Sophisticated noninvasive stimulation and recording techniques for measuring the brain’s electrical activity and noninvasive brain imaging methods led to a major shift in studying brain and behavior: cognitive neuroscience, the field that studies the neural bases of cognition. Cognitive neuroscience focuses on high-tech research methods but continues to rely on the decidedly low-tech tools of neuropsychological assessment—behavioral tests that compare the effects of brain injuries on performing particular tasks. Clinical Focus 15-4, Neuropsychological Assessment, illustrates its benefits.

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Sophisticated imaging techniques are helping cognitive neuroscientists map the human brain. Its methods assist social psychologists in discovering how the brain mediates social interactions and economists in discovering how the brain makes decisions.

Among the great scientific challenges of the twenty-first century, mapping the human brain connectome may be the most intriguing. Toward this end, the U.S. National Institutes of Health (NIH) awarded grants totaling $40 million to map the complete structural and functional fiber pathway connections of the human brain in vivo. Researchers participating in the Human Connectome Project (HCP) are combining diffusion tensor imaging (DTI) and functional connectivity magnetic resonance imaging (fcMRI) to plot these connections in the living human brain. By the end of 2015 HCP investigators had completed scanning 1200 healthy adults’ brains, including 300 twin pairs.

The HCP’s goal is to provide a reference atlas for those seeking to understand human brain function and dysfunction. It has attracted considerable public interest, and an explosion of studies outside the HCP also seeks to map the human brain. For example, in a review, Roser Sala-Llonch and colleagues (2015) report that as people age, they show reduced functional connectivity compared to young adults. The investigators hypothesize that this finding could explain age-related cognitive changes.

Using fMRI in Brain Mapping

The fcMRI technique uses resting-state fMRI (rs-fMRI) to measure functional correlations between brain regions. Pooling rs-fMRI data across thousands of healthy young adults makes it possible to identify consistent patterns of connectivity, or nerve tracts, in the brain. Utilizing rs-fMRI data from 1000 participants, Thomas Yeo and colleagues (2011) parcellated the human cerebral cortex into the 17 networks illustrated in Figure 15-9.

The cerebral cortex is made up of primary sensory and motor networks as well as the multiple, large-scale networks that form the association cortex. The sensory and motor networks are largely local: adjacent areas tend to show strong functional coupling with one another. In Figure 15-9, the turquoise and blue/gray regions in somatosensory and motor cortex and the purple region in visual cortex illustrate these couplings.

In contrast, the association networks include areas distributed throughout the prefrontal, parietal, anterior temporal, and midline regions. In Figure 15-9, the distributed yellow regions show prefrontal–posterior parietal connectivity. Some distributed networks, shown in light red, include temporal, posterior parietal, and prefrontal regions.

Unlike DTI, fcMRI does not measure static anatomical connectivity but rather uses temporal (time-based) correlations between neurophysiological activities in different regions to infer functional connectivity. One obvious direction of investigations based on fcMRI is searching for individual phenotypic variations. Such mapping will allow examination of specific traits (e.g., musical ability) or psychiatric diagnoses (see the review by Kelly and colleagues, 2012).

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Tractography Using Diffusion Tensor Imaging

DTI studies provide results, often called tractography, that complement the networks mapped by fcMRI. Tractography measures actual neuroanatomical pathways that can be related to specific traits. Traditional postmortem tract tracing was performed on single brains. Today, tractography can be performed quickly on many living brains, and measurements can be made simultaneously in the entire brain. This advance allows researchers to correlate specific behavioral traits with specific patterns of connectivity.

Psyche Loui and her colleagues (2011) were interested in the neural basis of perfect (or absolute) pitch, the ability not only to discriminate among musical notes but also to name any note heard. Perfect pitch is rare among humans but shared by people with remarkable musical talents. Think Mozart. Development of perfect pitch is sensitive to early experiences, including musical training and exposure to tonal languages such as Japanese.

Loui’s team studied musicians, with and without absolute pitch, who were matched in gender, age, handedness, ethnicity, IQ score, and years of musical training. Participants were given a test of pitch-labeling that allowed the researchers to place the musicians with absolute pitch into two categories: more accurate and less accurate. The less accurate group was superior to participants without absolute pitch, who could not accurately identify the note.

