Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the generalized context model (Nosofsky, 1986) to account for both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used triallevel eye-tracking data from training (memory precision) and test (decision weights) to replace the otherwise freely-estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with 1) selective attention to a relevant dimension, 2) distributed attention across dimensions, and 3) flexibly shifting strategies between tasks. Although humans engage selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future.Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the generalized context model (Nosofsky, 1986) to account for both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used trial level eye-tracking data from training (memory precision) and test (decision weights) to replace the otherwise freely-estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with 1) selective attention to a relevant dimension, 2) distributed attention across dimensions, and 3) flexibly shifting strategies between tasks. Although humans engage selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)