Real-world choice options have many features or attributes, whereas the reward outcome from those options only depends on a few features or attributes. It has been shown that humans learn and combine feature-based with more complex conjunction-based learning to tackle challenges of learning in naturalistic reward environments. However, it remains unclear how different learning strategies interact to determine what features or conjunctions should be attended to and control choice behavior, and how subsequent attentional modulations influence future learning and choice. To address these questions, we examined the behavior of male and female human participants during a three-dimensional learning task in which reward outcomes for different stimuli could be predicted based on a combination of an informative feature and conjunction. Using multiple approaches, we found that both choice behavior and reward probabilities estimated by participants were most accurately described by attention-modulated models that learned the predictive values of both the informative feature and the informative conjunction. Specifically, in the reinforcement learning model that best fit choice data, attention was controlled by the difference in the integrated feature and conjunction values. The resulting attention weights modulated learning by increasing the learning rate on attended features and conjunctions. Critically, modulating decision making by attention weights did not improve the fit of data, providing little evidence for direct attentional effects on choice. These results suggest that in multidimensional environments, humans direct their attention not only to selectively process reward-predictive attributes, but also to find parsimonious representations of the reward contingencies for more efficient learning. Significance Statement From trying exotic recipes to befriending new social groups, outcomes of real-life actions depend on many factors, but how do we learn the predictive values of those factors based on feedback we receive? It has been shown that humans simplify this problem by focusing on individual features that are most predictive of the outcomes but can extend their learning strategy to include combinations of features when necessary. Here, we examined interaction between attention and learning in a multidimensional reward environment that requires learning about individual features and their conjunctions. Using multiple approaches, we found that learning about features and conjunctions control attention in a cooperative manner and that the ensuing attentional modulations mainly affects future learning and not decision making.

Michael Chong Wang

Alireza Soltani

Journal of Neuroscience

JNEUROSCI (ISSN 0270-6474) 是神经科学学会的官方期刊。它由协会每周出版，一年五十周，一年一卷。 JNEUROSCI 发表的论文涉及神经系统研究人员普遍感兴趣的广泛主题。作者现在可以为其发表的文章提供开放选择选项

The Journal of Neuroscience (ISSN 0270-6474) is the official journal of the Society for Neuroscience. It is published weekly, with fifty issues per year, forming one volume annually. The journal publishes papers covering a wide range of topics of interest to researchers in the field of neuroscience. Authors now have the option to provide open access for their published articles.

《Journal of Neuroscience》（ISSN 0270-6474）是神经科学学会的官方期刊。该期刊每周出版一次，每年出版五十期，形成一卷。期刊发表的论文涵盖了神经系统研究领域的广泛主题，旨在满足研究人员的兴趣。此外，作者现在可以为其发表的文章选择开放获取选项。

Category	Quartile
医学	2
医学, 神经科学	2

Contributions of attention to learning in multidimensional reward environments

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info