Exploring Neuropsychological Mechanism Guiding Food Craving

Chatterjee, Avishek (2022) Exploring Neuropsychological Mechanism Guiding Food Craving. PhD thesis, Indian Institute of Science Education and Research Kolkata.

Text (PhD thesis of Avishek Chatterjee (17RS012)) 17RS012.pdf - Submitted Version Restricted to Repository staff only Download (4MB)

Abstract

Experiencing food craving is nearly ubiquitous and has several negative pathological impacts prompting an increase in recent craving-related research. The aim of this thesis is to explore computationally the neural mechanisms underlying food craving through several manipulations of multisensory food exposure. We at first explored the neuropsychological correlates of food craving preference using psychophysical tasks on human participants considering their individual food preferences in a multisensory food exposure set-up. Participants were grouped into Liked Food Exposure (LFE), Disliked Food Exposure (DFE), and Neutral Control (NEC) based on their preference for sweet and savory items (i.e., chocolate and chips). Participants reported their momentary craving for the displayed food stimuli through desire scale and bidding scale (willingness to pay) pre and post multisensory exposure. Our results asserted the effect of the multisensory food exposure showing statistically significant increase in food craving for DFE participants postexposure to disliked food items. Using computational model and statistical methods we also show that desire for food does not necessarily translate to willingness to pay every time and instantaneous subjective valuation of food craving is an important parameter for subsequent action. Our results further demonstrate the role of parietal N200 and centro-parietal P300 in reversing craving preference and possibly points to the decrease of inhibitory control in up-regulating craving for disliked food. Apart from univariate ERP analysis, we have also performed a detailed single trial pattern analysis using CPCA, a multivariate technique, and FCPCA, our proposed supervised classification framework for functional data analysis. Further, we demonstrate the efficacy of FCPCA by applying it to both synthetic data sets and real time series data from diverse fields including but not limited to neuroscience, food science, medical sciences, and chemometrics. We also demonstrate the effect of multisensory food exposure to Indian sweet and savoury items through an exploratory data analysis.

Actions (login required)

View Item