Decision criterion dynamics in animals performing an auditory detection task.

Classical signal detection theory attributes bias in perceptual decisions to a threshold criterion, against which sensory excitation is compared. The optimal criterion setting depends on the signal level, which may vary over time, and about which the subject is naïve. Consequently, the subject must...

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Main Authors: Robert W Mill, Ana Alves-Pinto, Christian J Sumner
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4259461?pdf=render
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author Robert W Mill
Ana Alves-Pinto
Christian J Sumner
author_facet Robert W Mill
Ana Alves-Pinto
Christian J Sumner
author_sort Robert W Mill
collection DOAJ
description Classical signal detection theory attributes bias in perceptual decisions to a threshold criterion, against which sensory excitation is compared. The optimal criterion setting depends on the signal level, which may vary over time, and about which the subject is naïve. Consequently, the subject must optimise its threshold by responding appropriately to feedback. Here a series of experiments was conducted, and a computational model applied, to determine how the decision bias of the ferret in an auditory signal detection task tracks changes in the stimulus level. The time scales of criterion dynamics were investigated by means of a yes-no signal-in-noise detection task, in which trials were grouped into blocks that alternately contained easy- and hard-to-detect signals. The responses of the ferrets implied both long- and short-term criterion dynamics. The animals exhibited a bias in favour of responding "yes" during blocks of harder trials, and vice versa. Moreover, the outcome of each single trial had a strong influence on the decision at the next trial. We demonstrate that the single-trial and block-level changes in bias are a manifestation of the same criterion update policy by fitting a model, in which the criterion is shifted by fixed amounts according to the outcome of the previous trial and decays strongly towards a resting value. The apparent block-level stabilisation of bias arises as the probabilities of outcomes and shifts on single trials mutually interact to establish equilibrium. To gain an intuition into how stable criterion distributions arise from specific parameter sets we develop a Markov model which accounts for the dynamic effects of criterion shifts. Our approach provides a framework for investigating the dynamics of decisions at different timescales in other species (e.g., humans) and in other psychological domains (e.g., vision, memory).
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spelling doaj.art-987712c2d168416e8dd129ade86f790b2022-12-22T00:45:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11407610.1371/journal.pone.0114076Decision criterion dynamics in animals performing an auditory detection task.Robert W MillAna Alves-PintoChristian J SumnerClassical signal detection theory attributes bias in perceptual decisions to a threshold criterion, against which sensory excitation is compared. The optimal criterion setting depends on the signal level, which may vary over time, and about which the subject is naïve. Consequently, the subject must optimise its threshold by responding appropriately to feedback. Here a series of experiments was conducted, and a computational model applied, to determine how the decision bias of the ferret in an auditory signal detection task tracks changes in the stimulus level. The time scales of criterion dynamics were investigated by means of a yes-no signal-in-noise detection task, in which trials were grouped into blocks that alternately contained easy- and hard-to-detect signals. The responses of the ferrets implied both long- and short-term criterion dynamics. The animals exhibited a bias in favour of responding "yes" during blocks of harder trials, and vice versa. Moreover, the outcome of each single trial had a strong influence on the decision at the next trial. We demonstrate that the single-trial and block-level changes in bias are a manifestation of the same criterion update policy by fitting a model, in which the criterion is shifted by fixed amounts according to the outcome of the previous trial and decays strongly towards a resting value. The apparent block-level stabilisation of bias arises as the probabilities of outcomes and shifts on single trials mutually interact to establish equilibrium. To gain an intuition into how stable criterion distributions arise from specific parameter sets we develop a Markov model which accounts for the dynamic effects of criterion shifts. Our approach provides a framework for investigating the dynamics of decisions at different timescales in other species (e.g., humans) and in other psychological domains (e.g., vision, memory).http://europepmc.org/articles/PMC4259461?pdf=render
spellingShingle Robert W Mill
Ana Alves-Pinto
Christian J Sumner
Decision criterion dynamics in animals performing an auditory detection task.
PLoS ONE
title Decision criterion dynamics in animals performing an auditory detection task.
title_full Decision criterion dynamics in animals performing an auditory detection task.
title_fullStr Decision criterion dynamics in animals performing an auditory detection task.
title_full_unstemmed Decision criterion dynamics in animals performing an auditory detection task.
title_short Decision criterion dynamics in animals performing an auditory detection task.
title_sort decision criterion dynamics in animals performing an auditory detection task
url http://europepmc.org/articles/PMC4259461?pdf=render
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