Incentive value and spatial certainty combine additively to determine visual priorities
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Incentive value and spatial certainty combine additively to determine visual priorities K.G. Garner 1,2
&
H. Bowman 1 & J.E. Raymond 1
# The Author(s) 2020
Abstract How does the brain combine information predictive of the value of a visually guided task (incentive value) with information predictive of where task-relevant stimuli may occur (spatial certainty)? Human behavioural evidence indicates that these two predictions may be combined additively to bias visual selection (Additive Hypothesis), whereas neuroeconomic studies posit that they may be multiplicatively combined (Expected Value Hypothesis). We sought to adjudicate between these two alternatives. Participants viewed two coloured placeholders that specified the potential value of correctly identifying an imminent letter target if it appeared in that placeholder. Then, prior to the target’s presentation, an endogenous spatial cue was presented indicating the target’s more likely location. Spatial cues were parametrically manipulated with regard to the information gained (in bits). Across two experiments, performance was better for targets appearing in high versus low value placeholders and better when targets appeared in validly cued locations. Interestingly, as shown with a Bayesian model selection approach, these effects did not interact, clearly supporting the Additive Hypothesis. Even when conditions were adjusted to increase the optimality of a multiplicative operation, support for it remained. These findings refute recent theories that expected value computations are the singular mechanism driving the deployment of endogenous spatial attention. Instead, incentive value and spatial certainty seem to act independently to influence visual selection. Keywords Attention . Prediction . Expectation . Reward . Incentive
Introduction Humans are good at learning that specific sensory information, or cues, can predict subsequent events. For example, we learn quickly that hearing a siren on the left predicts a speeding emergency vehicle appearing from that direction, or that seeing a smile predicts a likely future opportunity to gain social approval. Knowledge about where and when new, important sensory information may appear or new reward opportunities may arise is only useful, however, if such knowledge can influence how cognitive mechanisms prioritise information representation for the eventual control of behaviour. Yet, our understanding of how learning and experience modify such prioritisation of visual signals, i.e. visual selection,
* K.G. Garner [email protected] 1
School of Psychology, University of Birmingham, Birmingham, UK
2
Queensland Brain Institute (79), University of Queensland, St Lucia, QLD 4072, Australia
remains incomplete. Particularly unclear is how multiple concurrent sensory cues, each associated with and therefore predictive of specific consequent outcomes, are combined to influence visual selection. A central tenet of many cognitive, computational, and neurobiological theories of visual selection (Buschman & Kastner, 201
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