Abstract
Generative models of decision now permeate all subfields of psychology, cognitive and clinical neuroscience. To successfully investigate decision mechanisms from behaviour, it is necessary to assume the presence of delays prior and after the decision process itself. However, directly observing this “non-decision time” from behaviour long appeared beyond reach, the field mainly
relying on models to estimate it. Here, we propose a biological definition of decision that includes perceptual discrimination and action selection, and in turn explicitly equates non-decision time with the minimum sensorimotor delay, or “deadtime”. We show how this delay is directly observable in behavioural data, without modelling assumptions, using the visual interference
approach. We apply this approach to 11 novel and archival datasets from humans and monkeys gathered from multiple labs. We validate the method by showing that visual properties (brightness, colour, size) consistently affect empirically measured visuomotor deadtime, as predicted by neurophysiology. We then show that endogenous factors (strategic slowing, attention) do not affect visuomotor deadtime. Therefore, visuomotor deadtime consistently satisfies widespread selective influence assumptions, in contrast to non-decision time parameters from model fits. Last, contrasting empirically observed visuomotor deadtime with non-decision
time estimates from the EZ, DDM and LBA models, we conclude that non-decision time parameter from these models is unlikely to consistently reflect visuomotor delays, neither at a group level nor for individual differences, in contrast to a widely held assumption.
relying on models to estimate it. Here, we propose a biological definition of decision that includes perceptual discrimination and action selection, and in turn explicitly equates non-decision time with the minimum sensorimotor delay, or “deadtime”. We show how this delay is directly observable in behavioural data, without modelling assumptions, using the visual interference
approach. We apply this approach to 11 novel and archival datasets from humans and monkeys gathered from multiple labs. We validate the method by showing that visual properties (brightness, colour, size) consistently affect empirically measured visuomotor deadtime, as predicted by neurophysiology. We then show that endogenous factors (strategic slowing, attention) do not affect visuomotor deadtime. Therefore, visuomotor deadtime consistently satisfies widespread selective influence assumptions, in contrast to non-decision time parameters from model fits. Last, contrasting empirically observed visuomotor deadtime with non-decision
time estimates from the EZ, DDM and LBA models, we conclude that non-decision time parameter from these models is unlikely to consistently reflect visuomotor delays, neither at a group level nor for individual differences, in contrast to a widely held assumption.
| Original language | English |
|---|---|
| Journal | Psychological Review |
| Early online date | 11 Jul 2024 |
| DOIs | |
| Publication status | E-pub ahead of print - 11 Jul 2024 |
Bibliographical note
Copyright © 2024 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/by/4.0). This license permits copying and redistributing the work in any medium or format, as well as adapting the material for any purpose, even commercially.Data Access Statement
All the data sets collected by the authors are available on the Open Science Framework, alongside the code used for analyses (any other study material, such as experiment code and raw data files, is available upon request). All links are inserted in the relevant methods section, where each data set is introduced. The raw data files from studies collected elsewhere can be requested to the corresponding author, Aline Bompas, pending permission from their owners (identified in the article and the acknowledgements). The code used to produce all the empirical and modeling figures in the article is available on the Open Science Framework (https://osf.io/gz9uc/, and this repository also contains links to all the other shared data sets mentioned above.Keywords
- reaction time
- sensorimotor processes
- vision
- decision model