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Incentive salience attribution can be understood as a psychobiologicalmechanism ascribing relevance to potentially rewarding objects and actions.Despite being an important component of the motivational process guiding oureveryday behaviour its study in naturalistic contexts is not straightforward.Here we propose a methodology based on artificial neural networks (ANNs) forapproximating latent states produced by this process in situations where largevolumes of behavioural data are available but no experimental control ispossible. Leveraging knowledge derived from theoretical and computationalaccounts of incentive salience attribution we designed an ANN for estimatingduration and intensity of future interactions between individuals and a seriesof video games in a large-scale ($N> 3 \times 10^6$) longitudinal dataset. Wefound video games to be the ideal context for developing such methodology dueto their reliance on reward mechanics and their ability to provide ecologicallyrobust behavioural measures at scale. When compared to competing approaches ourmethodology produces representations that are better suited for predicting theintensity future behaviour and approximating some functional properties ofattributed incentive salience. We discuss our findings with reference to theadopted theoretical and computational frameworks and suggest how ourmethodology could be an initial step for estimating attributed incentivesalience in large scale behavioural studies.

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