Two connectionist frameworks, GRAIN (McClelland, 1993) and BSB (Anderson, 1991), and the diffusion model (Ratcliff, 1978) were evaluated using data from a signal detection task. Subjects were asked to choose one of two possible responses to a stimulus and were provided feedback about whether the choice was correct. The dependent variables included response probabilities, reaction times for correct and error responses, and reaction time distributions, and the independent variables were stimulus value, stimulus probability, and lag from an abrupt switch in stimulus probability. The diffusion model accounted for all aspects of the asymptotic data, including error reaction times, which had previously been a problem. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model very similar to the diffusion model. The connectionist learning mechanisms were unable to account for initial learning or abrupt changes in stimulus probability. The results provide an advance in the development of the diffusion model and show that the long tradition of reaction time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

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A large number of human psychophysical results have been successfully explained in recent years using Bayesian models. However, the neural implementation of such mod-els remains largely unclear. In this paper, we show that a network architecture com-monly used to model the cerebral cortex can implement Bayesian inference for an arbi-trary hidden Markov model. We illustrate the approach using an orientation discrimi-nation task and a visual motion detection task. In the case of orientation discrimination, we show that the model network can infer the posterior distribution over orientations and correctly estimate stimulus orientation in the presence of significant noise. In the case of motion detection, we show that the resulting model network exhibits direction selectivity and correctly computes the posterior probabilities over motion direction and position. When used to solve the well-known random dots motion discrimination task, the model generates responses that mimic the activities of evidence-accumulating neu-rons in cortical areas LIP and FEF. The framework introduced in the paper posits a new interpretation of cortical activities in terms of log posterior probabilities of stimuli occurring in the natural world. 1 1

this article shouldbe addressed to T. S. Braver, Department of Psychology, Washington University, Campus Box 1125, One BrookingsDrive, St. Louis, MO 63130 (e-mail: tbraver@artsci.wustl.edu)

This work presents a modular neural-network model (based on reinforcement-leaming actor-eritic methods) that tries to capture some of the most-relevant known aspects of the role that basal ganglia play in learning and selecting motor behavior related to different goals. In particular some simulations with the model show that basal ganglia selects "chunks" of behaviour whose "details" are specified by direct sensory-motor pathways, and how emergent modularity can help to deal with multiple behavioral tasks. A "top-down" approach is adopted. The starting point is the adaptive interaction of a (simulated) organism with the environment, and its capacity to learn. Then an attempt is made to implement these functions with neural architectures and mechanisms that have a neuroanatomical and neurophysiological empirical foundation.

Almost all models of simple and choice response time (RT) employ a stochastic (i.e., variable within trial) accumulation decision process. In order to account for the relationship between correct and error choice RT, it has been found necessary to also include between trial variability in the starting point and/or the rate of accumulation, both in linear (Ratcliff & Rouder, 1998) and nonlinear (Usher & McClelland, 2001) stochastic models. We show that a ballistic (i.e., deterministic within trial) model using a simplified version of Usher and McClellands nonlinear accumulation process, and assuming only between trial variability in the rate and starting point of accumulation, is not only capable of accounting for the relationship between error and correct RT, but can also model other benchmark behavioural phenomena, such as RT distribution and speed-accuracy trade off. We successfully fit our ballistic model to Ratcliff and Rouders data, which exhibit many of the benchmark phenomena. Even for fast and easy decisions, a simple summation of sensory and motor transduction delays and conduction times in the nervous system cannot account for the duration and variability of reaction times. (Hanes & Schall, 1996, p.427). The slowness and variability of response time (RT) has been almost universally explained by decision processes involving stochastic accumulation of information. Stochastic models assume that the accumulated information varies randomly from moment to moment during the decision process. RT is relatively slow because a criterion amount of information must be accumulated before a response is made, and RT ...

Cortical organization of vision appears to be divided into perception and action. Models of vision have generally assumed that eye movements serve to select a scene for perception, so action and perception are sequential processes. We suggest a less distinct separation. According to our model, occulomotor areas responsible for planning an eye movement, such as the frontal eye field, influence perception prior to the eye movement. The activity reflecting the planning of an eye movement reenters the ventral pathway and sensitizes all cells within the movement field so the planned action determines perception. We demonstrate the performance of the computational model in a visual search task that demands an eye movement toward a target.

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Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piéron’s Law, a power function dependence of response time on stimulus strength. The theory’s analytic chronometric function allows one to extend theories of accuracy to response time.

Psychologists and neuroscientists have had a long-standing interest in the P3, a prominent component of the event-related brain potential. This review aims to integrate knowledge regarding the neural basis of the P3 and to elucidate its functional role in information processing. The authors review evidence suggesting that the P3 reflects phasic activity of the neuromodulatory locus coeruleus–norepinephrine (LC-NE) system. They discuss the P3 literature in the light of empirical findings and a recent theory regarding the information-processing function of the LC-NE phasic response. The theoretical framework emerging from this research synthesis suggests that the P3 reflects the response of the LC-NE system to the outcome of internal decision-making processes and the consequent effects of noradrenergic potentiation of information processing.