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Human optional stopping in a heteroscedastic world

Standard

Human optional stopping in a heteroscedastic world. / Tickle, Hannah; Tsetsos, Konstantinos; Speekenbrink, Maarten; Summerfield, Christopher.

in: PSYCHOL REV, Jahrgang 130, Nr. 1, 01.2023, S. 1-22.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Review › Forschung

Harvard

Tickle, H, Tsetsos, K, Speekenbrink, M & Summerfield, C 2023, 'Human optional stopping in a heteroscedastic world', PSYCHOL REV, Jg. 130, Nr. 1, S. 1-22. https://doi.org/10.1037/rev0000315

APA

Tickle, H., Tsetsos, K., Speekenbrink, M., & Summerfield, C. (2023). Human optional stopping in a heteroscedastic world. PSYCHOL REV, 130(1), 1-22. https://doi.org/10.1037/rev0000315

Vancouver

Tickle H, Tsetsos K, Speekenbrink M, Summerfield C. Human optional stopping in a heteroscedastic world. PSYCHOL REV. 2023 Jan;130(1):1-22. https://doi.org/10.1037/rev0000315

Bibtex

@article{52da17c543b740d58010416af5a66a37,
title = "Human optional stopping in a heteroscedastic world",
abstract = "When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or {"}heteroscedasticity{"}). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).",
author = "Hannah Tickle and Konstantinos Tsetsos and Maarten Speekenbrink and Christopher Summerfield",
year = "2023",
month = jan,
doi = "10.1037/rev0000315",
language = "English",
volume = "130",
pages = "1--22",
journal = "PSYCHOL REV",
issn = "0033-295X",
publisher = "American Psychological Association Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Human optional stopping in a heteroscedastic world

AU - Tickle, Hannah

AU - Tsetsos, Konstantinos

AU - Speekenbrink, Maarten

AU - Summerfield, Christopher

PY - 2023/1

Y1 - 2023/1

N2 - When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

AB - When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

U2 - 10.1037/rev0000315

DO - 10.1037/rev0000315

M3 - SCORING: Review article

C2 - 34570524

VL - 130

SP - 1

EP - 22

JO - PSYCHOL REV

JF - PSYCHOL REV

SN - 0033-295X

IS - 1

ER -