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The Spatial Leaky Competing Accumulator Model

Viktoria Zemliak, Joe MacInnes Orcid Logo

Frontiers in Computer Science, Volume: 4

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.3389/fcomp.2022.866029

Abstract

The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to simulate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumula...

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Published in: Frontiers in Computer Science
ISSN: 2624-9898
Published: Frontiers Media SA
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URI: https://cronfa.swan.ac.uk/Record/cronfa63433
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spelling v2 63433 2023-05-11 The Spatial Leaky Competing Accumulator Model 06dcb003ec50192bafde2c77bef4fd5c 0000-0002-5134-1601 Joe MacInnes Joe MacInnes true false 2023-05-11 MACS The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to simulate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumulator models have shown excellent fit to these distributions. We propose a new implementation called the Spatial Leaky Competing Accumulator (SLCA), which can be used to predict the timing of subsequent fixation durations during a visual task. SLCA uses a pre-existing saliency map as input and represents accumulation neurons as a two-dimensional grid to generate predictions in visual space. The SLCA builds on several biologically motivated parameters: leakage, recurrent self-excitation, randomness and non-linearity, and we also test two implementations of lateral inhibition. A global lateral inhibition, as implemented in the original model of Usher and McClelland, is applied to all competing neurons, while a local implementation allows only inhibition of immediate neighbors. We trained and compared versions of the SLCA with both global and local lateral inhibition with use of a genetic algorithm, and compared their performance in simulating human fixation latency distribution in a foraging task. Although both implementations were able to produce a positively skewed latency distribution, only the local SLCA was able to match the human data distribution from the foraging task. Our model is discussed for its potential in models of salience and priority, and its benefits as compared to other models like the Leaky integrate and fire network. Journal Article Frontiers in Computer Science 4 Frontiers Media SA 2624-9898 0 0 0 0001-01-01 10.3389/fcomp.2022.866029 http://dx.doi.org/10.3389/fcomp.2022.866029 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Other This research was supported in part through computational resources of HPC facilities at HSE University (Kostenetskiy et al., 2021). 2024-07-12T11:21:24.5115350 2023-05-11T11:53:38.5447943 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Viktoria Zemliak 1 Joe MacInnes 0000-0002-5134-1601 2 63433__27650__329b9be76da74ca2b26daefc502b7865.pdf 63433.pdf 2023-05-31T10:20:36.5464439 Output 630582 application/pdf Version of Record true Copyright © 2022 Zemliak and MacInnes. 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) and the copyright owner(s) 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. true eng http://creativecommons.org/licenses/by/4.0/
title The Spatial Leaky Competing Accumulator Model
spellingShingle The Spatial Leaky Competing Accumulator Model
Joe MacInnes
title_short The Spatial Leaky Competing Accumulator Model
title_full The Spatial Leaky Competing Accumulator Model
title_fullStr The Spatial Leaky Competing Accumulator Model
title_full_unstemmed The Spatial Leaky Competing Accumulator Model
title_sort The Spatial Leaky Competing Accumulator Model
author_id_str_mv 06dcb003ec50192bafde2c77bef4fd5c
author_id_fullname_str_mv 06dcb003ec50192bafde2c77bef4fd5c_***_Joe MacInnes
author Joe MacInnes
author2 Viktoria Zemliak
Joe MacInnes
format Journal article
container_title Frontiers in Computer Science
container_volume 4
institution Swansea University
issn 2624-9898
doi_str_mv 10.3389/fcomp.2022.866029
publisher Frontiers Media SA
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.3389/fcomp.2022.866029
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description The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to simulate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumulator models have shown excellent fit to these distributions. We propose a new implementation called the Spatial Leaky Competing Accumulator (SLCA), which can be used to predict the timing of subsequent fixation durations during a visual task. SLCA uses a pre-existing saliency map as input and represents accumulation neurons as a two-dimensional grid to generate predictions in visual space. The SLCA builds on several biologically motivated parameters: leakage, recurrent self-excitation, randomness and non-linearity, and we also test two implementations of lateral inhibition. A global lateral inhibition, as implemented in the original model of Usher and McClelland, is applied to all competing neurons, while a local implementation allows only inhibition of immediate neighbors. We trained and compared versions of the SLCA with both global and local lateral inhibition with use of a genetic algorithm, and compared their performance in simulating human fixation latency distribution in a foraging task. Although both implementations were able to produce a positively skewed latency distribution, only the local SLCA was able to match the human data distribution from the foraging task. Our model is discussed for its potential in models of salience and priority, and its benefits as compared to other models like the Leaky integrate and fire network.
published_date 0001-01-01T11:21:24Z
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