Technical Program

Paper Detail

Paper: PS-1A.26
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Optimal maintenance and use of uncertainty in visual working memory
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Aspen Yoo, Wei Ji Ma, New York University, United States
Abstract: Unlike in perceptual tasks, it is unclear whether humans near-optimally use uncertainty information in their visual working memory (VWM) decisions. Some circumstantial evidence is available: people can explicitly report their uncertainty and can near-optimally integrate knowledge of uncertainty with working memories. However, it is unclear whether people can do the conjunction: accurately store uncertainty information in VWM and use it in a subsequent decision. To investigate this, we collected data in two orientation change detection tasks. One task did not require the maintenance of uncertainty information and the other did. We factorially evaluate Bayesian observer models with different assumptions about the memory noise generating process, the observer's assumption of this process, and the observer's decision rule. For both experiments, the model that best fits human data assumes that memory precision varies as a function of stimulus reliability and other internal fluctuations, observers know their memory uncertainty on an individual-item basis, and observers optimally integrate information across items when making their decision. These results provide evidence that participants are able to maintain uncertainty information across a delay, and use it optimally in subsequent decisions.