What is a technique used to produce images of the brain while the subject is performing some sort of mental task?

Cognitive neuroscience investigates the emergence of cognitive function from the physical and chemical activity of neurons in the brain. Active representations in the brain consist of patterns of neural activity, processing takes place through the propagation of activity via excitatory and inhibitory connections, and learning and memory arise primarily through the modification of connections. The organization of cognitive processing in the brain is a matter of some debate, with some investigators arguing that particular brain regions carry out distinct, encapsulated processing operations while others suggest that each region contributes in a particular way to a distributed, interactive process. Several methods contribute to the discipline, including study of the effects of lesions on cognitive functions in humans and animals, study of neuronal activity during cognitive processes via single- and multielectrode recordings, study of human functional brain activity using non-invasive methods such as fMRI and PET, and use of computational models to formalize explicit hypotheses about the underlying mechanisms. Many of these methods have emerged in the 1980s and 1990s, and they are likely to continue to be enhanced and extended in the coming years. Thus, further breakthroughs in our understanding of the neural basis of cognition are likely. Cognitive neuroscience seeks to use observations from the study of the brain to help unravel the mechanisms of the mind. How do the chemical and electrical signals produced by neurons in the brain give rise to cognitive processes, such as perception, memory, understanding, insight, and reasoning? How is knowledge—including explicit knowledge of objects and events in the world and in one's own personal history, as well as implicit knowledge underlying acquired abilities such as skilled performance and language—represented in the physical structure of the brain, and how is it accessed and used in thought, perception, and action? These are among the central questions addressed by the field of cognitive Neuroscience.

Introduction

Cognitive neuroscience has emerged over the past decades as one of the most significant research directions in all of neuroscience and psychology. More recently, the scientific interface between cognitive neuroscience and human development, developmental cognitive neuroscience, has become a hot topic. Part of the reason for the renewed interest in relating brain development to cognitive, social, and emotional change comes from advances in methodology that allow hypotheses to be generated and tested more readily than previously. One set of tools relates to brain imaging – the generation of ‘functional’ maps of brain activity based on either changes in cerebral metabolism, blood flow, or electrical activity. The three brain-imaging techniques most commonly applied to development in normal children are event-related potentials (ERPs), functional magnetic resonance imaging (fMRI), and near infrared spectroscopy (NIRS). Another methodological advance is related to the emergence of techniques for formal computational modeling of neural networks and cognitive processes. Such models allow us to begin to bridge data on developmental neuroanatomy to data on behavioral changes associated with development. A third methodological innovation is the increasing trend for studying groups of developmental disorders (such as autism and Williams syndrome) together alongside typical development. Thus, rather than each syndrome being studied in isolation, comparisons between different typical and atypical trajectories of development are helping to reveal the extent and limits on plasticity.

Abstract

Cognitive neuroscience is currently finding itself as a marketing trend in occupational science, particularly in terms of workplace assessment and measurement. However, the field has historically had little to do with occupational applications and has generally remained focused on the clinical and academic relevance of its research. We will explore several frontiers where research methods and theory established in cognitive neuroscience are beginning to produce meaningful applications in the workplace. Given that this application is likely to be unfamiliar with many in brain research, we look to outline concepts that should be perceived as key considerations when applying innovative measures to the workplace. Relating to these key considerations are several challenges that currently stand in the way of cognitive neuroscience progressing beyond a marketing trend into a steadfast perspective in occupational science.

Cognitive neuroscience combines the experimental strategies of cognitive psychology with various techniques to actually examine how brain function supports mental activities. Leading this research in normal humans are the new techniques of functional brain imaging: positron emission tomography (PET) and magnetic resonance imaging (MRI). The roots of these techniques are traced to the century-long study of blood flow to the brain. For the future, the hope is to combine the spatial dimension of responses captured with functional MRI with the temporal dimension provided by encephalography (EEC) or magnetoencephalography (MEG).

