From shunting inhibition to dynamic normalization: Attentional selection and decision-making in brief visual displays

• We derive normalization models as the asymptotic solutions of shunting equations.
• We show how shunting models can describe the time course of attentional processing.
• We develop dynamic models of attention that can predict response time and accuracy.
• Salience and attention jointly determine the representations used to make decisions.
• The models provide a unified account of a variety of attentional phenomena.

Normalization models of visual sensitivity assume that the response of a visual mechanism is scaled divisively by the sum of the activity in the excitatory and inhibitory mechanisms in its neighborhood. Normalization models of attention assume that the weighting of excitatory and inhibitory mechanisms is modulated by attention. Such models have provided explanations of the effects of attention in both behavioral and single-cell recording studies. We show how normalization models can be obtained as the asymptotic solutions of shunting differential equations, in which stimulus inputs and the activity in the mechanism control growth rates multiplicatively rather than additively. The value of the shunting equation approach is that it characterizes the entire time course of the response, not just its asymptotic strength. We describe two models of attention based on shunting dynamics, the integrated system model of Smith and Ratcliff (2009) and the competitive interaction theory of Smith and Sewell (2013). These models assume that attention, stimulus salience, and the observer’s strategy for the task jointly determine the selection of stimuli into visual short-term memory (VSTM) and the way in which stimulus representations are weighted. The quality of the VSTM representation determines the speed and accuracy of the decision. The models provide a unified account of a variety of attentional phenomena found in psychophysical tasks using single-element and multi-element displays. Our results show the generality and utility of the normalization approach to modeling attention.