Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
PLoS Comput Biol. 2024 Oct 31;20(10):e1012453. doi: 10.1371/journal.pcbi.1012453. eCollection 2024 Oct.
Curiosity-driven exploration involves actively engaging with the environment to learn from it. Here, we hypothesize that the cognitive mechanisms underlying exploratory behavior may differ across individuals depending on personal characteristics such as autistic traits. In turn, this variability might influence successful exploration. To investigate this, we collected self- and other-reports of autistic traits from university students, and tested them in an exploration task in which participants could learn the hiding patterns of multiple characters. Participants' prediction errors and learning progress (i.e., the decrease in prediction error) on the task were tracked with a hierarchical delta-rule model. Crucially, participants could freely decide when to disengage from a character and what to explore next. We examined whether autistic traits modulated the relation of prediction errors and learning progress with exploration. We found that participants with lower scores on other-reports of insistence-on-sameness and general autistic traits were less persistent, primarily relying on learning progress during the initial stages of exploration. Conversely, participants with higher scores were more persistent and relied on learning progress in later phases of exploration, resulting in better performance in the task. This research advances our understanding of the interplay between autistic traits and exploration drives, emphasizing the importance of individual traits in learning processes and highlighting the need for personalized learning approaches.
好奇心驱动的探索涉及主动参与环境以从中学习。在这里,我们假设探索行为的认知机制可能因人而异,取决于个人特征,如自闭症特征。反过来,这种可变性可能会影响成功的探索。为了研究这一点,我们从大学生那里收集了自我和他人的自闭症特征报告,并在一项探索任务中对他们进行了测试,在该任务中,参与者可以学习多个角色的隐藏模式。使用分层 delta 规则模型跟踪参与者在任务中的预测误差和学习进度(即预测误差的减少)。至关重要的是,参与者可以自由决定何时不再关注一个角色以及接下来探索什么。我们检查了自闭症特征是否调节了预测误差和学习进度与探索之间的关系。我们发现,在坚持相同性和一般自闭症特征的他人报告中得分较低的参与者的坚持性较低,主要依赖于探索的初始阶段的学习进度。相反,得分较高的参与者的坚持性更高,并且在探索的后期阶段依赖于学习进度,从而在任务中表现更好。这项研究增进了我们对自闭症特征和探索驱动之间相互作用的理解,强调了个体特征在学习过程中的重要性,并强调了个性化学习方法的必要性。