Social cognitive neuroscience offers the promise of understanding human sociality by investigating the affective and cognitive operations of the human brain. This approach has already provided evidence of the automaticity of intergroup perception and evaluation, and illuminated how complex interactions between multiple neural component processes guide behavior. This article highlights the rapidity with which individuals distinguish different groups, their ability to do so without conscious awareness, and how these initial judgments may be modified by motives or goals. Ultimately, insights from social cognitive neuroscience will enhance our understanding of intergroup processing and lead to interventions that will reduce intergroup conflict.

Measurement Versus Manipulation

Cognitive neuroscience techniques can be divided into two main categories. Measurement techniques, as the name implies, measure changes in brain function while a research participant (human or animal) engages in some cognitive activity. A typical neuroeconomic experiment using a measurement technique might require the participant to make a series of simple decisions while the researchers record changes in neuronal firing or metabolic activity that might differ between, say, higher-value or lower-value choices. Measurement techniques are often described (sometimes derisively) as being “correlational” because they can show that signals from a brain region co-occur with a function of interest, but they cannot show that a region is necessary for that function.

Manipulation techniques, in contrast, examine how perturbations of the brain’s function – either by transiently changing neuronal firing rates or neurotransmitter levels or by permanently damaging tissue – change cognitive functions or behavior. Accordingly, manipulation techniques are sometimes called causal approaches. Neuroeconomists have used manipulation techniques to disrupt processing in specific regions, which in turn alters the choices people make (e.g., in interactive games).

This chapter follows this basic division, first introducing techniques that measure changes in brain function which track the variables within decision models, then considering techniques that change neural processing and also decision behavior. It is important to recognize that measurement and manipulation techniques provide distinct and complementary information about brain function. Cognitive neuroscience research progresses more quickly when measurement techniques establish links between brain structure and cognitive function and then manipulation techniques probe that relationship to improve inferences and models.

Introduction

Cognitive neuroscience is a discipline that attempts to determine the neural mechanisms underlying cognitive processes. Specifically, cognitive neuroscientists test hypotheses about brain-behavior relationships that can be organized along two conceptual domains: functional specialization—the idea that areas of the cerebral cortex represent functional modules that are specialized for a specific cognitive process—and functional integration—the idea that a cognitive process can be an emergent property of interactions among a network of brain regions, and thus that a brain region can play a different role across many functions.

Early investigations of brain–behavior relationships consisted of careful observation of individuals with neurological injury resulting in focal brain damage. The idea of functional specialization evolved from hypotheses that damage to a particular brain region was responsible for a given behavioral syndrome characterized by a precise neurological examination. For instance, the association of nonfluent aphasia with right-sided limb weakness implicated the left hemisphere as the site of language abilities. Moreover, upon the death of a patient with a neurological disorder, clinicopathological correlations provided information confirming the site of damage that caused a specific neurobehavioral syndrome. For example, in 1861 Paul Broca's observations of nonfluent aphasia in the setting of a damaged left inferior frontal gyrus cemented the belief that this brain region was critical for speech output. The introduction of structural brain imaging more than 100 years after Broca's observations, first with computerized tomography and later with magnetic resonance imaging (MRI), paved the way for more precise anatomical localization in the living patient of the cognitive deficits that develop after brain injury. The superb spatial resolution of structural neuroimaging has reduced the reliance on the infrequently obtained autopsy for making brain–behavior correlations.

Functional neuroimaging methodologies, broadly defined as techniques that measure brain activity, have expanded our ability to study the neural basis of cognitive processes. As technology has advanced, a number of these techniques have arisen, including single photon emission computed tomography (SPECT), positron emission tomography (PET), functional MRI (fMRI), and magnetoencephalography (MEG). Using these techniques, researchers can measure regional brain activity in healthy subjects while they perform cognitive tasks, and thereby link localized brain activity with specific behaviors. For example, functional neuroimaging studies have demonstrated that the left inferior frontal gyrus is consistently activated during the performance of speech production tasks in healthy individuals. Such findings from functional neuroimaging complement findings derived from observations of patients with focal brain damage. In order to provide the reader with the necessary background for understanding functional neuroimaging data, this article focuses on the principles underlying each of these techniques, as well as their relative strengths and weaknesses. Particular attention will be paid to fMRI, as it is perhaps the most widely-employed neuroimaging method. The article concludes with some of the novel ways in which these techniques are being combined in order to harness their complementary strengths to answer questions in cognitive neuroscience.

