Do we prefer a small flat with a short commute or a large house with a long commute? Many real-life decisions require combining information across different attributes. It has been shown that during such multiattribute decisions people serially attend to (or sample) a subset of the available information. The way this process takes place largely influences the final choice: for example, if the “commute” attribute is considered for longer, then the small flat will tend to appear better.
Up to date, information sampling has been studied within the social sciences using eye-tracking techniques. However, in the context of choice tasks, the way eye fixations influence the upcoming choice is not precisely known and, thus, this line of research has not yielded any definitive mechanistic conclusions. Recently, theorists have proposed different mechanisms of information sampling but these proposals were not constrained by relevant data.
I propose to fill this gap in a data-driven fashion by harnessing tools from sensory neuroscience. Using magnetoencephalography (MEG) we will simultaneously track the locus of attention and the tendency to choose one alternative over the other, during the entire time-course of a single multiattribute decision. This approach will enable us to unravel the computational and neural mechanisms that guide attention towards different aspects of a multiattribute choice problem.
This project will yield a neurophysiologically detailed theory of multiattribute choice–from the level of neurotransmitters, to large-scale brain networks, to behaviour– that will ultimately shed light on century-long questions, such as why humans reverse their preferences irrationally, when irrelevant alternatives are added to the choice-set. The emerging framework will be useful to policy makers and practitioners, interested in a descriptively enriched model of choice; and to clinicians aiming to understand how information sampling goes awry in neuropsychiatric disorders.
Field of science
- /social sciences
Call for proposal
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