Using functional neuroimaging to test theories of cognition: a selective survey of studies from 2007 to 2011 as a contribution to the Decade of the Mind Initiative

Neural Population Dynamics and Cognitive Function

Representations in the brain are encoded as patterns of activity of large populations of neurons. The science of population encoded representations, also known as parallel distributed processing (PDP), achieves neurological verisimilitude and has been able to account for a large number of cognitive phenomena in normal people, including reaction times (and reading latencies), stimulus recognition, the effect of stimulus salience on attention, perceptual invariance, simultaneous egocentric and allocentric visual processing, top-down/bottom-up processing, language errors, the effect of statistical regularities of experience, frequency, and age of acquisition, instantiation of rules and symbols, content addressable memory and the capacity for pattern completion, preservation of function in the face of noisy or distorted input, inference, parallel constraint satisfaction, the binding problem and gamma coherence, principles of hippocampal function, the location of knowledge in the brain, limitations in the scope and depth of knowledge acquired through experience, and Piagetian stages of cognitive development. PDP studies have been able to provide a coherent account for impairment in a variety of language functions resulting from stroke or dementia in a large number of languages and the phenomenon of graceful degradation observed in such studies. They have also made important contributions to our understanding of attention (including hemispatial neglect), emotional function, executive function, motor planning, visual processing, decision making, and neuroeconomics. The relationship of neural network population dynamics to electroencephalographic rhythms is starting to emerge. Nevertheless, PDP approaches have scarcely penetrated major areas of study of cognition, including neuropsychology and cognitive neuropsychology, as well as much of cognitive psychology. This article attempts to provide an overview of PDP principles and applications that addresses a broader audience.

A Non-cognitive Behavioral Model for Interpreting Functional Neuroimaging Studies

The dominant model for interpreting brain imaging experiments, which we refer to as the Standard Cognitive Model (SCM), assumes that the brain is organized in support of mental processes that control behavior. However, functional neuroimaging experiments of cognitive tasks have not shown clear anatomic segregation between mental processes originally proposed by this model. This failing has been blamed on limitations in imaging technology and non-linearity in the brain's implementation of these processes. However, the validity of the underlying cognitive models used to describe the brain has rarely been questioned or directly tested against imaging results. We propose an alternative model of brain function, that we term the Non-cognitive Behavioral Model (NBM), which correlates observed human behavior directly with measured brain activity without making assumptions about intervening cognitive processes. Our model derives from behavioral psychology but is extended to include brain activity, in addition to behavior, as observables. A further extension is the role of neuroplasticity, as opposed to innate cognitive processes, in developing the brain's support of cognitive behavior. We present the theoretical basis with which the SCM maps cognitive processes onto functional magnetic resonance and positron emission tomography images and compare and contrast with the NBM. We also describe how the NBM can be used experimentally to study how the brain supports behavior. Two applications are presented that support the usefulness of the NBM. In one, the NBM use of the total functional imaging signal (not just the differences between states) provides a stronger correlation of neural activity with the behavioral state of consciousness than the SCM approach in both anesthesia and coma. The second example reviews studies of facial and object recognition that provide evidence for the NBM proposal that neuroplasticity and experience play key roles in the brain's support of recognition and other behaviors. The conclusions regarding neuroplasticity are then generalized to explain the incomplete functional segregation observed in the application of the SCM to neuroimaging.

The cart before the horse: When cognitive neuroscience precedes cognitive neuropsychology

Cognitive neuropsychology (CN) has had an immense impact on the understanding of the normal cognitive processes underlying reading, spelling, spoken language comprehension and production, spatial attention, memory, visual perception, and orchestration of actions, through detailed analysis of behavioural performance by neurologically impaired individuals. However, there are other domains of cognition and communication that have rarely been investigated with this approach. Many cognitive neuropsychologists have extended their work in language, perception, or attention by turning to functional neuroimaging or lesion-symptom mapping to identify the neural mechanisms underlying the cognitive mechanisms they have identified. Another approach to extending one's research in CN is to apply the methodology to other cognitive functions. We briefly review the domains evaluated using methods of CN to develop cognitive architectures and computational models and the domains that have used functional neuroimaging and other brain mapping approaches in healthy controls to identify the neural substrates involved in cognitive tasks over the past 20 years. We argue that in some domains, neuroimaging studies have preceded the careful analysis of the cognitive processes underlying tasks that are studied, with the consequence that results are difficult to interpret. We use this analysis as the basis for discussing opportunities for expanding the field.

