Tools of the Trade
Studying mind and brain with fMRI
Marc G. Berman, John Jonides, and Derek Evan Nee
Department of Psychology, University of Michigan, Ann Arbor, MI, USA
The explosion in publications using functional magnetic resonance imaging (fMRI) warrants an examination of how the
technique is being used to study processes of mind and brain. Here, we propose a classification of fMRI studies that reveals
how this technique is being used in the service of understanding psychological and neural processes and the relationship
between the two.
Keywords: fMRI; psychological models; psychological processes
In 1993, the number of published articles citing functional
magnetic resonance imaging (fMRI) was fewer than 20.
In 2003, that number was nearly 1800. Although not
exponential, the rise in publications year by year during
this interval was positively accelerated. What is it that
psychologists and neuroscientists have found useful about
this technique (and about other techniques such as positron
emission tomography and event-related potentials)? That is,
what use is being made of data from fMRI experiments?
As this technology matures, we take a moment to reflect
on the ways in which fMRI has been used to study mind
We implement this reflection by attempting to classify
the approaches that authors have used in applying fMRI data
to understanding psychological and neural mechanisms.
By our reckoning, the number of approaches taken by
authors is quite small. As a representative sample, we
surveyed all primary empirical studies using fMRI, published
in the journals Science and Nature from 2000–2006. There
are 64 such studies, and our analysis of them indicates that
they adopted one or more of the approaches that we describe
in subsequent sections.
STUDIES OF LOCALIZATION
One approach to the use of fMRI is motivated by an
interest in localizing psychological functions to brain
regions. The intent of authors who adopt this approach is
to identify brain behavior correlations—that is, to discover
how psychological processes are localized in brain tissue.
There is substantial value in understanding localization,
both to understand the normal organization of modules
of processing and to predict the nature of deficits that will
arise when brain tissue is damaged.
One example of this approach comes from Downing et al.
(2001) who were motivated to discover whether object
recognition makes use of the same neural machinery
regardless of the object being recognized, or whether there
are modules of processing tailored to specific classes of
objects. Specifically, these authors were concerned with
mapping out the brain regions responsible for recognition of
parts of the human body, a finding that might help inform
us about deficits such as those documented by Shelton et al.
(1998), having to do with failures to process semantic
information about body parts. They discovered remarkable
consistency among participants in the activation of a region
in right lateral occipitotemporal cortex that responded more
strongly to images of human bodies than to other classes
of objects, consistent both with the neuropsychological
evidence in humans and with the records of single-cell
activity in monkeys for similar material. Thus, a program of
this sort is of value not only in mapping out the architecture
of the visual processing stream, but also in helping to
understand neuropsychological pathologies that involve
selective deficits in processing certain classes of objects.
One issue that arises in the study of localization is
just how modular brain organization is. That is, is there
a one-to-one mapping of functions onto brain regions?
Cases such as face recognition (e.g. Kanwisher, 2000) and
recognition of parts of the human body (Downing et al.,
2001) suggest that there may be such a straightforward
mapping, but the work of Haxby et al. (2001) reveals that the
coding is more complex than this. They proposed a model of
object recognition in which the processing of faces and other
objects is distributed over a swath of brain regions.
According to their model, it is the pattern of activation
Received 1 August 2006; Accepted 21 August 2006
Preparation of this manuscript was supported in part by grant BCS 0520992 from the National Science
Foundation (NSF) to the University of Michigan and by NSF Graduate Fellowships to M.G.B and D.E.N.
Correspondence should be addressed to Marc G. Berman, Department of Psychology, University of
Michigan, Ann Arbor, MI 48109-1109, USA. E-mail: firstname.lastname@example.org.
doi:10.1093/scan/nsl019SCAN (2006) 1,158–161
? The Author(2006).Publishedby OxfordUniversityPress.For Permissions,pleaseemail:email@example.com
over the regions critical to object identification that is critical
to object recognition, and not individual, encapsulated brain
areas that are activated selectively for different stimuli. The
authors found uniquely distributed patterns of neural
activity in ventral temporal cortex (VTC) for the identifica-
tion of faces, houses, cats and various man-made objects.
Activation patterns in VTC predicted the category of object
being viewed with 96% accuracy, showing that objects are
uniquely represented in VTC. This result is consistent with
the regional specificity implied by the work of Downing et al.
(2001). What is striking is that even when the region that
responded maximally to a class of objects (e.g. faces) was
removed from the analysis, the pattern of activation in the
remaining regions in VTC still correctly discriminated the
classes of objects with 94% accuracy. That is, while VTC may
be the site of processes critical to visual object recognition,
its organization is not strictly modular, with an overlapping
and distributed representation of objects appearing to be
the most apt characterization. These results show that
a program of research concerned with localization need
not be restricted to identifying one-to-one brain-to-behavior
STUDIES OF COMMONALITIES IN BRAIN ACTIVATION
activation of common brain areas, then these two tasks or
behaviors are likely to share some process or processes
(Jonides et al., 2006; Henson, 2006). As Poldrack (2006) and
Coltheart (2006) demonstrated, this logic is not infallible.