The investigators hypothesized that absolute pitch could be related to increased connectivity in brain regions that process sounds. They used DTI to reconstruct white-matter tracts connecting two temporal cortex regions involved in auditory processing, the superior and middle temporal gyri, and regions not involved in auditory processing (corticospinal tract). The results, reproduced in Figure 15-10, show that people with absolute pitch have greater connectivity in temporal lobe regions responsible for pitch perception than do people without it. The effect is largest in people in the more accurate group.

Although enhanced connectivity appears in both hemispheres of the musicians, when the investigators correlated performance on a test of absolute pitch with tract volume, only left-hemisphere tract volume predicted performance. It appears that having more local connections, or hyperconnectivity, in the left hemisphere is responsible for absolute pitch.

It is tempting to speculate that other exceptional talents, such as creativity, might be related to hyperconnectivity in cerebral regions. Conversely, we can speculate that reduced structural and functional connectivity is related to cognitive impairments after acquired brain injuries and/or neurodevelopmental and psychiatric disorders.

So far we have emphasized the role of the neocortex in cognitive functions, in part because of its marked expansion in size and neuron numbers in primate brains. Yet the human cerebellum accounts for 80 percent of the brain’s neurons. The cerebellum has long been known to play a central role in motor control and motor learning, but the concurrent evolution of neocortex, cerebellum, and cognitive complexity in primates suggests that the cerebellum may play a larger role in cognitive processes than investigators have appreciated (e.g., Barton, 2012).

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The extensive neocortex–cerebellum interconnections include prefrontal cortex, Broca’s area, and neocortical regions that have sensory or perceptual functions. Thus, the cerebellum appears to be critical in producing fine movements and perception, but some executive function also may be associated with working memory, attention, language, music, and decision-making processes (Bauman et al., 2015). No single coherent model of cerebellar activity in cognition has emerged, partly because researchers interested in cognitive functions have neglected the cerebellum for decades. Undoubtedly, the coming decade will deliver an abundance of new information, allowing for a better- informed model of cerebellar functions in cognition.

By combining cognitive neuroscience tools, especially functional neuroimaging, with abstract constructs from social psychology, social neuroscience seeks to understand how the brain mediates social interactions. Matthew Lieberman (2007) identified broad themes that attempt to encompass all cognitive processes involved in understanding and interacting with others and in understanding ourselves.

Understanding Others

Animals’ mind and experiences are not open to direct inspection. We infer animals’ mind in part by observing their behaviors and peoples’ mind by listening to their words. In doing so, we may develop a theory of mind, the attribution of mental states to others. Theory of mind includes an understanding that others may have feelings and beliefs different from our own. This broader understanding has led some investigators to conclude that theory of mind may be uniquely human. But many researchers who study apes strongly believe that other apes, too, possess a theory of mind.

Many fMRI studies over the past decade suggest that the brain region believed most closely associated with theory of mind is the dorsolateral prefrontal cortex (see Figure 15-2). Human prefrontal regions are disproportionately large as corrected for brain size, but other apes also have large prefrontal regions. The anatomy supports the likelihood that they also possess a theory of mind.

The capacity to understand others can also be inferred from the presence of empathy. For example, when participants watch videos of others smelling disgusting odors, they report a feeling of disgust. Lieberman and his colleagues (Rameson et al., 2012) used fMRI to assess the neural correlates of empathy by asking participants to empathize with sad images. Empathy correlated with increased activity in the medial prefrontal region, suggesting that the area is critical for empathic experience.

Understanding Oneself

Not only are we humans aware of others’ intentions, we also have a sense of self. Humans and apes have the ability to recognize themselves in a mirror, an ability that human infants demonstrate by about 21 months of age. Studies using fMRI show that when we recognize our own face versus the face of familiar others, brain activity increases in the right lateral prefrontal cortex and in the lateral parietal cortex. The parietal cortex activation is thought to reflect the body’s recognition of what itself feels like.

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But self-recognition is only the beginning of understanding oneself. People also have a self-concept that includes beliefs about their own personal traits (e.g., kind, intelligent). When participants are asked to determine whether trait words or sentences are self-descriptive, brain activity in medial prefrontal regions increases.

Self-Regulation

Self-regulation is the ability to control emotions and impulses as a means of achieving long-term goals. We may wish to yell at the professor because an exam was unfair, but most of us recognize that this course will not be productive. Dynamic imaging studies again reveal that prefrontal regions are critical in social cognition, in this case in self-regulation.

