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Consistent Implementation of Decisions in the Brain

Overview of attention for article published in PLOS ONE, September 2012
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Title
Consistent Implementation of Decisions in the Brain
Published in
PLOS ONE, September 2012
DOI 10.1371/journal.pone.0043443
Pubmed ID
Authors

James A. R. Marshall, Rafal Bogacz, Iain D. Gilchrist

Abstract

Despite the complexity and variability of decision processes, motor responses are generally stereotypical and independent of decision difficulty. How is this consistency achieved? Through an engineering analogy we consider how and why a system should be designed to realise not only flexible decision-making, but also consistent decision implementation. We specifically consider neurobiologically-plausible accumulator models of decision-making, in which decisions are made when a decision threshold is reached. To trade-off between the speed and accuracy of the decision in these models, one can either adjust the thresholds themselves or, equivalently, fix the thresholds and adjust baseline activation. Here we review how this equivalence can be implemented in such models. We then argue that manipulating baseline activation is preferable as it realises consistent decision implementation by ensuring consistency of motor inputs, summarise empirical evidence in support of this hypothesis, and suggest that it could be a general principle of decision making and implementation. Our goal is therefore to review how neurobiologically-plausible models of decision-making can manipulate speed-accuracy trade-offs using different mechanisms, to consider which of these mechanisms has more desirable decision-implementation properties, and then review the relevant neuroscientific data on which mechanism brains actually use.

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The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
France 2 5%
Switzerland 1 2%
United Kingdom 1 2%
Italy 1 2%
Unknown 37 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 25%
Student > Master 8 18%
Student > Ph. D. Student 7 16%
Professor 5 11%
Lecturer 4 9%
Other 7 16%
Unknown 2 5%
Readers by discipline Count As %
Psychology 10 23%
Agricultural and Biological Sciences 8 18%
Computer Science 5 11%
Engineering 4 9%
Neuroscience 4 9%
Other 7 16%
Unknown 6 14%