Prefrontal Cortex and the Flexibility of Cognitive Control : Rules Without Symbols

A hallmark of human behavior is the flexibility of cognitive control, which is believed to depend on prefrontal cortex (PFC). Previous work has suggested that specific PFC mechanisms, including active maintenance of rule or goal representations, adaptive gating of these representations, and the top-down biasing of processing in other cortical areas, are critical for flexible cognitive control. Neural network models have demonstrated that these mechanisms are sufficient to accurately simulate flexible cognitive control in a variety of tasks. However, these models have not addressed a fundamental question: How is information represented in PFC and, critically, how does this develop? Here, we show that PFC-specific mechanisms interact with the breadth of training experience to produce abstract, rule-like representations that support generalization of performance in novel task circumstances. We also show that these rule-like representations support patterns of performance characteristic of neurologically intact and frontally-damaged people on benchmark tasks of cognitive control (Stroop and WCST). Although the rule-like representations that developed in our model of PFC support flexible cognitive control, they do so in a way that is fundamentally different from symbolic representations characteristic of more traditional unified theories of cognition. Therefore, these results bear on both the organization and development of PFC at the neurobiogical level, as well as debates regarding the nature of cognitive flexibility and rule-like behavior at the psychological level. † corresponding authors: oreilly@psych.colorado. edu, 345 UCB, Boulder, CO 80309-0345 or jdc@princeton.edu, Green Hall, Princeton, NJ 08544. Supported by ONR grants N00014-00-1-0246 and N00014-03-1-0428, and NIH grants MH64445. Last authorship reflects equal contribution; order was determined by a flip of coin. We thank Tim Curran, Michael Frank, Tom Hazy, Dave Jilk, Ken Norman, Yuko Munakata and members of the CCN lab for helpful comments. One of the fundamental faculties that distinguishes human cognition and behavior from other species is our capacity for cognitive control: the ability to behave in accord with rules, goals, or intentions, even when this runs counter to reflexive or otherwise highly compelling competing responses (e.g., the ability to keep typing rather than scratch a mosquito bite). A hallmark of the human capacity for cognitive control is its remarkable flexibility. This flexibility is exhibited in many ways, including the ability to perform a familiar task in novel environments (generalization; e.g., playing cards with a novel type of deck with new players in a new location), and the ability to rapidly configure the behavior needed to perform arbitrary new tasks with relatively little or no prior training (generativity; e.g., playing a new card game for the first time). These abilities pose a major challenge to theories of cognitive control: How can they be explained in terms of selforganizing mechanisms (that develop on their own, without recourse to unexplained sources of influence or intelligence; i.e., a “homunculus”), and extensive but not infinite computational resources (1)? Classic unified theories of cognition, such as ACT-R and SOAR (2), have addressed this issue by assuming that human cognition has the capacity for symbol processing — that is, the ability to execute procedures for any values of the relevant variables, and to arbitrarily combine these procedures in new ways. While such theories provide a powerful account of human behavior, they fail to answer several important questions. First, most models based on these theories pre-specify the basic set of procedures that are available, without explaining how these are acquired. Thus, the modeler is required to imbue such models with much of the intelligence that is to be explained. Second, one of the great mysteries of neuroscience is how coordinated, goal directed behavior can arise from the highly distributed activity of billions of neurons. Symbolic models have not addressed this question. In this paper, we explore an alternative explanation for the origin of cognitive flexibility that addresses both of these questions: how neural mechanisms can support flexibility of cognitive control, and how this can develop through experience. Our approach builds on the idea that a primary function of prefrontal cortex (PFC) is to support rule-like representations that guide behavior (3). Unlike symbolic models, these rule-like representations do not support arbitrary symbol binding operations, but rather are more like standard neural network representations, in that they derive their meaning from specific connections between neurons in PFC that support these representations and neurons in other parts of the brain responsible for carrying out behavior. By virtue of these connections, sustained patterns of activity in PFC can modulate processing in other parts of the brain, and thereby govern rule-like behavior. Flexibility of control derives from having a suitably general set of representations in PFC, and the ability to activate these in task-appropriate contexts. Extensive neurobiological and theoretical work supports this view. They indicate that PFC exhibits: 1. Robust maintenance of patterns of neural activity over time and against interference from distracting inputs, so that currently relevant information can be actively held in working memory (4, 5, 6). 2. Adaptive gating of these activity patterns, allowing them to be rapidly and flexibly updated by dynamically switching between robust maintenance and rapid updating (7, 8, 9). 3. Extensive interconnectivity with other cortical areas (e.g., in posterior cortex) (4) allowing patterns of activity in PFC to modulate processing in pathways responsible for task execution (10, 11). Neural network models that simulate these PFC mechanisms can explain performance in a wide range of tasks that rely on cognitive control (10, 12 13, 14), the ability to flexibly shift task sets (15, 16, 17, 14), as well as behavioral deficits in such tasks associated with disturbances in PFC (18, 9, 14). A critical feature of these models is the incorporation of rule-like representations in PFC that allow the PFC to exercise control over processing, and to rapidly shift between tasks. This is consistent with accumulating neurobiological data indicating that PFC neurons can represent the goals of a task, or the rules needed to perform it (3). A fundamental limitation of existing models, however, is their use of prespecified representations in PFC, that are designed “by hand” to perform the particular tasks of interest. Thus, like symbolic models, these models do not address the question of how these representations develop, and acquire the properties needed to support the flexible control of behavior. We propose that the development of representations in PFC relies critically on an interaction between the functional specializations that are characteristic of PFC, as reviewed above, and exposure to a broad range of tasks during learning. This is consistent with the protracted course of development of cognitive control, and the human PFC itself (19). In this article, we describe modeling results that support this proposal, by demonstrating that when a neural network with mechanisms specific to PFC is trained on several different tasks, it develops abstract, rulelike representations that support performance in novel task contexts. Networks without a PFC, or with more limited training, fail to exhibit this form of flexibility. We present the results of two simulation experiments