从大规模行为数据逼近归因激励显著性的流形结构。基于人工神经网络的表示学习方法
Approximating the Manifold Structure of Attributed Incentive Salience from Large Scale Behavioural Data. A Representation Learning Approach Based on Artificial Neural Networks
摘要：
激励显著性归因可以被理解为一种心理生物学机制，将相关性归因于潜在的奖励对象和行为。尽管它是指导我们日常行为的动机过程的重要组成部分，但在自然主义背景下的研究并不简单。在这里，我们提出了一种基于人工神经网络（ANN）的方法，用于近似的情况下，大量的行为数据，但没有实验控制是可能的这个过程中产生的潜在状态。利用知识来自理论和计算帐户的激励显着性归因，我们设计了一个人工神经网络来估计持续时间和强度之间的未来互动的个人和一系列的视频游戏在一个大规模的（$N> 3 \times 10^6$）纵向数据集。我们发现视频游戏是开发这种方法的理想环境，因为它们依赖于奖励机制，并且能够大规模提供生态稳健的行为措施。与竞争方法相比，我们的方法产生的表示，更适合于预测强度未来的行为和近似的一些功能属性的归因激励显着性。我们讨论了我们的研究结果，参考所采用的理论和计算框架，并建议我们的方法可以是一个初始步骤，估计归因于激励显着性在大规模的行为研究。
英文摘要：
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.
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