We consider neurally-based models for decision-making in the presence of noisy incoming data. The two-alternative forced-choice task has been extensively studied, and in that case it is known that mutually-inhibited leaky integrators in which leakage and inhibition balance can closely approximate a drift-diffusion process that is the continuum limit of the optimal sequential probability ratio test (SPRT). Here we study the performance of neural integrators in n ≥ 2 alternative choice tasks and relate them to a multihypothesis sequential probability ratio test (MSPRT) that is asymptotically optimal in the limit of vanishing error rates. While a simple race model can implement this ‘max-vs-next ’ MSPRT, it requires an additional computational layer, while absolute threshold crossing tests do not require such a layer. Race models with absolute thresholds perform relatively poorly, but we show that a balanced leaky accumulator model with an absolute crossing criterion can approximate a ‘max-vs-ave ’ test that is intermediate in performance between the absolute and max-vs-next tests. We consider free and fixed time response protocols, and show that the resulting mean reaction times under the former and decision times for fixed accuracy under the latter obey versions of Hick’s law in the low error rate range, and we interpret this in terms of information gained. Specifically, we derive relationships of the forms log(n − 1), log(n), or log(n + 1) depending on error rates, signal-to-noise ratio, and the test itself. We focus on linearized models, but also consider nonlinear effects of neural activities (firing rates) that are bounded below and show how they modify Hick’s law. KEYWORDS: leaky accumulator, drift-diffusion model, neural network, Hick’s law, multihypothesis sequential test, sequential ratio test.

We study the synthesis of neural coding, selective attention and perceptual decision making. A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and topdown attentional priors, leading to sound perceptual discrimination. The model offers an explicit explanation for the experimentally observed modulation that prior information in one stimulus feature (location) can have on an independent feature (orientation). The network’s intermediate levels of representation instantiate known physiological properties of visual cortical neurons. The model also illustrates a possible reconciliation of cortical and neuromodulatory representations of uncertainty. 1

The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition—cognitively bounded rational analysis—that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.

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.

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The concept of error detection plays a central role in theories of executive control. In this article, the authors present a mechanism that can rapidly detect errors in speeded response time tasks. This error monitor assigns values to the output of cognitive processes involved in stimulus categorization and response generation and detects errors by identifying states of the system associated with negative value. The mechanism is formalized in a computational model based on a recent theoretical framework for understanding error processing in humans (C. B. Holroyd & M. G. H. Coles, 2002). The model is used to simulate behavioral and event-related brain potential data in a speeded response time task, and the results of the simulation are compared with empirical data. Frontal parts of the brain, including the prefrontal cortex (Luria, 1973; Stuss & Knight, 2002), the anterior cingulate cortex (Devinsky, Morrell, & Vogt, 1995; Posner & DiGirolamo, 1998), and their connections with the basal ganglia (L. L. Brown, Schneider, & Lidsky, 1997; Cummings, 1993), are thought to compose an executive system for cognitive control. The functions of this system are thought to include setting high-level goals, directing other

A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994;

perceptual decision making

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