As Within, so Without; As Above, so Below: Common Mechanisms Can Support Between- and Within-Trial Learning Dynamics

Abstract

Two fundamental difficulties when learning is deciding 1) what information is relevant, and 2) when to use it. To overcome these difficulties, humans continuously make choices about which dimensions of information to selectively attend to, and monitor their relevance to the current goal. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal ordering of information sampling influences learning. Here, we use the Adaptive Attention Representation Model (AARM) to demonstrate that a common set of mechanisms can be used to specify: 1) how the distribution of attention is updated between trials over the course of learning; and 2) how attention dynamically shifts among dimensions within-trial. We validate or proposed set of mechanisms by comparing AARM’s predictions to observed behavior across five case studies, which collectively encompass different theoretical aspects of selective attention. Importantly, we use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence our continuous choices about which dimensions to attend to via gaze fixations?

Distributed neural systems enable flexible attention updating during category learning

“…By fitting AARM to eye-tracking data in previous work, we were able to show that AARM's mechanisms of attention not only predict learning at the level of response accuracy, but at the level of information sampling behaviors as well with increasing reliance on relevant dimensions as the task proceeds. Additional work showed that AARM extends to within-trial dynamics, such that it can accurately predict the order in which individuals will fixate to dimensions after gaining sufficient experience with the structure of the task (Weichart et al, 2021).…”

“…increased categorization accuracy across trials) is conceptualized as a natural consequence of storing experiences of stimuli and associated feedback as they occur, and preferentially allocating attention to the most relevant dimensions. Here, we will introduce the framework in terms of three core components: Representation, Decision, and Attention (Turner, 2019;Weichart et al, 2021).…”

“…AARM's innovation relative to GCM, therefore, lies in its inclusion of a gradient-based mechanism for updating attention according to feedback. Because attention is redistributed on every trial based only on what the observer has experienced up until that point, AARM can account for the gradual accrual of information that is required for identifying the task-relevant dimensions concurrent with learning Weichart et al, 2021).…”

Cognitive and personality predictors of school performance from preschool to secondary school: An overarching model.

“…It is accepted that problem-solving is based on the construction of simplified representations of the problem under consideration, which reduce the dimensions needed to solve the problem. These simplified initial representations bias where attention is to be initially committed and refocused in the process, interchanging between covert attention to a goal hierarchy and overt attention to information changing online (Weichart et al, 2022). They also affect the value of representations, balancing the cost of action plans with their utilities (Ho et al, 2022).…”