The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

By Rafal Bogacz, E. Brown, Jeff Moehlis, Philip J. Holmes, J. Cohen. Psychology Review, 2006

1,823 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/bbdd99468b296c3be48601628ea164411a8b8c75

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Citations in the corpus

Decision making in recurrent neuronal circuits. (2008, methodology): “This model is easily extended to decisions with multiple alternatives (Usher and McClelland, 2001; McMillen and Holmes, 2006; Bogacz et al., 2007), which is not straightforward for the diffusion model (Niwa and Ditterich, 2008; Churchland et al.”
The Cognitive Neuroscience of Metacognition (2014, methodology): “Metacognition can reveal a hierarchical structure to cognition, because often metacognitive processes regulate lower-level cognitive processes [7].”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, background): “…(see Figure 4, top), accumulated evidence is represented in the form of a log posterior ratio, to which is added a log-likelihood ratio representing the evidence from each new observation (see Beck & Pouget, 2007; Bogacz et al., 2006; Gold & Shadlen, 2007; Rao, 2006; Ratcliff & McKoon, 2008).”
Symbols and quantities in parietal cortex: elements of a mathematical theory of number representation and manipulation (2007, background): “A theory for the optimal choice of parameter q, depending on the rewarding scheme and inter-trial interval, has been presented (Bogacz et al., 2006).”
Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making. (2012, background): “Following this strategy leads to the fastest decisions for a pre-set level of correctness, or – when optimizing this level – to maximizing reward rate [17,33].”
Collective decision-making (2017, background): “This has led to the proposal of a speed-value tradeoff [15] that underlies value-based decisions, rather than a speedaccuracy tradeoff as discussed in the preceding section, and considered in conventional two-alternative choice perceptual decisions [16].”
An Introduction to Model-Based Cognitive Neuroscience (2015, methodology): “To understand and communicate the notion of spreading activation, several analogies might be used: Some researchers liken the spread to electricity passing through wires [22] whereas others liken it to water passing through pipes (as one of us has done in lectures to undergraduates).”
The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure (2010, positive): “Under appropriate assumptions, it can be shown that the drift-diffusion model implements an optimal decision-making process that amounts to a sequential-likelihood ratio test (Bogacz, 2007; Bogacz et al., 2006; Gold & Shadlen, 2001, 2002; Gold & Shadlen, 2007).”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “The basic mechanism of the decision process is competition between independent options and underlies a large variety of decision models (55, 56, 441, 585, 623).”
A model of interval timing by neural integration (2011, methodology): “Cue categorization is a perceptual decision-making process that can be modeled successfully by drift-diffusion models implemented with nearly identical modeling assumptions (cf. Bogacz et al., 2006).”
CIDEr: Consensus-based image description evaluation (2014, methodology): “These annotation protocols for similarity may be understood as instantiations of 2AFC (two alternative forced choice) [3], a popular modality in psychophysics.”