Even so, examining cases in which regional brain activation
can be quite informative. For example, Eisenberger et al.
(2003) showed that neural activity in anterior cingulate and
right ventral prefrontal cortex found during the experience
of social exclusion (social pain) was very similar to that found
during the experience of physical pain, suggesting that the
experiences corresponding tothesetwotypesofpainarequite
(2004) who studied the neural mechanisms underlying the
placebo effect. Wager et al. (2004) found that placebo
analgesia was related to decreased activity in regions sensitive
to physical pain such as the thalamus, insula and anterior
cingulate cortex (Wager et al., 2004). That is, there was an
overlap in the regions that were decreased in activation
by placebos with the regions that are increased in activation
by physical pain. This result leads to the hypothesis that
placebos exercise their effect by lowering the activation
in brain regions that respond to physical pain, thereby
exercising their analgesic effect on central processing
mechanisms. These studies demonstrate that fMRI can be
used to infer the cognitive processes involved in one task
by showing similarities in brain activation to a better
STUDIES OF DISTINCTIVENESS IN BRAIN ACTIVATION
The complement to studies of common brain activations
are studies that seek to discover distinctive activations
between two tasks. Discovering such dissociations permits
the inference that two tasks have different cognitive
processes mediating them (e.g. Smith and Jonides, 1995;
Smith et al., 1996; Jonides et al., 2006; Henson, 2006;
Poldrack, 2006). Thus, studies of distinctive activations when
added to studies of common activations enable a program
of research that will gradually build an architecture of
psychological processing out of an architecture of brain
activity. Of course, one caveat with this technique is that
most findings of distinctive activations yield results of partial
overlap in activations, so the distinctiveness that is found
may be quantitative rather than a qualitative one.
An excellent example of a search for dissociations
comes from the work of MacDonald et al. (2000).
They found dissociable neural mechanisms underlying the
implementation of cognitive control and performance
monitoring, suggesting that these two psychological pro-
cesses are separable. The authors used a variant of the Stroop
task in order to study these phenomena (Stroop, 1935).
Participants were given an instruction either to read the
word or to name the color of an upcoming Stroop stimulus.
Thus, they had an opportunity to prepare for the task at hand.
Stimuli could be congruent (in which the word and its ink
color matched, such as the word ‘green’ printed in green ink)
or incongruent (in which the word and its color mismatched,
such as the word ‘green’ printed in red ink). On the one hand,
the authors found that left dorsolateral prefrontal cortex was
more active when subjects were instructed to name the color
rather than read the word, evidence that this region was
implementing cognitive control by preparing for the more
challenging task. On the other hand, the anterior cingulate
cortex was activated by the stimuli itself, showing greater
activation for the high-conflict incongruent compared with the
low-conflict congruent stimuli. This pattern of dissociation
enabled a hypothesis that the lateral prefrontal cortex was
implementing cognitive control while the anterior cingulate
cortex was involved in the monitoring of performance.
DOCUMENTING INDIVIDUAL DIFFERENCES
The first three approaches we described above, all rest on
a consistent mapping of brain and behavior that is found
across individuals. However, there have been recent lines of
research that extend the use of fMRI to the identification
of differences across individuals as well. Consider, for
example, a study by Canli et al. (2002). They found
consistent activation in the amygdala among participants
when viewing fearful facial expressions, but inconsistent
activation when participants viewed happy facial expres-
sions. A good deal of the variability in activation when
viewing happy expressions was predicted by measuring
participants’ scores on an extraversion scale. The higher the
score on this scale, the higher was the activation in amygdala.
StudyingmindandbrainwithfMRI SCAN (2006)159
The authors discuss the difference between the responses
to fearful and happy expressions in terms of the adaptive
value in responding consistently to fear, in contrast to the
tailored responsiveness of outgoing people to happiness.
Using similar methodology, Schwartz et al. (2003) found
that adults who were identified as inhibited toddlers showed
higher activity in the amygdala for novel vs familiar faces
compared with adults who were identified as uninhibited
toddlers. One possibility raised by these data is that the
difference in temperament exhibited by inhibited and
uninhibited toddlers may be the result of differential activity
in the amygdala in response to novelty, and that neural
properties relating to temperament may be preserved from
childhood into adulthood (Schwartz et al., 2003).
These and other studies of individual differences in
brain activation can play a role with behavioral data in
accounting for both consistent and inconsistent behavior
TESTING PSYCHOLOGICAL MODELS
As we come to understand more and more about the
functionality of regions of the brain, it is becoming
increasingly possible to use fMRI data to distinguish
alternative psychological models of task performance. For
instance, Brown and Braver (2005) used fMRI to distinguish
between two competing theories of cognitive control. One
theory posits that in situations where multiple responses
compete for behavior, a conflict monitor calls for increased
control to mitigate interfering responses (Botvinick et al.,
2001). Supporters of this theory have demonstrated that the
anterior cingulate cortex appears to be responsive to the
degree of response conflict in a variety of cognitive tasks
and that interactions between the anterior cingulate and
lateral prefrontal cortex correlate with behavioral adjust-
ments in situations of high conflict (Kerns et al., 2004).
