Poster Session: A unifying framework for perceptual decision-making.


Journal

Journal of vision
ISSN: 1534-7362
Titre abrégé: J Vis
Pays: United States
ID NLM: 101147197

Informations de publication

Date de publication:
01 09 2023
Historique:
medline: 25 9 2023
pubmed: 21 9 2023
entrez: 21 9 2023
Statut: ppublish

Résumé

Perceptual decision making (PDM) has been studied using two approaches. Threshold measurement is predominant used in psychophysics, while reaction times (RT) with associated models have been used to estimate components of PDM (i.e., drift rate). To test if these two approaches reflect overlapping mechanisms, we conducted 3 experiments: a motion, a static orientation, and a dynamic orientation task. DT is the shortest stimulus presentation time sufficient to make accurate perceptual decisions. RTs and choices were fitted by a drift diffusion model (DDM). We expected a close relationship between DTs and drift rates, allowing us to accurately predict DTs from RT. In the motion task, we found a close relation between the empirical DTs and the DTs predicted by the DDM. Surprisingly, in the static task, there was little correlation between the two; DTs, improved monotonically with higher contrast, but drift rates saturated at 6%. We hypothesize that this mismatch is due to the information being available immediately in the static task, without needing to accumulate new evidence. Thus, we developed a novel dynamic orientation task that mimics the dynamic nature of the motion task and found a similar relation between DTs and drift rates. In summary, we show a close link between DTs and drift rate for the two dynamic tasks. This result supports the conceptualization of drift rate as a proxy for perceptual sensitivity but only for task where new information becomes available over time.

Identifiants

pubmed: 37733519
pii: 2792807
doi: 10.1167/jov.23.11.59
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

59

Auteurs

Ying Lin (Y)

University of Rochester.

Zhen Chen (Z)

University of Rochester.

Jose Reynoso (J)

The City College of New York.

Ralf Haefner (R)

University of Rochester.

Duje Tadin (D)

University of Rochester.

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Classifications MeSH