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on Neuroeconomics |
| By: | Gareth Markel (George Mason University); Jonathan Beauchamp (George Mason University); Rafael Ahlskog (Uppsala University); Joakim Coleman Ebeltoft (University of Oslo); René Mõttus (Department of Psychology, University of Edinburgh); Sven Oskarsson (Department of Government); Uku Vainik (University of Tartu); Eivind Ystrom (University of Oslo) |
| Abstract: | A consequence of Mendel’s First Law is that siblings’ genetic relatedness varies randomly (with a mean of 50% and a standard deviation of ∼4%). We use molecular genetic data to compute the genetic relatedness of ∼80, 000 sib pairs. We then compare the pairs’ genetic relatedness to their similarity on 15 outcomes in the cognitive and educational, labor market, risk taking, health, and anthropometric domains, to estimate the relative importance of genetic (i.e., heritability) and family environmental influences on each outcome. We find evidence of sizeable genetic influences on risk tolerance, subjective wellbeing, cognitive performance, height, and BMI, and robust evidence of family environmental influences on educational attainment and labor market outcomes. |
| Keywords: | heritability, family environment, molecular genetics |
| JEL: | A12 I12 J24 J30 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:hka:wpaper:2025-004 |
| By: | Olesja Lammert (Paderborn University) |
| Abstract: | Research indicates that anger is a prevalent emotion in human-technology interactions, often leading to frustration, rejection and reduced trust, significantly impacting user experience and acceptance of technology. Particularly in high-risk or uncertain situations, where AI explanations are intended to help users make more informed decisions, decision-making is influenced by emotional factors, impairing understanding and leading to suboptimal choices. While XAI research continues to evolve, greater consideration of users' emotions and individual characteristics remains necessary. Broadening empirical studies in this area could foster a more comprehensive understanding of decision-making processes following explanations, especially in relation to the interaction between emotions and cognition. In response, this study seeks to contribute to this area by employing an experimental design to examine the effects of AI explanations and emotion regulation on user reliance and trust of emotional users. The results provide a foundation for future human-centered research in XAI, focusing on the impact of emotions and cognition in human-technology interactions. |
| Keywords: | human-centered XAI, explanation strategy, emotion induction, emotions, emotion regulation, cognitive reappraisal nudge, decision-making, behavioral and psychological decision-making factors, user reliance, trust, empirical study |
| JEL: | C91 D81 C88 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:139 |
| By: | Francesca Barigozzi; Pietro Biroli; Chiara Monfardini; Natalia Montinari; Elena Pisanelli; Sveva Vitellozzi |
| Abstract: | This paper introduces a novel, scalable methodology to measure individual perceptions of gaps in mental load - the cognitive and emotional burden associated with organizing household and childcare tasks - within heterosexual couples. Using original data from the TIMES Observatory in Italy, the study combines time-use diaries with new survey indicators to quantify cognitive labor, emotional fatigue, and the spillover of mental load into the workplace. Results reveal systematic gender asymmetries: women are significantly more likely than men to bear organizational responsibility for domestic tasks, report lower satisfaction with this division, and experience higher emotional fatigue. These burdens are underestimated by their partners. The effects are particularly pronounced among college-educated and employed women, who also report greater spillovers of family responsibilities than men during paid work hours. The perceived responsibility for managing family activities is more strongly associated with within-couple gaps in time use than with the absolute time spent on their execution, underscoring the relational and conflictual nature of mental load. |
| JEL: | J16 J22 D91 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:bol:bodewp:wp1203 |
| By: | Venkat Ram Reddy Ganuthula; Krishna Kumar Balaraman |
| Abstract: | As artificial intelligence becomes increasingly integrated into professional and personal domains, traditional metrics of human intelligence require reconceptualization. This paper introduces the Artificial Intelligence Quotient (AIQ), a novel measurement framework designed to assess an individual's capacity to effectively collaborate with and leverage AI systems, particularly Large Language Models (LLMs). Building upon established cognitive assessment methodologies and contemporary AI interaction research, we present a comprehensive framework for quantifying human-AI collaborative intelligence. This work addresses the growing need for standardized evaluation of AI-augmented cognitive capabilities in educational and professional contexts. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.16438 |
| By: | Curry, Joshua Daniel |
| Abstract: | This paper introduces the Vox Cognita framework, which functions as both an assessment tool and a developmental framework for verbal intelligence. By positioning verbal expression as a measurable and developable cognitive function, Vox Cognita offers insights into dimensions of intelligence often overlooked by traditional assessment methods. The framework identifies five core components of verbal intelligence and implements an innovative adaptive assessment methodology that evolves with user capabilities. Preliminary findings from pilot testing demonstrate the system’s ability to identify dimensional strengths and growth opportunities. The paper concludes with a discussion of applications across educational, professional, and personal domains and outlines directions for future research and validation. |
| Date: | 2025–04–28 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:9s7fc_v1 |
| By: | Magaloni, Beatriz; Marinkovic Dal Poggetto, Sofía; Murphy, Tommy E.; Pucci, Florencia; Serra Fernández, Beatriz |
| Abstract: | Cognitive behavioral therapy (CBT) has become a powerful and effective tool to deal with violence in many at-risk areas in the world. However, its use for gender-based violence (GBV) and dating violence, although promising, has been limited and used as a response service for survivors, rather than for prevention. To understand to what extent such interventions can help provide tools and skills to young people in their impressionable years to produce behavioral changes that prevent GBV, we carried out such an intervention among high school students in the municipality of Ecatepec in Mexico. We assessed the intervention with a randomized control trial. We introduce the novelty of collecting objective measures from automated neuropsychological tests to explore whether CBT might be functioning through the development of subjects' executive functions. Results from this intervention fail to show any clear change in self-reported violence. They do show, however, impacts on executive functions related to violence, such as emotional recognition and inhibitory control skills. |
| JEL: | J16 I31 Z18 H43 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14090 |
| By: | Holmberg, Ulf PhD (Swedbank) |
| Abstract: | This paper presents the Cognitive Entropy Shift Model (CESM), a structured framework for exploring how distinct cognitive states, specifically passive emotional attention and goal-directed intention may influence probabilistic systems by reducing entropy. Drawing on principles from information theory and Bayesian-inspired probability updating, CESM conceptualizes consciousness as an informational constraint capable of subtly biasing outcomes in systems typically governed by randomness. To evaluate this framework, a two-year empirical study was conducted under controlled conditions using high-resolution data from a physical random number generator (RNG). CESM was used to predict when deviations from randomness would occur and the analysis revealed statistically significant deviations (t = -4.347, p < 0.001) during periods characterized by heightened emotional attention, with effect sizes in the range of 0.5-0.7%. These results aligned closely with CESM’s predictions. The effect also diminished with increasing spatial distance from the presumed source of influence, highlighting proximity as a potentially critical factor. In addition to presenting new empirical results, this paper also applies CESM retrospectively to earlier studies, offering a clear and testable reinterpretation of previously reported anomalies. By distinguishing between passive and active forms of cognitive engagement, and embedding them within a quantifiable statistical model, CESM provides a structured approach for examining whether, and under what conditions, cognitive states may correspond to subtle deviations in probabilities. The findings encourage further exploration into how consciousness relates to information, including potential effects across spatial distance, through a framework that supports the formulation of formal, testable hypotheses in advance of data collection, while remaining grounded in established scientific principles. |
| Date: | 2025–04–30 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:epbdt_v1 |