An alternative account is that the anterior cingulate responds
to the likelihood that an error will follow from a given
context. This theory builds upon the reinforcement learning
literature, which demonstrates that a dopaminergic error
signal drives learning, allowing an organism to adapt its
behaviors to specific contexts. By carefully dissociating error
likelihood and conflict through a change-signal task, Brown
and Braver (2005) found that the anterior cingulate learns
to respond to situations where errors are probable even when
conflict is low. These results are consonant with an error
likelihood account of cognitive control rather than a conflict
monitoring theory. Importantly, the authors point out that
both the error likelihood model and the conflict monitor
model accurately fit behavioral data from the change-signal
task, and that it was only the fMRI data that differentiated
these two models of cognitive control (Brown and Braver,
2005). Here we see that fMRI can be used to test model
predictions and thereby distinguish among competing
psychological theories even when behavioral data alone
may not make those discriminations.
Of what value have fMRI data been to the study of mind and
brain? Let us count the ways. They have been instrumental
in establishing correlations between brain and behavior.
They have allowed us to examine overlapping and non-
overlapping patterns of brain activation that are valuable in
building up a view of shared and distinct processes
among psychological tasks. They are beginning to permit
us to understand consistencies and inconsistencies in
human behavior, as accounted for by consistencies and
inconsistencies in brain activation. Finally, they are now
allowing us to test among alternative psychological models
of behavior. We are not advocating a neuroimaging-
imperialism here because it is quite clear that the true
value of neuroimaging data comes in concert with
sophisticated behavioral data collected from normal and
brain-injured participants. Nevertheless, there have been
substantial accomplishments with fMRI data in fewer than
Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., Cohen, J.D. (2001).
Conflict monitoring and cognitive control. Psychological Review, 108,
Brown, J.W., Braver, T.S. (2005). Learned predictions of error likelihood in
the anterior cingulate cortex. Science, 307, 1118–21.
Canli, T., Sivers, H., Whitfield, S.L., Gotlib, I.H., Gabrieli, J.D.E. (2002).
Amygdala response to happy faces as a function of extraversion. Science,
Coltheart, M. (2006). Perhaps functional neuroimaging has not told us
anything about the mind (so far). Cortex, 42, 422–7.
Downing, P.E., Jiang, Y.H., Shuman, M., Kanwisher, N. (2001). A cortical
area selective for visual processing of the human body. Science, 293,
Eisenberger, N.I., Lieberman, M.D., Williams, K.D. (2003). Does rejection
hurt? An fMRI study of social exclusion. Science, 302, 290–2.
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.
(2001). Distributed and overlapping representations of faces and objects
in ventral temporal cortex. Science, 293, 2425–30.
Henson, R. (2006). Forward inference using functional neuroimaging:
dissociations versus associations. Trends in Cognitive Sciences, 10, 64–9.
Jonides, J., Nee, D.E., Berman, M.G. (2006). What has functional
neuroimaging told us about the mind? So many examples, so little
space. Cortex, 42, 414–7.
Kanwisher, N. (2000). Domain specificity in face perception. Nature
Neuroscience, 3, 759–3.
Kerns, J.G., Cohen, J.D., MacDonald, A.W., Cho, R.Y., Stenger, V.A.,
Carter, C.S. (2004). Anterior cingulate conflict monitoring and adjust-
ments in control. Science, 303, 1023–6.
MacDonald, A.W., Cohen, J.D., Stenger, V.A., Carter, C.S. (2000).
Dissociating the role of the dorsolateral prefrontal and anterior cingulate
cortex in cognitive control. Science, 288, 1835–8.
Poldrack, R.A. (2006). Can cognitive processes be inferred from neuro-
imaging data? Trends in Cognitive Sciences, 10, 59–63.
Schwartz, C.E., Wright, C.I., Shin, L.M., Kagan, J., Rauch, S.L. (2003).
Inhibited and uninhibited infants ‘‘grown up’’: Adult amygdalar response
to novelty. Science, 300, 1952–3.
Shelton, J.R., Fouch, E., Caramazza, A. (1998). The selective sparing of body
part knowledge: a case study. Neurocase, 4, 339–51.
Smith, E.E., Jonides, J. (1995). Working memory in humans: neuropsycho- Download full-text
logical evidence. In: Gazzaniga, M., editor. The Cognitive Neurosciences.
Cambridge, MA: MIT Press, pp. 1009–20.
Smith, E.E., Jonides, J., Koeppe, R.A. (1996). Dissociating verbal and spatial
working memory using PET. Cerebral Cortex, 6, 11–20.
Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal
of Experimental Psychology, 12, 643–62.
Wager, T.D., Rilling, J.K., Smith, E.E., et al. (2004). Placebo-induced
changes in fMRI in the anticipation and experience of pain. Science, 303,