ABSTRACT
Cognitive slowing is a feature of problem solving performance by Parkinson’s patients. Here we present a computational model of the Tower of London problem solving task that provides a straightforward explanation of this latency impairment. The model is a neural network consisting of a set of idealized neurons whose activation levels are continuously varying quantities ranging between 0 and 1, organized hierarchically into a collection of structured, columnar assemblies. The model differs from many neural network models in that it intentionally approximates the discrete stages of processing and rule-based computation of production systems. It differs from many symbolic models in that it does not assume an instantaneous transition between different stages of processing and does not rely on a central, system-wide clock signal. As a result, the model faces timing problems that can prevent it from approximating an idealized discrete-time system closely enough to perform symbolic computation. The columnar assembly provides a solution to these problems by imposing a direction on the flow of activation between units and by controlling the rate of that flow. Columns are proposed to play the role of distributed timers implemented in cortical columns and frontostriatal loop circuits in the brain. Dopamine depletion in Parkinson’s is proposed to reduce the rate of information transmission through a frontostriatal timer circuit critical for the generation of subgoal representations in prefrontal cortex. The model fits latency impairments in problem-solving by Parkinson’s patients relative to controls and predicts that only problems that require the generation of subgoals will produce a significant latency impairment in Parkinson’s patients.