7.6 Unsupervised Learning Algorithms

Unsupervised learning algorithms are used to group cases based on similar attributes. These models also are referred to as self-organizing maps. Unsupervised models include clustering techniques and neural networks. Different algorithms use different strategies for dividing data into groups. Some methods are relatively straightforward, quickly dividing the cases into groups based on common attributes or some other similarity. The Two-Step clustering method differs somewhat in that an optimal number of clusters is determined in an initial pass through the data, based on certain statistical criteria. Group assignment is then made on a second pass through the data; hence the name “Two-Step.” Neural networks are more complicated than some of the other unsupervised learning algorithms and can yield results that are difficult to interpret.

Conclusion and Future Directions

Cognitive neuroscience studies largely indicate that human mathematical skills are rooted in nonverbal mechanisms. It is thus somewhat paradoxical that there appears to be a link between the development of arithmetic and reading skills in children (Durand et al., 2005; Hart et al., 2009; Hecht et al., 2001; Wilson et al., 2015). The goal of this chapter was to provide an overview of two possible explanations for this link and confront these explanations to available evidence from developmental cognitive neuroscience.

First, the triple-code model suggests that answers of the most familiar arithmetic facts may be increasingly retrieved from verbal-phonological codes as individuals become fluent (Dehaene et al., 2003). Yet, developmental cognitive neuroscience studies provide limited evidence for this assumption (Cho et al., 2012; Rivera et al., 2005; Rosenberg-Lee, Chang, Young, Wu, & Menon, 2011). One study suggests that increases of activity may be observed in the left temporal cortex as children become increasingly proficient with single-digit multiplication problems (Prado et al., 2014), but this effect appears to be task dependent. The contribution of verbal-phonological mechanisms to arithmetic learning may thus be restricted to facts that are explicitly learned by rote in school.

Second, it has been proposed that learning arithmetic and learning to read may both rely on the automatization of rules and procedures (Barrouillet & Thevenot, 2013; Thevenot et al., 2015; Uittenhove et al., 2015). Procedural memory systems may thus be critical to the acquisition of both skills, and impairments in procedural memory may be at the source of both dyscalculia and dyslexia. Not only does that hypothesis explain why one of the most consistent results obtained in developmental neuroimaging studies is an increase of activity in the parietal cortex (rather than in the left temporoparietal cortex) but also it highlights the importance of procedural memory for arithmetic learning. Because procedural memory has long been hypothesized to also be central to reading acquisition (Ullman, 2016, pp. 953–968), this idea may explain the link between arithmetic and reading acquisition and the comorbidity between dyslexia and dyscalculia (Wilson et al., 2015).

Overall, given the limited neurodevelopmental support for the idea that verbal-phonological processing underlies arithmetic learning in children, the procedural hypothesis is an interesting explanation for the link between arithmetic and reading skills. Of course, this does not mean that other domain-general factors cannot also account for that link in some children. For example, it is clear that both arithmetic and reading tasks involve working memory, attention, or cognitive control (Ashkenazi et al., 2013). It is possible that disruptions in these domain-general mechanisms may also lead to both reading and arithmetic impairments in children (as well as impairments in other skills). This is generally consistent with the idea that arithmetic learning involves a wide range of skills and that dyscalculia may be a heterogeneous disorder (Fias, Menon, & Szucs, 2013). Nevertheless, learning to read and learning arithmetic may both place important demands on procedural memory and automatization of skills, a factor that may explain a large part of the relationship between arithmetic and reading performance in children.