September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Failure to account for extrinsic noise when integrating visual cues and prior information
Author Affiliations & Notes
  • Stacey Aston
    Durham University
  • Reeve Molly
    Durham University
  • Yip Esther
    Durham University
  • Nardini Marko
    Durham University
  • Beierholm Ulrik
    Durham University
  • Footnotes
    Acknowledgements  This work was funded by Leverhulme Trust Research Project Grant RPG-2017-097. MR was supported by an Undergraduate Summary Bursary from the Experimental Psychology Society. EY was supported by an Undergraduate Summer Bursary from Durham University's Biophysical Sciences Institute.
Journal of Vision September 2021, Vol.21, 2278. doi:
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      Stacey Aston, Reeve Molly, Yip Esther, Nardini Marko, Beierholm Ulrik; Failure to account for extrinsic noise when integrating visual cues and prior information. Journal of Vision 2021;21(9):2278.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Information integration for perception and decision making can be near-optimal, such as reliability-weighted averaging of sensory cues (Ernst and Banks, 2002) or sensory cues and prior knowledge (Wolpert et al. 2011). Behaviour diverges from optimal in a variety of circumstances (Rahnev and Denison, 2018). Recent studies suggest suboptimalities arise as perceptual and decision-making systems are not equally sensitive to all sources of uncertainty. Castanon et al. (2018) suggest un-certainty brought about through sensory encoding noise can be accounted for, but uncertainty in higher level integration processes cannot. Similarly, Kiryakova et al. (2020) hypothesise that uncer-tainty arising within perceptual/decision-making systems (intrinsic noise) can be tracked, but un-certainty arising in the world (extrinsic noise) cannot. Here, 40 participants used intrinsic-only or intrinsic+extrinsic noise visual cues (dot-clouds) and prior information (Gaussian base-rate distribu-tions shown and reinforced through feedback) to estimate the location of a hidden target. Intrin-sic-only cues were four dots from a Gaussian centred on the true location with low/high variability (low/high intrinsic noise). Intrinsic+extrinsic cues were centred on a position varying about the true location according to a draw from a second Gaussian, adding extrinsic uncertainty. Participants bi-ased their responses more towards the prior when presented with high compared to low intrinsic noise cues (p < .001) but did not adjust the weight given to the cues when extrinsic noise was add-ed (p = .209). Accordingly, the weight placed on the cue was further from optimal when extrinsic noise was present (p < .001). Subjective uncertainty measures suggest participants were not fully aware of the extent of the added extrinsic noise. These results are in favour of the hypothesis that perceptual and decision-making systems struggle to track and account for extrinsic noise. They suggest optimal information integration for perception and decision-making is only possible when accounting for specific types of uncertainty.


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