A Structural Model of Attentional Effort Dynamics: Evidence From a Naturalistic Discrimination Task

ObjectiveTo propose a model of how attentional effort varies over time in a vigilance task and how this effort relates to subjectively inferred context. To propose an estimation methodology and test the empirical validity of the proposed model in a naturalistic dataset.BackgroundAttentional effort in a task can vary based on how an individual subjectively perceives the task context. However, both attention exertion and subjective context perception are not directly observable. We present a methodology for estimating a structural model that explicitly incorporates subjective models of context perception and attention allocation policies. To our knowledge, this is the first methodology to estimate a structural model of attentional effort dynamics.MethodA Bayesian model of attentional allocation that integrates subjective perceptions of task-relevant context is developed. An estimation methodology based upon expectation-maximization algorithm is proposed to uncover how the allocation of attentional effort is adapted to subjectively perceived context.ResultsThe methodology is applied to a naturalistic dataset of Major League Baseball umpire decisions, revealing context perception (i.e., how umpires infer game situations) and attention allocation policy (i.e., how umpires adjust attentional effort). Model reveals that umpires adjust attentional effort based on inferred game criticality and status bias.ConclusionThis work advances understanding of vigilance failure by providing a structural account for contextual inference determines attentional effort. The estimated model closely tracks empirically observed decision accuracy patterns in a naturalistic dataset.ApplicationThe proposed model enables counterfactual predictions, allowing exploration of hypothetical interventions to improve decision accuracy in environments that require sustained attention.