Dynamics of attentional selection under conflict: toward a rational Bayesian account.

Yu, A. J., Dayan, P., and Cohen, J. D.
J Exp Psychol Hum Percept Perform, 35:700–717, 2009
DOI, Google Scholar


The brain exhibits remarkable facility in exerting attentional control in most circumstances, but it also suffers apparent limitations in others. The authors' goal is to construct a rational account for why attentional control appears suboptimal under conditions of conflict and what this implies about the underlying computational principles. The formal framework used is based on Bayesian probability theory, which provides a convenient language for delineating the rationale and dynamics of attentional selection. The authors illustrate these issues with the Eriksen flanker task, a classical paradigm that explores the effects of competing sensory inputs on response tendencies. The authors show how 2 distinctly formulated models, based on compatibility bias and spatial uncertainty principles, can account for the behavioral data. They also suggest novel experiments that may differentiate these models. In addition, they elaborate a simplified model that approximates optimal computation and may map more directly onto the underlying neural machinery. This approximate model uses conflict monitoring, putatively mediated by the anterior cingulate cortex, as a proxy for compatibility representation. The authors also consider how this conflict information might be disseminated and used to control processing.


They suggest two simple, Bayesian perceptual models based on evidence integration for the (deadlined) Eriksen task. Their focus is on attentional mechanisms that can explain why particpants' responses are below chance for very fast responses. These mechanisms are based on a prior on compatibility (that flankers are compatible with the relevant centre stimulus) and spatial uncertainty (flankers influence processing of centre stimulus on a low, sensory level). The core inference is the same and replicates the basic mechanism you would expect for any perceptual decision making task. They don't fit behaviour, but rather show average trajectories from model simulations with hand-tuned parameters. They further suggest a third model inspired by previous work on conflict monitoring and cognitive control which supposedly is more likely to be implemented in the brain, because instead of having to consider (and compute with) all possible stimuli in the environment, it uses a conflict monitoring mechanism to switch between types of stimuli that are considered.