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 ($N> 3 \times 10^6$) 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.
翻译:激励显著的归因可被理解为一种心理生物学机制,它与潜在的奖励对象和行动相关。尽管它是指导我们日常行为的激励过程的重要组成部分,但在自然环境中的研究并非直截了当。我们在这里建议了一种基于人工神经网络(ANNS)的方法,用于在有大量行为数据但无法进行实验控制的情况下,通过这一过程产生的接近潜伏国家。利用从奖励显著属性的理论和计算账户中获得的知识,我们设计了一个ANN,用于在大型(N > 3\ times 10 ⁇ 6$)纵向数据集中估计个人与一系列视频游戏之间未来互动的时间和强度。我们发现视频游戏是制定这种方法的理想背景,因为它们依赖奖励机制并有能力提供规模强生态稳健的行为措施。与相互竞争的方法相比,我们的方法产生更适合预测强度的未来行为和接近的奖励显著特征的一些功能性特征。我们讨论了我们的结论,并参考了已经采纳的理论和计算框架,并建议如何在大规模行为研究中采用我们的方法。