| Paper: | PS-2A.37 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Evidence accumulation in changing environments: linking normative computation and neural implementation | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1085-0 | ||
| Authors: | Peter Murphy, Niklas Wilming, Carolina Hernandez Bocanegra, University Medical Center Hamburg Eppendorf, Germany; Genis Prat Ortega, Institut D’Investigacions Biomèdiques August Pi i Sunyer, Spain; Tobias Donner, University Medical Center Hamburg-Eppendorf, Germany | ||
| Abstract: | Several models posit that perceptual decisions under uncertainty result from the lossless temporal accumulation of momentary ‘evidence’ for alternative world states. An often-overlooked challenge of perceptual decisions in natural environments is that the world state can undergo hidden changes. This requires adaptive tuning of the accumulation process to suit the statistics of the changes. We assessed the behavior of human decision-makers performing a perceptual choice task with state changes, compared the behavior with the normative accumulation process for this task, and unraveled the underlying large-scale neural mechanisms with magnetoencephalography (MEG). Observers’ choices were consistent with those of the normative model. Both gave especially strong weight to evidence samples that indicated a high probability of a state change. Choice-specific preparatory activity in movement-selective regions of motor and parietal cortex exhibited the same sensitivity to change-point probability as the normative model and the human observers, and encoded the model’s decision-variable in a near-categorical fashion. These features qualitatively distinguished human behavior from simpler decision algorithms (e.g. drift diffusion or leaky accumulation) but were all reproduced by a biophysically inspired attractor model of decision-making. We propose that attractor dynamics in decision-related cortical activity approximate nor-mative evidence accumulation in changing environments. | ||