{"id":1265,"date":"2015-05-11T09:57:40","date_gmt":"2015-05-11T09:57:40","guid":{"rendered":"http:\/\/mariuslab.co.nf\/test\/?page_id=1265"},"modified":"2022-04-18T11:55:39","modified_gmt":"2022-04-18T11:55:39","slug":"decision-making-confidence","status":"publish","type":"page","link":"https:\/\/people1.socsci.tau.ac.il\/mu\/usherlab\/homepage\/decision-making-confidence\/","title":{"rendered":"Decision Making & Confidence"},"content":{"rendered":"
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The algorithm of evidence integration in perceptual decisions and judgments of confidence<\/strong>
\nOne stream of research involves the nature of the mechanism by which people integrate noisy evidence and reach a decision. The goal here is to account for both choice and RT-distribution data. We do this by manipulating relevant variables, such as the flow of evidence, task requirements, etc., and we fit the data with competing neurocomputational theories.
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Illustration of the stimuli: On each trial, one of the disk\u2019s brightness level stochastically dominated the other; B) Experimental design:<\/i><\/span> In the first experimental session, observers were instructed to choose the dominating disk. After making a decision, additional information was presented for at least 1.5 seconds. Hereafter observers could either confirm, or change their decision (t2).<\/i><\/span><\/p>\n

The results indicate that the decision-mechanism is efficient (subject to response-competition) and adaptive (observers adapt the nature of the integration to the environmental contingencies). We also find that people keep integrating evidence even after they make decisions and that this contributes to their decision-confidence. Interestingly, the gain of the post-decisional information is reduced (less efficient integration, see Figure 1), accounting for commitment effects (cases in which decision-makers are less likely to change their mind, even in face of contradictory evidence).
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<\/div>\n\"ReducedGain\"
\nFigure 1. Illustration of the reduced-gain drift-diffusion model, in which sensitivity to additional information is reduced following a decision (from Bronfman et al., 2015. Decisions reduce sensitivity to subsequent information. Proceedings of the Royal Soc. London, B.)<\/p>\n
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A behavioral algorithm to monitor the decision boundaries, which shows that people integrate evidence to a collapsing boundary, and that they have a bias favoring evidence samples that are consistent with preceding evidence samples:   <\/strong><\/p>\n