Children are often poor self-regulators, which probably reflects slow development of the prefrontal regions responsible for impulse control. A uniquely human ability to self-regulate is by putting feelings into words, a strategy that allows us to control emotional outbursts. Curiously, such verbal labeling is associated with increased activity in the right lateral prefrontal regions but not in the left.

Not only can humans control their emotions; they also have expectations about how a stimulus might feel (e.g., an injection by syringe). Our expectations can alter the actual feeling of an event. It is common for people to say ouch when they do something like stub a toe, even if they actually feel no pain. Nobukatsu Sawamoto and colleagues (2000) found that when participants expect pain, activity increases in the anterior cingulate cortex (see Figure 15-2B), a region associated with pain perception, even if the stimulus turns out not to be painful.

Living in a Social World

We spend much of our waking time interacting with others socially. In a sense, our understanding of our self and our social interactions link together into a single mental action. One important aspect of this behavior includes forming attitudes and beliefs about ourselves and about others. When we express attitudes (including prejudices) toward ideas or human groups, brain imaging shows activation in prefrontal, anterior cingulate, and lateral parietal regions.

Samuel McLure and colleagues (2004) took advantage of the fact that many people have strong attitudes toward cola-flavored sodas. Coca-Cola and Pepsi Cola are nearly identical in chemical composition, yet subjectively, people routinely prefer one to the other. The researchers ran blind taste tests among people who stated a cola brand preference.

As a group, participants failed to discriminate the drinks accurately when they were presented in a blind taste test. Brain activity was also equivalent for each cola in the blind condition. However, when participants believed that they were drinking Coca-Cola, significant changes in brain activity were recorded in many regions, including the hippocampus and dorsolateral prefrontal cortex. The investigators concluded that cultural information biases brain systems, which in turn biases attitudes.

Social Cognition and Brain Activity

Social cognitions running the gamut from understanding ourselves to understanding others clearly are associated with activation of specific brain regions, especially prefrontal regions. The obvious conclusion is that prefrontal activity produces our social cognitions, just as activity in visual regions produces our visual perceptions. But this conclusion has proved controversial. Ed Vul and colleagues (2009, 2012) go so far as to suggest that “correlations in social neuroscience are voodoo.” Their assertion has led to strong disputations (e.g., Lieberman et al., 2012). The arguments are complex, focus on the nature of the analysis of fMRI data, and will certainly continue. In our view, the debate does not impugn the general conclusion that brain states produce behavioral states.

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Historically, economics was a discipline based on the “rational actor,” the belief that people make rational decisions. In the real world, people often make decisions based on assumption or intuition, as is common in gambling. Why don’t people always make rational decisions?

The cerebral processes underlying human decision making are not easily inferred from behavioral studies. But investigators in the field of neuroeconomics, which combines ideas from economics, psychology, and neuroscience, attempt to explain those processes by studying patterns of brain activity as people make decisions in real time. The general assumption among neuroeconomists is that two neural decision pathways influence our choices. One is deliberate, slow, rule-driven, and emotionally neutral; it acts as a reflective system. The other pathway—fast, automatic, emotionally biased—forms a reflexive system.

If people must make quick decisions they believe will provide immediate gain, widespread activity appears in the dopaminergic reward system. This includes the ventromedial prefrontal cortex and ventral striatum (nucleus accumbens): the reflexive pathway. If slower, deliberative decisions are possible, activity is greater in the lateral prefrontal, medial temporal, and posterior parietal cortex, the areas that form the reflective pathway.

Neuroeconomists are looking to identify patterns of neural activity in everyday decision making, patterns that may help account for how people make decisions about their finances, social relations, and other personal choices. Although most neuroeconomic studies to date have used fMRI, in principle these studies could also use other noninvasive imaging technologies. Epigenetic factors probably contribute to developing the balance between the reflective and reflexive systems in individuals. Epigenetic studies therefore may help explain why many people make decisions that are not in their long-term best interest.

Because of predictions that all mammals will have similar reflective and reflexive decision systems and because all animals make decisions, the neural bases of decision processes in nonhumans undoubtedly will receive more study in the future.

Expanding Frontiers of Cognitive Neuroscience

Before you continue, check your understanding.

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Answers appear in the Self Test section of the book.