arXiv subjects

Joydeep Bhattacharya

Publications and source records attributed to Joydeep Bhattacharya.

4 recordsLinked to original sources

arXiv2025

Resting-State EEG Network Profiles Associated with Creative Engagement and Creative Self-Efficacy

Creativity is a core cognitive capacity underlying innovation and adaptive problem solving, yet how it is represented in the brain's intrinsic functional architecture is not fully understood. While resting-state fMRI studies have identified large-scale network correlates associated with differences in creativity, EEG provides the temporal resolution for examining oscillatory dynamics contributing to intrinsic network organization. We examined whether resting-state EEG connectivity patterns are associated with individual differences across multiple creativity-related measures. Thirty healthy young adults completed a multidimensional creativity battery comprising the Inventory of Creative Activities and Achievements (ICAA), the Divergent Association Task (DAT), the Matchstick Arithmetic Puzzles Task (MAPT) and a Self-rating (SR) of creative ability. Graph-theoretical analyses of alpha-band functional connectivity revealed two participant groups, each with distinct patterns of neural activity: Cluster 1 showed reduced global connectivity with relatively preserved left frontal connectivity and greater network modularity; Cluster 0 exhibited stronger overall connectivity strength, reduced modularity and higher local clustering. Notably, Cluster 1 reported higher self-rated creative ability and more frequent engagement in real-world creative activities. These findings suggest that resting-state EEG connectivity patterns are associated with variation in creative self-efficacy and creative engagement, highlighting characteristic patterns of alpha-band network organization observed at rest.

q-bio.NC↗

arXiv2023

Businesses in high-income zip codes often saw sharper visit reductions during the COVID-19 pandemic

As the COVID-19 pandemic unfolded, the mobility patterns of people worldwide changed drastically. While travel time, costs, and trip convenience have always influenced mobility, the risk of infection and policy actions such as lockdowns and stay-at-home orders emerged as new factors to consider in the location-visitation calculus. We use SafeGraph mobility data from Minnesota, USA, to demonstrate that businesses (especially those requiring extended indoor visits) located in affluent zip codes witnessed sharper reductions in visits (relative to pre-pandemic times) outside of the lockdown periods than their poorer counterparts. To the extent visits translate into sales, we contend that post-pandemic recovery efforts should prioritize relief funding, keeping the losses relating to diminished visits in mind.

cs.CY↗

arXiv2020

Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19

We conduct a unique, Amazon MTurk-based global experiment to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the well-established fact that disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. We define prediction bias as the systematic error arising from faulty prediction of the number of cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our design permits us to identify the root of this under-prediction as an EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Our data reveals that the "degree of convexity" reflected in the predicted path of the disease is significantly and substantially lower than the actual path. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. We find that individuals who exhibit EGPB are also more likely to reveal markedly reduced compliance with the WHO-recommended safety measures, find general violations of safety protocols less alarming, and show greater faith in their government's actions. A simple behavioral nudge which shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Clear communication of risk via raw numbers could increase accuracy of risk perception, in turn facilitating compliance with suggested protective behaviors.

econ.GN↗

arXiv2005

Nonlinear multivariate analysis of Neurophysiological Signals

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependences between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

nlin.CD↗