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More than meets the AI: The possibilities and limits of machine learning in olfaction

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlat...

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Detalles Bibliográficos
Autores principales: Barwich, Ann-Sophie, Lloyd, Elisabeth A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9475214/
https://www.ncbi.nlm.nih.gov/pubmed/36117640
http://dx.doi.org/10.3389/fnins.2022.981294
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author Barwich, Ann-Sophie
Lloyd, Elisabeth A.
author_facet Barwich, Ann-Sophie
Lloyd, Elisabeth A.
author_sort Barwich, Ann-Sophie
collection PubMed
description Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system’s biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd’s “Logic of Research Questions,” a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.
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spelling pubmed-94752142022-09-16 More than meets the AI: The possibilities and limits of machine learning in olfaction Barwich, Ann-Sophie Lloyd, Elisabeth A. Front Neurosci Neuroscience Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system’s biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd’s “Logic of Research Questions,” a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question. Frontiers Media S.A. 2022-09-01 /pmc/articles/PMC9475214/ /pubmed/36117640 http://dx.doi.org/10.3389/fnins.2022.981294 Text en Copyright © 2022 Barwich and Lloyd. https://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) 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.
spellingShingle Neuroscience
Barwich, Ann-Sophie
Lloyd, Elisabeth A.
More than meets the AI: The possibilities and limits of machine learning in olfaction
title More than meets the AI: The possibilities and limits of machine learning in olfaction
title_full More than meets the AI: The possibilities and limits of machine learning in olfaction
title_fullStr More than meets the AI: The possibilities and limits of machine learning in olfaction
title_full_unstemmed More than meets the AI: The possibilities and limits of machine learning in olfaction
title_short More than meets the AI: The possibilities and limits of machine learning in olfaction
title_sort more than meets the ai: the possibilities and limits of machine learning in olfaction
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9475214/
https://www.ncbi.nlm.nih.gov/pubmed/36117640
http://dx.doi.org/10.3389/fnins.2022.981294
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