Individual Differences in the Neural Dynamics of Response Inhibition

Abstract

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design

“…Although it has been used in many different disciplines, such as astronomy ( Thrane & Talbot, 2019 ), ecology ( Reum, Hovel, & Greene, 2015 ; Wikle, 2003 ), genetics ( Storz & Beaumont, 2002 ), machine learning ( Li & Perona, 2005 ), cognitive science ( Ahn, Krawitz, Kim, Busmeyer, & Brown, 2011 ; Lee, 2006 ; Lee & Mumford, 2003 ; Merkle, Smithson, & Verkuilen, 2011 ; Molloy, Bahg, Li, Steyvers, Lu, & Turner, 2018 ; Molloy, Bahg, Lu, & Turner, 2019 ; Rouder & Lu, 2005 ; Rouder et al, 2003 ; Wilson et al, 2020 ) and visual acuity ( Zhao, Lesmes, Dorr, & Lu, 2021 ), HBM has not been applied to analyze the CSF. Here, we develop a three-level HBM to model the entire CSF dataset in a single-factor (luminance), multi-condition (3 luminance conditions), and within-subject experiment design.…”

Linking Models with Brain Measures

“…An alternative approach, such as in Bayesian hierarchal modelling, is to assume that individuals belong to a common family such that estimates of individual inform the estimates for others. When data are noisy, hierarchal approaches that link estimates may offer advantages and have been used successfully in modelling individual differences in cognitive control (Molloy et al, 2019). When using an independent or hierarchal approach, the conclusion that cognitive models can reflect a reality at both the behavioural and neural levels for individual participants is exciting and demonstrates how modelling can extract fine-grain information.…”

Hierarchical Bayesian perceptual template modeling of mechanisms of spatial attention in central and peripheral cuing

“…Building on the success of the original PTM as a generative model of human performance, the goal of the current study is to develop a new hierarchical Bayesian perceptual template model (HBPTM) to model the trial-by-trial data from all individuals and conditions in an external noise experiment within a single hierarchical structure. With hyperparameters and parameters at multiple levels and conditional dependencies that share information across levels, hierarchical Bayesian models have been developed to compute the joint posterior distribution of all the hyperparameters and parameters, capture their statistical relationships, and enable statistical inferences at multiple levels in a wide range of applications ( Ahn, Krawitz, Kim, Busemeyer, & Brown, 2011 ; Kruschke, 2015 ; Lee, 2006 ; Lee, 2011 ; Lee & Mumford, 2003 ; Merkle, Smithson, & Verkuilen, 2011 ; Molloy et al, 2018 ; Molloy, Bahg, Lu, & Turner, 2019 ; Rouder & Lu, 2005 ; Rouder, Sun, Speckman, Lu, & Zhou, 2003 ), whereas the PTM provides an excellent likelihood function that relates model parameters to trial-by-trial performance. Here, we developed an HBPTM by incorporating the PTM into a hierarchical Bayesian model (HBM) framework to model the data from a published spatial cuing study of attention ( Lu & Dosher, 2000 ), and compared the performance of the HBPTM to that of a Bayesian Inference Procedure (BIP), which separately infers the posterior distributions of the model parameters for each individual observer without the hierarchical structure of the population level hyperparameters.…”