The time course of perceptual choice: the leaky, competing accumulator model.

By Marius Usher, James L. McClelland. 2001

2,176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

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Citations in the corpus

Neural computations associated with goal-directed choice. (2010, background): “The process terminates when the relative value action becomes sufficiently biased towards one of the choices [48,51–55].”
The neural basis of decision making. (2007, methodology): “…work has begun to explore algorithms to solve these decisions [e.g., the multiple sequential probability ratio test (MSPRT); McMillen & Holmes 2006] and how they might be implemented in the brain (Gurney & Bogacz 2006, Roe et al. 2001, Usher & McClelland 2001), but much work remains to be done.”
How the brain integrates costs and benefits during decision making (2010, background): “Importantly, sequential sampling models not only describe accuracy and response time in perceptual and memory tasks but can also describe value-based decisions (12, 13).”
Connectionist models of development (2003, background): “In models that simulate this gradual activation process, it is typically formalized as a differential equation relating the rate of change of some variable (such as the activation of some unit) to the inputs it is currently receiving from other units via weighted connections.”
Probabilistic decision making by slow reverberation in cortical circuits. (2002, positive): “This last point has been the focus of a recent but the effect is more limited in the model than in real work (Usher and McClelland, 2001) where a leakage is neurons.”
A model of interval timing by neural integration (2011, background): “Nevertheless, a single set of integrator mechanisms already used to model the neural basis of decision making through sequential sampling (cf. Bogacz et al., 2006; Gold and Shadlen, 2001; Usher and McClelland, 2001) can clearly be parameterized both to time intervals and to make perceptual decisions”
Neural basis of reinforcement learning and decision making. (2012, background): “Competitive interactions among different pools of recurrently connected neurons are a likely mechanism for this selection process (Usher & McClelland 2001, Wang 2002), but their neuroanatomical substrates remain poorly understood (Wang 2008).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, positive): “There is lateral inhibition within cortex, thus supporting competition between multiple available responses (e.g., Usher & McClelland, 2001).”
The Basal Ganglia and Cortex Implement Optimal Decision Making Between Alternative Actions (2007, methodology): “Section C.2 shows that if g g∗, the decision time does increase, but it never exceeds that of the UM model (Usher & McClelland, 2001) because as g → 0, the proposed model converges to an approximation of the UM model.”
The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. (2006, methodology): “To account for the fact that neural firing rates are never negative, Usher & McClelland (2001) assume that y1 and y2 are transformed via a threshold-linear activation function f(y) with f(y)=y for y≥0 and f(y)=0 for y<0; Equations 2.14 linearize this function by ignoring thresholding below 0.”
Symbols and quantities in parietal cortex: elements of a mathematical theory of number representation and manipulation (2007, background): “It plays a key role in modern models of decision making and response time distributions (for accessible syntheses, see Gold and Shadlen, 2001, 2002; Usher and McClelland, 2001; Smith and Ratcliff, 2004).”