Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism

Zohar Z Bronfman, Noam Brezis and Marius Usher

PLOS Computational Biology, 2016, vol. 12, issue 2, 1-21

Abstract: Perceptual decisions are thought to be mediated by a mechanism of sequential sampling and integration of noisy evidence whose temporal weighting profile affects the decision quality. To examine temporal weighting, participants were presented with two brightness-fluctuating disks for 1, 2 or 3 seconds and were requested to choose the overall brighter disk at the end of each trial. By employing a signal-perturbation method, which deploys across trials a set of systematically controlled temporal dispersions of the same overall signal, we were able to quantify the participants’ temporal weighting profile. Results indicate that, for intervals of 1 or 2 sec, participants exhibit a primacy-bias. However, for longer stimuli (3-sec) the temporal weighting profile is non-monotonic, with concurrent primacy and recency, which is inconsistent with the predictions of previously suggested computational models of perceptual decision-making (drift-diffusion and Ornstein-Uhlenbeck processes). We propose a novel, dynamic variant of the leaky-competing accumulator model as a potential account for this finding, and we discuss potential neural mechanisms.Author Summary: An important process that supports decision-making is the integration of evidence over time, which optimizes decision quality by enhancing the signal to noise ratio. The nature of this process depends critically on the weight given to evidence across time: which information has more impact—early, intermediate or late? We used a novel psychophysical technique, which relies on differential temporal dispersion of evidence. This technique allowed us to extract the temporal weights people assign to the flow of evidence. We find that in decisions that are based on relatively short streams of evidence, people gave stronger weight to early information (primacy). Surprisingly, however, with longer streams of evidence, people assigned higher weights to early and late evidence, while underweighting intermediate evidence. This non-monotonic pattern of evidence integration is not predicted by the existing models of decision-making, posing a challenge to current theories. We propose a novel model that accounts for non-monotonic weighting, based on a change in leak and response-competition with integration-time, and we discuss potential neural mechanisms.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004667 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 04667&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004667

DOI: 10.1371/journal.pcbi.1004667

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().