Header

UZH-Logo

Maintenance Infos

Enhancing models of social and strategic decision making with process tracing and neural data


Konovalov, Arkady; Ruff, Christian C (2021). Enhancing models of social and strategic decision making with process tracing and neural data. Wiley Interdisciplinary Reviews: Cognitive Science:Epub ahead of print.

Abstract

Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the “black box” of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi‐faceted decisions that are taken in varying contexts and are based on many different types of information.

Abstract

Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the “black box” of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi‐faceted decisions that are taken in varying contexts and are based on many different types of information.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

2 downloads since deposited on 20 May 2021
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, not_refereed, further contribution
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Life Sciences > General Neuroscience
Social Sciences & Humanities > General Psychology
Uncontrolled Keywords:Economics, interactive decision‐making, neuroscience, cognition, psychology, reasoning and decision making
Language:English
Date:20 April 2021
Deposited On:20 May 2021 08:51
Last Modified:21 May 2021 20:00
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:1939-5078
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/wcs.1559
Project Information:
  • : FunderH2020
  • : Grant ID725355
  • : Project TitleBRAINCODES - Brain networks controlling social decisions

Download

Closed Access: Download allowed only for UZH members

Content: Accepted Version
Language: English
Filetype: PDF - Registered users only until 20 April 2022
Size: 668kB
Embargo till: 2022-04-20