- Poster presentation
- Open Access
Speed and accuracy in decision making: input correlations and performance
© Cain and Shea-Brown; licensee BioMed Central Ltd. 2012
- Published: 16 July 2012
- Decision Making
- Firing Rate
- Sensory Neuron
- Decision Task
- Solid Curf
In models of perceptual decision making, evidence for and against different task alternatives is encoded in the firing rates of sensory neurons, and a downstream computation or circuit integrates this evidence over time and makes a decision. What are the consequences of correlations among the sensory neurons for performance in the decision task? We answer this question for three models of decision-making: exact spike integration, the sequential probability ratio test, and a physiologically based model of decision making consisting of 2000 model neurons . Others [2, 3] have have previously reported that without correlations, spike integration implements optimal inference via the SPRT  by accumulating the log of the likelihood ratio for two alternatives until a threshold is reached. We extend these results to incorporate correlations among the sensory neurons. We compare the performance of each decision making model by computing the accuracy that they produce at a given mean reaction time. Because each decision model receives identically formatted inputs, our approach is to directly compare each model by examining mean reaction time and accuracy together, across the frontier of average performances attainable by a change in decision making threshold.
This work has been supported by a Career Award at the Scientific Interface from the Burroughs-Wellcome Fund, and in part by the University of Washington eScience Institute.
- Wang XJ: Probabilistic decision making by slow reverberation in cortical circuits. Neuron. 2002, 36 (5): 955-968. 10.1016/S0896-6273(02)01092-9.View ArticlePubMedGoogle Scholar
- Zhang J, Bogacz R: Optimal Decision Making on the Basis of Evidence Represented in Spike Trains. Neural Computation. 2010, 22 (5): 1113-1148. 10.1162/neco.2009.05-09-1025.View ArticlePubMedGoogle Scholar
- Beck JM, Ma WJ, Kiani R, Hanks T, Churchland AK, Roitman J, Shadlen MN, Latham PE, Pouget A: Probabilistic Population Codes for Bayesian Decision Making. Neuron. 2008, 60 (6): 1142-1152. 10.1016/j.neuron.2008.09.021.PubMed CentralView ArticlePubMedGoogle Scholar
- Wald A, Wolfowitz J: Optimum character of the sequential probability ratio test. The Annals of Mathematical Statistics. 1948, 19 (3): 326-339. 10.1214/aoms/1177730197.View ArticleGoogle Scholar
- Kuhn A, Aertsen A, Rotter S: Higher-order statistics of input ensembles and the response of simple model neurons. Neural Computation. 2003, 15: 67-101. 10.1162/089976603321043702.View ArticlePubMedGoogle Scholar
- Roitman JD, Shadlen MN: Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience. 2002, 22 (21): 9475-9489.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.