Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study.

Introduction: This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. Methods: The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Results: Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. Discussion: This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception.

Introduction: Political neuromarketing is an emerging interdisciplinary field integrating marketing, neuroscience, and psychology to decipher voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. Methods: This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. Results: This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. Discussion: The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science, and machine learning, in turn enabling predictive insights into voter preferences and behavior.

Olfactory bulb volume and cortical thickness evolve during sommelier training.

Brain plasticity is essential for experts to acquire the abilities they need. Sommeliers are olfaction experts who display differences in olfactory regions in the brain that correlate with greater olfactory abilities. While most studies on this topic are cross-sectional, we used a longitudinal design and invited 17 sommelier students at the start and end of their training then to compare them to 17 control students to study the effects of training-related brain plasticity. After a year and a half, 5 sommelier students and 4 control students dropped out, leading to 12 sommelier students versus 13 controls. We used magnetic resonance imaging to measure cortical thickness and olfactory bulb volume, as this structure plays a crucial role in olfactory processing. We used the Sniffin' Sticks test to evaluate olfactory performance. During training, olfactory bulb volume increased in sommelier students while there was no significant change in the control group. We also observed that thickness of right entorhinal cortex increased, and cortical thickness decreased in other cerebral regions. Our olfactory tests did not reveal any significant changes in sommelier students. In conclusion, this is the first longitudinal study to report an increase in olfactory bulb volume in olfaction experts in line with the notion of effects of ecological training-related brain plasticity. The mixed results about cortical thickness might be explained by a "overproduction-pruning" model of brain plasticity, according to which the effects of training-related plasticity are non-linear and simultaneously involve different processes.

Synaesthesia is linked to a distinctive and heritable cognitive profile.

This study argues that there exists, in the general population, a distinctive profile of cognitive traits that predisposes people to develop synaesthesia (termed a 'synaesthetic disposition'). This consists of more vivid mental imagery, better episodic memory, and greater attention-to-detail (amongst others things). Using a machine-learning classifier, we show that it is possible to distinguish synaesthetes from others using only standard cognitive and personality measures. Importantly, people with multiple forms of synaesthesia have a more distinctive profile (i.e., they can be more accurately classified). This suggests that whilst the presence/absence of synaesthesia is dichotomous, the underlying causal mechanisms are continuous. Moreover, we provide evidence that the cognitive profile constitutes a heritable endophenotype. Non-synaesthetic relatives of synaesthetes are cognitively similar to synaesthetes. This provides new insights into why synaesthesia might have evolved (i.e., it is possible to have the cognitive benefits of synaesthesia in the absence of the anomalous experiences). The notion of a synaesthetic disposition represents a novel, quantifiable individual difference in cognition/personality. This paves the way for determining if this is linked to a distinctive pattern of clinical vulnerabilities.