How fMRI Can Inform Cognitive Theories

How can functional magnetic resonance imaging (fMRI) advance cognitive theory? Some have argued that fMRI can do little beyond localizing brain regions that carry out certain cognitive functions (and may not even be able to do that). However, in this article, we argue that fMRI can inform theories of cognition by helping to answer at least four distinct kinds of questions. Which mental functions are performed in brain regions specialized for just that function (and which are performed in more general-purpose brain machinery)? When fMRI markers of a particular Mental Process X are found, is Mental Process X engaged when people perform Task Y? How distinct are the representations of different stimulus classes? Do specific pairs of tasks engage common or distinct processing mechanisms? Thus, fMRI data can be used to address theoretical debates that have nothing to do with where in the brain a particular process is carried out.

How Can Functional Neuroimaging Inform Cognitive Theories?

Work on functional neuroimaging of cognition falls into two categories. The first aims at localizing specific cognitive subsystems in specific brain regions. In this research, the cognitive subsystems in question need to be defined independently of the neuroimaging data because the interpretation of the data requires such definition; so functional neuroimaging is informed by cognitive theories rather than informing them. The second category uses neuroimaging data to test cognitive theories. As cognitive theories are expressed in cognitive terms, such theories have to be embellished by explicit proposals about relationships between cognition and the brain if they are to become capable of generating predictions about the results of experiments that use functional neuroimaging. Whether functional neuroimaging can succeed in informing a cognitive theory depends critically upon the plausibility of such supplementary proposals. It is also critical to avoid the "consistency fallacy." When neuroimaging data from an experiment are consistent with predictions from a particular cognitive theory, this cannot be offered as evidence in support of that theory unless it can be shown that there were possible other outcomes of the experiment that are inconsistent with the theory-outcomes that would have falsified predictions from the theory had they been obtained.

The modern search for the Holy Grail: is neuroscience a solution?

Neuroscience has become prevalent in recent years; nevertheless, its value in the examination of psychological and philosophical phenomena is still a matter of debate. The examples reviewed here suggest that neuroscientific tools can be significant in the investigation of such complex phenomena. In this article, we argue that it is important to study concepts that do not have a clear characterization and emphasize the role of neuroscience in this quest for knowledge. The data reviewed here suggest that neuroscience may (1) enrich our knowledge; (2) outline the nature of an explanation; and (3) lead to substantial empirical and theoretical discoveries. To that end, we review work on hedonia and eudaimonia in the fields of neuroscience, psychology, and philosophy. These studies demonstrate the importance of neuroscientific tools in the investigation of phenomena that are difficult to define using other methods.

There and up again: on the uses and misuses of neuroimaging in psychology

The aim of this article is to discuss the conditions under which functional neuroimaging can contribute to the study of higher cognition. We begin by presenting two case studies--on moral and economic decision making--which will help us identify and examine one of the main ways in which neuroimaging can help advance the study of higher cognition. We agree with critics that functional magnetic resonance imaging (fMRI) studies seldom "refine" or "confirm" particular psychological hypotheses, or even provide details of the neural implementation of cognitive functions. However, we suggest that neuroimaging can support psychology in a different way--namely, by selecting among competing hypotheses of the cognitive mechanisms underlying some mental function. One of the main ways in which neuroimaging can be used for hypothesis selection is via reverse inferences, which we here examine in detail. Despite frequent claims to the contrary, we argue that successful reverse inferences do not assume any strong or objectionable form of reductionism or functional locationism. Moreover, our discussion illustrates that reverse inferences can be successful at early stages of psychological theorizing, when models of the cognitive mechanisms are only partially developed.