bunge, 2004).Overall, despite the use of a common experimental
procedure, it is dicult to interpret the ndings of Prado et al. (2010a) as
supporting the language-based view of deduction proposed by Reverberi and
colleagues.
 

Logic, language and the brain: Where do we stand
When considering the involvement of linguistic resources in deductive rea-
soning, it is important to distinguish two potential roles. It is evident that
linguistic resources must be recruited for initial reading and encoding of ver-
bally presented stimuli. More controversial is whether language plays a part
in the subsequent inferential process. Evaluating this latter point has proven
dicult, for at least two reasons. First, recruitment of a given neural region
may be triggered by particular features of an inference, such as the speci c
logical vocabulary on which it relies (e.g., propositional connectives, quanti-
ers, relational terms). Second, it is often dicult to disentangle activations
re
ecting the deductive process from activations re
ecting accompanying an-
cillary processes, such as reading.
A recent series of results (Monti et al., 2007, 2009; Rodriguez-Moreno and
Hirsch, 2009) nonetheless suggests that language areas are not involved in the
deductive process, a nding consistent with several previous reports (Canessa
et al., 2005; Fangmeier et al., 2006; Goel and Dolan, 2001; Knau et al.,
2002; Noveck et al., 2004; Parsons and Osherson, 2001; Prado and Noveck,
2007). We interpret these ndings as implying that the role of language is
con ned to initial encoding of verbal statements into mental representations
suitable for the inferential calculus. The representations themselves, as well
as the deductive operations, are not linguistic in nature. A di erent view,
stressing the centrality of language regions to deduction, has been recently
advocated by Reverberi and colleagues (Reverberi et al., 2007, 2010). How
can the two sets of ndings be reconciled? One way of reducing the discrep-
ancy is suggested by the results of Prado et al. (2010a). In this study, the
experimental design departed from that used in Reverberi et al. (2010) in
only one important feature, namely, the complexity of the logic arguments.
Prado and colleagues make exclusive use of the modus tollens logic form, a
structure more complex than modus ponens, aa well as the conjunction and
disjunction problems used by Reverberi and coworkers (c.f., Garnham and
Oakhill, 1994, p. 78). It is possible that e ortless inferences may be entirely
supported by linguistic comprehension, while e ortful deductions require a
qualitatively di erent set of processes, not supported by the neural mecha-
nisms of language. This possibility is consistent with the nding that BA
44/45 is implicated in spontaneous (non-deductive) causal inference-making
during text comprehension (Kuperberg et al., 2006; Virtue et al., 2006). On
the other hand, the foregoing hypothesis seems inconsistent with the im-
paired performance, on elementary deductions, of prefrontal patients with
preserved language skills (Reverberi et al., 2009b). The latter nding seems
to indicate that linguistic resources are not sucient for deductive inference.
A second factor that may help explain the discrepant recruitment of lan-
guage resources across experiments, is the use of di erent means for elic-
iting deductive reasoning (c.f., Reverberi et al., 2007). It is possible that
the crucial feature engaging linguistic mechanisms is the necessity to gener-
ate a deductive conclusion (as in Reverberi et al., 2007, 2010), as compared
to evaluating a conclusion that is provided (as in Monti et al., 2007, 2009;
Rodriguez-Moreno and Hirsch, 2009). This distinction might explain why
prefrontal patients with intact linguistic resources could not correctly assess
the validity of provided inferences (Reverberi et al., 2009b). But this pro-
posal cannot explain the failure to activate, in the generation task employed
in Prado et al. (2010a), the sections of inferior frontal gyrus reported in
Reverberi et al. (2007) and Reverberi et al. (2010).
Several studies have reported remarkable dissociations between cognitive
and linguistic abilities (e.g., in theory of mind and arithmetic; see Varley and
Siegal, 2000; Varley et al., 2005). Yet the role of language in human thought
remains a controversial issue (Gleitman and Papafragou, 2005; Levinson,
2003). With respect to deductive reasoning in a mature healthy brain, we
propose the role of language to be con ned to initial encoding of verbally
presented materials; neither the mental representations formed as a result
of the initial encoding nor the deductive operations themselves appear to be
supported by the neural mechanisms of natural language. Whether these
latter mechanisms contribute to the development of deductive competence
remains to be investigated.