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The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced choice tasks
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Psychology and neurobiology of simple decisions
- Trends Neurosci
, 2004
"... Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavi ..."
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Cited by 213 (7 self)
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Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data. The question of how two-alternative decisions are made is an important one for neuroscience and psychology alike because of the pivotal role played by decision making in translating perception and cognition into action. This translation brings encoded stimulus information into contact with the behavioral intention of the decision maker to produce a goal-directed act. Psychology has a long history of decision-making research that has resulted in detailed mathematical models of underlying processes [1,2] but only recently has it become possible to observe the neural correlates of these processes directly in awake behaving monkeys. To study processes involved in simple two-choice decisions, neuroscientists have used an analog of the two-choice response-time (RT) task from psychology, in which monkeys make saccadic eye movements to indicate their decisions about visual stimuli. Recordings from cells in premotor areas of the frontal lobe and the posterior parietal cortex have shown that the time-course of activity in these cells corresponds well with that of behavioral eye movement decisions Neural correlates of simple two-choice decisions Neural activity linked to eye movement decisions has been recorded in several visual tasks The view that neural firing rate can be understood as a correlate of the behavioral decision process From neurons to sequential-sampling models The picture that emerges from these findings is strikingly consistent with statistical decision models that have been
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
, 2008
"... The diffusion decision model allows detailed explanations of behavior in two-choice 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 203 (25 self)
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The diffusion decision model allows detailed explanations of behavior in two-choice 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.
A comparison of sequential sampling models for two-choice reaction time
- Psychological Review
, 2004
"... The authors evaluated 4 sequential sampling models for 2-choice decisions—the Wiener diffusion, Ornstein–Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models—by fitting them to the response time (RT) distributions and accuracy data from 3 experiments. Each of the models was augmented wi ..."
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Cited by 197 (33 self)
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The authors evaluated 4 sequential sampling models for 2-choice decisions—the Wiener diffusion, Ornstein–Uhlenbeck (OU) diffusion, accumulator, and Poisson counter models—by fitting them to the response time (RT) distributions and accuracy data from 3 experiments. Each of the models was augmented with assumptions of variability across trials in the rate of accumulation of evidence from stimuli, the values of response criteria, and the value of base RT across trials. Although there was substantial model mimicry, empirical conditions were identified under which the models make discriminably different predictions. The best accounts of the data were provided by the Wiener diffusion model, the OU model with small-to-moderate decay, and the accumulator model with long-tailed (exponential) distributions of criteria, although the last was unable to produce error RTs shorter than correct RTs. The relationship between these models and 3 recent, neurally inspired models was also examined. A common feature of many tasks studied by experimental psychologists is that they involve a simple decision about some feature of a stimulus that is expressed as a choice between two alternative responses. Because decisions of this type are so fundamental to theory development and evaluation, their study has been
Tutorial on maximum likelihood estimation.
- Journal of Mathematical Psychology,
, 2003
"... Abstract In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a ..."
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Cited by 115 (3 self)
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Abstract In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles. r
A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of simple two-choice decisions
- Journal of Neurophysiology
, 2003
"... of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J Neurophysiol 90: 1392–1407, 2003. First published May 21, 2003; 10.1152/jn.01049.2002. Recently, models in psychology have been shown capable of accounting for the full range of behavi ..."
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Cited by 102 (16 self)
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of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J Neurophysiol 90: 1392–1407, 2003. First published May 21, 2003; 10.1152/jn.01049.2002. Recently, models in psychology have been shown capable of accounting for the full range of behavioral data from simple two-choice decision tasks: mean reaction times for correct and error responses, accuracy, and the reaction time distributions for correct and error responses. At the same time, recent data from neural recordings have allowed investigation of the neural systems that implement such decisions. In the experiment presented here, neural recordings were obtained from superior colliculus prelude/buildup cells in two monkeys while they performed a two-choice task that has been used in humans for testing psychological models of the decision process. The best-developed psychological model, the diffusion model, and a competing model, the Poisson counter model, were explicitly fit to the behavioral data. The pattern of activity shown in the prelude/buildup cells, including the point at which response choices were discriminated, was matched by the evidence accumulation process predicted from the diffusion model using the parameters from the fits to the behavioral data but not by the Poisson counter model. These results suggest that prelude/buildup cells in the superior colliculus, or cells in circuits in which the superior colliculus cells participate, implement a diffusion decision process or a variant of the diffusion process.
Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychol Bull.
, 2005
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Cortical mechanisms of action selection: the affordance competition hypothesis.
- Philos Trans R Soc Lond B Biol Sci
, 2007
"... At every moment, the natural world presents animals with two fundamental pragmatic problems: selection between actions that are currently possible and specification of the parameters or metrics of those actions. It is commonly suggested that the brain addresses these by first constructing represent ..."
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Cited by 96 (3 self)
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At every moment, the natural world presents animals with two fundamental pragmatic problems: selection between actions that are currently possible and specification of the parameters or metrics of those actions. It is commonly suggested that the brain addresses these by first constructing representations of the world on which to build knowledge and make a decision, and then by computing and executing an action plan. However, neurophysiological data argue against this serial viewpoint. In contrast, it is proposed here that the brain processes sensory information to specify, in parallel, several potential actions that are currently available. These potential actions compete against each other for further processing, while information is collected to bias this competition until a single response is selected. The hypothesis suggests that the dorsal visual system specifies actions which compete against each other within the fronto-parietal cortex, while a variety of biasing influences are provided by prefrontal regions and the basal ganglia. A computational model is described, which illustrates how this competition may take place in the cerebral cortex. Simulations of the model capture qualitative features of neurophysiological data and reproduce various behavioural phenomena.
Individual and Developmental Differences in Semantic Priming: Empirical and Computational Support for a Single-Mechanism Account of Lexical Processing
, 2000
"... the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network mod ..."
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Cited by 95 (11 self)
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the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing. Introduction It is well-established that people are faster and more accurate to read a word (e.g., BUTTER) when it is preceded by a related word (e.g., BREAD) compared with when it is preceded by an unrelated word (e.g., DOCTOR; The research was supported by an NIMH FIRST award (MH55628) to the first author and by NIMH Training Grant 5T32MH19102 and NICHD Grant 80258. The computational simulation was run using customized software written within the Xerion simulator (version 3.1) developed by Drew van Camp, Tony Plate, and Geoff Hinton at the Univers
Bayesian computation in recurrent neural circuits
- Neural Computation
, 2004
"... 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 implem ..."
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Cited by 94 (4 self)
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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
