Gaussian Process Linking Functions for Mind, Brain and Behavior

Abstract

The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., EEG, fMRI) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models (Turner, Forstmann, & Steyvers, 2019). Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proven prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, non-parametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fit to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data, and reveals interesting latent cognitive dynamics. Finally, we provide a test of the model’s generalizability by assessing its predictive accuracy in a cross-validation test.

A regularization method for linking brain and behavior.

“…In the joint modeling framework, fluctuations in neural data are statistically mapped to fluctuations in the parameters of a cognitive model. There are many types of statistical or mathematical maps that can be formed (see Turner, Palestro, et al, 2019, for a review), and typically the map follows a parametric form (Turner, Forstmann, et al, 2019), although nonparametric functional forms are also possible (Bahg et al, 2020). Predominantly, the probabilistic map that is used is a multivariate normal distribution (Palestro et al, 2018; Turner, Forstmann, et al, 2013; Turner et al, 2015; Turner et al, 2016; Turner, Wang, et al, 2017).…”

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

“…Moreover, we combining latent processes and the dynamics underlyings of varied modalities (e.g. bahavioral data, EEG, fMRI and so forth) could yield more insight into the neurocognitive role of spatial attention ( 28,(79)(80)(81)(82)(83) ).…”

A general integrative neurocognitive modeling framework to jointly describe EEG and decision-making on single trials

“…These neuro-cognitive models utilize trial-or individual-level signatures of EEG or magnetoencephalography (MEG) signals to assess, constrain, replace, or even add cognitive parameters in models (Nunez et al, 2017(Nunez et al, , 2022. Moreover, Turner et al have proposed many approaches for directly or indirectly relating neural data to a cognitive model; for instance, the field of model-based cognitive neuroscience has used BOLD responses and EEG waveforms simultaneously and separately to predict and constrain cognitive parameters and behavioral data (Turner et al, 2013(Turner et al, , 2016(Turner et al, , 2019Bahg et al, 2020;Kang et al, 2021). Also, Nunez et al have introduced some neuro-cognitive models to study the effect of selective attention, the role of visual encoding time (VET), and the relationship of readiness potentials (RPs) in motor cortical areas to evidence accumulation during perceptual decision-making tasks (Nunez et al, 2017(Nunez et al, , 2019Lui et al, 2021).…”