Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data: A Representation Learning Approach Based on Artificial Neural Networks

Valerio Bonometti*, Mathieu J. Ruiz, Anders Drachen, Alex Wade

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstract

Incentive salience attribution can be understood as a psychobiological mechanism ascribing relevance to potentially rewarding objects and actions. Despite being an important component of the motivational process guiding our everyday behaviour its study in naturalistic contexts is not straightforward. Here we propose a methodology based on artificial neural networks (ANNs) for approximating latent states produced by this process in situations where large volumes of behavioural data are available but no experimental control is possible. Leveraging knowledge derived from theoretical and computational accounts of incentive salience attribution we designed an ANN for estimating duration and intensity of future interactions between individuals and a series of video games in a large-scale (N > 3 × 106) longitudinal dataset. We found video games to be the ideal context for developing such methodology due to their reliance on reward mechanics and their ability to provide ecologically robust behavioural measures at scale. When compared to competing approaches our methodology produces representations that are better suited for predicting the intensity future behaviour and approximating some functional properties of attributed incentive salience. We discuss our findings with reference to the adopted theoretical and computational frameworks and suggest how our methodology could be an initial step for estimating attributed incentive salience in large-scale behavioural studies.

OriginalsprogEngelsk
TidsskriftComputational Brain and Behavior
Vol/bind6
Udgave nummer2
Sider (fra-til)280-315
ISSN2522-0861
DOI
StatusUdgivet - jun. 2023
Udgivet eksterntJa

Bibliografisk note

Funding Information:
This work was supported by the EPSRC Centre for Doctoral Training in Intelligent Games & Games Intelligence (IGGI) (EP/L015846/1).

Publisher Copyright:
© 2022, The Author(s).

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