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243
The Diffusion Decision Model: Theory and Data for TwoChoice Decision Tasks
, 2008
"... The diffusion decision model allows detailed explanations of behavior in twochoice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three exp ..."
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Cited by 187 (24 self)
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The diffusion decision model allows detailed explanations of behavior in twochoice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.
Decision making, the P3, and the locus coeruleusnorepinephrine system
 Psychol Bull
, 2005
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Shortlist B: A Bayesian model of continuous speech recognition
 Psychological Review
, 2008
"... A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; ..."
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Cited by 80 (5 self)
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A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994;
The dynamics of choice among multiple alternatives
 Journal of Mathematical Psychology
, 2006
"... We consider neurallybased models for decisionmaking in the presence of noisy incoming data. The twoalternative forcedchoice task has been extensively studied, and in that case it is known that mutuallyinhibited leaky integrators in which leakage and inhibition balance can closely approximate a ..."
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Cited by 52 (8 self)
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We consider neurallybased models for decisionmaking in the presence of noisy incoming data. The twoalternative forcedchoice task has been extensively studied, and in that case it is known that mutuallyinhibited leaky integrators in which leakage and inhibition balance can closely approximate a driftdiffusion 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 ‘maxvsnext ’ 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 ‘maxvsave ’ test that is intermediate in performance between the absolute and maxvsnext 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, signaltonoise 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, driftdiffusion model, neural network, Hick’s law, multihypothesis sequential test, sequential ratio test.
A contextbased theory of recency and contiguity in free recall
 Psychological Review
, 2008
"... The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana’s (2002a) temporal context model and M. Usher and J. L. McClelland’s (2001) leakyaccumulator decision model. In this model, contextual drift gives rise to both shortterm and longterm recency effects, and ..."
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Cited by 42 (19 self)
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The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana’s (2002a) temporal context model and M. Usher and J. L. McClelland’s (2001) leakyaccumulator decision model. In this model, contextual drift gives rise to both shortterm and longterm recency effects, and contextual retrieval gives rise to shortterm and longterm contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short and longterm recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.
Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action
 Psychological Review
, 2009
"... 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 th ..."
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Cited by 38 (13 self)
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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) dualtask performance and the development and analysis of a new theory of ordered dualtask 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 dualtask 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.
A mechanism for error detection in speeded response time tasks
 Journal of Experimental Psychology: General
, 2005
"... 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 categoriza ..."
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Cited by 37 (12 self)
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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 eventrelated 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 highlevel goals, directing other
Inference, attention, and decision in a Bayesian neural architecture
 Advances in Neural Information Processing Systems 17
, 2005
"... 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 ex ..."
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Cited by 35 (5 self)
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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
Simple neural networks that optimize decisions
 Int. J. Bifurc. Chaos
"... We review simple connectionist and ¯ring rate models for mutually inhibiting pools of neurons that discriminate between pairs of stimuli. Both are twodimensional nonlinear stochastic ordinary di®erential equations, and although they di®er in how inputs and stimuli enter, we show that they are equi ..."
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Cited by 33 (15 self)
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We review simple connectionist and ¯ring rate models for mutually inhibiting pools of neurons that discriminate between pairs of stimuli. Both are twodimensional nonlinear stochastic ordinary di®erential equations, and although they di®er in how inputs and stimuli enter, we show that they are equivalent under state variable and parameter coordinate changes. A key parameter is gain: the maximum slope of the sigmoidal activation function. We develop piecewiselinear and purely linear models, and onedimensional reductions to OrnsteinUhlenbeck processes that can be viewed as linear ¯lters, and show that reaction time and error rate statistics are well approximated by these simpler models. We then pose and solve the optimal gain problem for the OrnsteinUhlenbeck processes, ¯nding explicit gain schedules that minimize error rates for timevarying stimuli. We relate these to time courses of norepinephrine release in cortical areas, and argue that transient ¯ring rate changes in the brainstem nucleus locus coeruleus may be responsible for approximate gain optimization.