Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood
Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood
复制标题
奖励最大化证明了从童年的感觉选择到成年的感觉统合的转变是合理的
作者：
Pedram Daee;M. Mirian;M. N. Ahmadabadi
在多感官任务中，人类成年人以最优贝叶斯方式整合来自不同感官模式的信息，而儿童大多依赖于单一的感官模式进行决策。随着年龄的增长，这种行为变化背后的原因以及学习最佳整合所需统计数据背后的过程仍然不清楚，也没有被传统的贝叶斯模型所证明。我们提出了一个交互式多感官学习框架，而不需要对感官模型进行任何预先假设。在这个框架中，每个模态及其联合空间的学习是使用单步强化学习方法并行完成的。对奖励分布均值置信区间的简单统计检验用于在个体模态和联合空间中选择最具信息量的信息源。对该方法的分析和多模态定位任务的仿真结果表明，该学习系统从感觉选择开始自主学习，并逐渐过渡到感觉整合。这是因为，在早期学习阶段（童年）更多地依赖于模态——即选择——比在联合空间中学习决策更有益，因为模态中较小的状态空间导致每个个体模态的学习更快。相反，在获得足够的经验（成年期）后，关节空间的学习质量趋于成熟，而模态的学习由于感知混叠而准确性不足。它的结果是更紧密的置信区间的关节空间，从而导致平滑的转变，从选择到整合。这表明感官选择和整合是一种紧急行为，都是单一奖励最大化过程的输出；也就是说，这种转变不是一种预先设定好的现象。
In a multisensory task, human adults integrate information from different sensory modalities -behaviorally in an optimal Bayesian fashion- while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities -i.e. selection- at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.
影响因子：
2.5
作者：
Wallace, Mark T.;Stein, Barry E.
通讯作者：
Stein, Barry E.