Individual differences in attention influence perceptual decision making
Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account...
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Frontiers Media S.A.
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329506/ |
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pubmed-43295062015-03-11 Individual differences in attention influence perceptual decision making Nunez, Michael D. Srinivasan, Ramesh Vandekerckhove, Joachim Psychology Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data. Frontiers Media S.A. 2015-02-05 /pmc/articles/PMC4329506/ /pubmed/25762974 http://dx.doi.org/10.3389/fpsyg.2015.00018 Text en Copyright © 2015 Nunez, Srinivasan and Vandekerckhove. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Nunez, Michael D. Srinivasan, Ramesh Vandekerckhove, Joachim |
| spellingShingle |
Nunez, Michael D. Srinivasan, Ramesh Vandekerckhove, Joachim Individual differences in attention influence perceptual decision making |
| author_facet |
Nunez, Michael D. Srinivasan, Ramesh Vandekerckhove, Joachim |
| author_sort |
Nunez, Michael D. |
| title |
Individual differences in attention influence perceptual decision making |
| title_short |
Individual differences in attention influence perceptual decision making |
| title_full |
Individual differences in attention influence perceptual decision making |
| title_fullStr |
Individual differences in attention influence perceptual decision making |
| title_full_unstemmed |
Individual differences in attention influence perceptual decision making |
| title_sort |
individual differences in attention influence perceptual decision making |
| description |
Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4329506/ |
| _version_ |
1613188676911628288 |