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A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.

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Title: A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.
Authors: Forest, Simon (AUTHOR), Quinton, Jean-Charles (AUTHOR), Lefort, Mathieu (AUTHOR)
Source: Neural Computation. Aug2022, Vol. 34 Issue 8, p1701-1726. 26p. 2 Black and White Photographs, 2 Diagrams, 3 Charts, 4 Graphs.
Subjects: Superior colliculus, Distribution (Probability theory)
Abstract: Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale—for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Aug2022, Vol. 34 Issue 8, p1701-1726. 26p. 2 Black and White Photographs, 2 Diagrams, 3 Charts, 4 Graphs.
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  Data: Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale—for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1162/neco_a_01509
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 26
        StartPage: 1701
    Subjects:
      – SubjectFull: Superior colliculus
        Type: general
      – SubjectFull: Distribution (Probability theory)
        Type: general
    Titles:
      – TitleFull: A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.
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            NameFull: Forest, Simon
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            NameFull: Quinton, Jean-Charles
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            NameFull: Lefort, Mathieu
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            – D: 01
              M: 08
              Text: Aug2022
              Type: published
              Y: 2022
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              Value: 08997667
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              Value: 34
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: Neural Computation
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