Showing papers by "Paul Sajda published in 2023"

Pupillary response is associated with the reset and switching of functional brain networks during salience processing

Abstract: The interface between processing internal goals and salient events in the environment involves various top-down processes. Previous studies have identified multiple brain areas for salience processing, including the salience network (SN), dorsal attention network, and the locus coeruleus-norepinephrine (LC-NE) system. However, interactions among these systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, EEG, and fMRI during an auditory oddball paradigm. The analyses of EEG and fMRI data uncovered spatiotemporally organized target-associated neural correlates. By modeling the target-modulated effective connectivity, we found that the target-evoked pupillary response is associated with the network directional couplings from late to early subsystems in the trial, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with the pupil-indexed LC-NE system in the reset and switching of cortical networks, and shed light on their implications in various cognitive processes and neurological diseases.

Neurophysiological markers of emotion regulation predict efficacy of entrepreneurship education

Abstract: Recent evidence shows that programs targeting the socio-emotional dimensions of entrepreneurship-e.g., resilience, personal initiative, and empathy-are more highly correlated with success along with key business metrics, such as sales and survival, than programs with a narrow, technical bent-e.g., accounting and finance. We argue that programs designed to foster socio-emotional skills are effective in improving entrepreneurship outcomes because they improve the students' ability to regulate their emotions. They enhance the individuals' disposition to make more measured, rational decisions. We test this hypothesis studying a randomized controlled trial (RCT, RCT ID: AEARCTR-0000916) of an entrepreneurship program in Chile. We combine administrative data, surveys, and neuro-psychological data from lab-in-the-field measurements. A key methodological contribution of this study is the use of the electroencephalogram (EEG) to quantify the impact of emotional responses. We find that the program has a positive and significant impact on educational outcomes and, in line with the findings of other studies in the literature, we find no impact on self-reported measures of socio-emotional skills (e.g., grit and locus of control) and creativity. Our novel insight comes from the finding that the program has a significant impact on neurophysiological markers, decreasing arousal (a proxy of alertness), valence (a proxy for withdrawal from or approachability to an event or stimuli), and neuro-psychological changes to negative stimuli.

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks

Abstract: Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed for classical sparse coding models have largely been unexplored in the context of deep models. We propose a non-parametric iterative algorithm for learning discrete latent representations in such deep models. Additionally, to learn scale invariant discrete features, we propose local data scaling variables. Lastly, to encourage sparsity in our representations, we propose a Beta-Bernoulli process prior on the latent factors. We evaluate our spare coding model coupled with different likelihood models. We evaluate our method across datasets with varying characteristics and compare our results to current amortized approximate inference methods.