Cognitive control, the ability to flexibly and selectively process information in the service of higher-level goals, is essential to daily functioning. However, despite the burgeoning research in this domain, much remains to be understood regarding its underlying neurocomputational mechanisms. This chapter highlights several prominent models that have made significant progress towards understanding the core principles of neural information processing and computation that are central to cognitive control. Neural network models are reviewed that characterize: (1) how tasks are represented, updated, and learned (e.g., attentional control, task-switching, structure learning); and (2) how cognitive control is evaluated and allocated based on assessments of demand (e.g., conflict monitoring, outcome prediction, and expected value of control). This brief survey of influential theoretical models provides an important foundational introduction into the primary mechanisms of cognitive control, and concludes with key open questions and future directions aimed at developing a fuller understanding of this domain.

Schizophrenia is a complex and debilitating psychiatric disorder that affects approximately one percent of the population. Lay conceptions of schizophrenia typically focus on symptoms such as hallucinations, delusions, and disorganized speech, which are often considered the hallmark features of this disorder. However, clinicians, researchers, and theorists have long noted that individuals with schizophrenia also commonly suffer from disturbances in memory and cognition, often severely so. Interestingly, recent research suggests that disturbances in social and occupational functioning in individuals with schizophrenia may be more influenced by the severity of their cognitive deficits than the severity of symptoms such as hallucinations and delusions (Green, Kern, Braff, & Mintz, 2000). Such findings have led to a resurgence of interest in identifying the nature of cognitive abnormalities in schizophrenia. A close examination of the types of symptoms and cognitive disturbances displayed by individuals suggests that many of these disturbances appear to reflect an inability to control or regulate their cognitive and emotional states. In this chapter, we review the evidence that one of the core cognitive disturbances in schizophrenia is a deficit in one or more components of executive function, which leads to disturbances in the ability to appropriately regulate thoughts and behavior in accordance with internal goals. More specifically, we suggest that individuals with schizophrenia suffer from a disturbance in a specific type of executive control process that we refer to as a deficit in the ability to represent and maintain context.

FIVE CENTRAL FEATURES OF THE MODEL

We define working memory as controlled processing involving active maintenance and/or rapid learning, where controlled processing is an emergent property of the dynamic interactions of multiple brain systems, but the prefrontal cortex (PFC) and hippocampus (HCMP) are especially influential owing to their specialized processing abilities and their privileged locations within the processing hierarchy (both the PFC and HCMP are well connected with a wide range of brain areas, allowing them to influence behavior at a global level). The specific features of our model include:

1. (1) A PFC specialized for active maintenance of internal contextual information that is dynamically updated and self-regulated, allowing it to bias (control) ongoing processing according to maintained information (e.g., goals, instructions, partial products).

2. (2) An HCMP specialized for rapid learning of arbitrary information, which can be recalled in the service of controlled processing, whereas the posterior perceptual and motor cortex (PMC) exhibits slow, long-term learning that can efficiently represent accumulated knowledge and skills.

3. (3) Control that emerges from interacting systems (PFC, HCMP, and PMC).

4. (4) Dimensions that define continua of specialization in different brain systems: for example, robust active maintenance, fast versus slow learning.

5. (5) Integration of biological and computational principles.

Working memory is an intuitively appealing theoretical construct – perhaps deceptively so.