Matrix analysis
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A mapping model of bow effects in absolute identification
Journal of Mathematical Psychology
Stochastic dynamic models of response time and accuracy: a foundational primer
Journal of Mathematical Psychology
Hick's law in a stochastic race model with speed-accuracy tradeoffs
Journal of Mathematical Psychology
Rapid decision threshold modulation by reward rate in a neural network
Neural Networks - 2006 Special issue: Neurobiology of decision making
Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer Series in Statistics)
Multihypothesis sequential probability ratio tests .I. Asymptotic optimality
IEEE Transactions on Information Theory
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We consider the effects of signal sharpness or acuity on the performance of neural models of decision making. In these models, a vector of signals is presented, and the subject must decide which of the elements of the vector is the largest. McMillen and Holmes (2006) derived asymptotically optimal tests under the assumption that the elements of the signal vector were all equal except one. In this letter, we consider the case of signals spread around a peak. The acuity is a measure of how strongly peaked the signal is. We find that the optimal test is one in which the detectors are passed through an output layer that encodes knowledge of the possible shapes of the incoming signals. The incorporation of such an output layer can lead to significant improvements in decision-making tasks.