Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches

Harris, Alison; Hutcherson, Cendri A.

doi:10.1002/wcs.1586

Cited by 5 publications

(4 citation statements)

References 140 publications

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“…This sham condition seeks to achieve the sensation of tDCS stimulation by the participant for a block of experimental trials, but to not actually stimulate during those trials (e.g., see Au et al, 2021). We propose an experimental design with one experimental condition (1) in which the experimenters stimulate brain areas expected to be involved in decision-making (perhaps placing stimulation electrodes on the scalp over parietal cortices), and a control condition (2) in which experimenters stimulate brain areas not expected to be involved in decision-making, say placing tDCS electrodes on the scalp over the temporal cortex. We could also include a different sham condition, another experimental control, (3) in which tDCS electrodes do not stimulate the brain but current is still injected into the body, for instance placing and activating tDCS electrodes over the neck musculature in the back of the head.…”

Section: Hypothetical Experimentsmentioning

confidence: 99%

“…Therefore, we can inspect the posterior distributions themselves to calculate the probability of observing certain values of a parameter given the specific model and data. For instance, the posterior distributions that result from fitting Model 2B to data from Experiment 2 would result in posterior distributions for all parameters, including both effect parameters ξ 1 j1 for experimental condition (1) and ξ 1 j2 for experimental condition (2). We could calculate the probability that each effect parameter is greater than 0.5 (for instance) by finding the proportion of posterior samples that are above 0.5, thus approximating the area under the curves of the posterior distributions, and thus approximating the probability (e.g., evidence) that each effect parameter is greater than 0.5.…”

Section: Assessing Posterior Distributionsmentioning

confidence: 99%

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A tutorial on fitting joint models of M/EEG and behavior to understand cognition

Nunez,

Fernandez,

Srinivasan

et al. 2024

Behav Res

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We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

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Section: Hypothetical Experimentsmentioning

confidence: 99%

Section: Assessing Posterior Distributionsmentioning

confidence: 99%

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

Nunez,

Fernandez,

Srinivasan

et al. 2024

Behav Res

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show abstract

“…urge to make a choice (e.g. forced to report a choice in the presence of a set time deadline) (Harris & Hutcherson, 2022 ). Thus, additional features are required to be incorporated into the basic DDM, leading to various DDM variants.…”

Section: Introductionmentioning

confidence: 99%

Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

Asadpour,

Tan,

Lenfesty

et al. 2024

Comput Brain Behav

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Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting.

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“…urge to make a choice (e.g. forced to report a choice in the presence of a set time deadline) (Harris & Hutcherson, 2022). Thus, additional features are required to be incorporated into the basic DDM, leading to various DDM variants.…”

Section: Introductionmentioning

confidence: 99%

Time-variant gain in noise but not signal in drift-diffusion model leads to slower error decisions

Tan¹,

Faraz²,

Lenfesty³

et al. 2023

Preprint

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Sequential sampling based mathematical models of decision-making involve evidence accumulation over time during decision formation and have been successful in capturing choice behaviour. A popular model is the drift-diffusion model (DDM). To capture finer aspects of choice behaviour, such as differences in choice reaction times, time-variant gain features that imitate urgency signals have been implemented in standard DDM. In particular, a previous DDM with a time-variant gain can replicate slower error decisions than correct decisions. However, the time-variant gain of the model, and other similar time-variant gain-based models, often implement time-variant gain on both signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether the gain effects on just the signal or noise feature can lead to different choice behaviour. Here, using computational simulations and theoretical approximations, we used a previous time-variant gain DDM and systemically showed that time-variant gain only on the DDM’s noise suffices to produce slower error choices while time-variant gain only on the drift rate speeds up error choices. In addition, the former was associated with more skewness and kurtosis on correct RT distributions while the latter led to more skewness and kurtosis in error RT distributions. Thus, time-variant gain in DDM’s different components can lead to different choice outcomes, which can potentially be used for more systematic data fitting.

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Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches

Cited by 5 publications

References 140 publications

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

Time-variant gain in noise but not signal in drift-diffusion model leads to slower error decisions

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