Trusting Humans and Avatars: Behavioral and Neural Evidence

Over the past decade, information technology has dramatically changed the context in which economic transactions take place. Increasingly, transactions are computer-mediated, so that, relative to human-human interactions, human-computer interactions are gaining in relevance. Computer-mediated transactions, and in particular those related to the Internet, increase perceptions of uncertainty. Therefore, trust becomes a crucial factor in the reduction of these perceptions. To investigate this important construct, we studied individual trust behavior and the underlying brain mechanisms through a multi-round trust game. Participants acted in the role of an investor, playing against both humans and avatars. The behavioral results show that participants trusted avatars to a similar degree as they trusted humans. Participants also revealed similarity in learning an interaction partner’s trustworthiness, independent of whether the partner was human or avatar. However, the neuroimaging findings revealed differential responses within the brain network that is associated with theory of mind (mentalizing) depending on the interaction partner. Based on these results, the major conclusion of our study is that, in a situation of a computer with human-like characteristics (avatar), trust behavior in human-computer interaction resembles that of human-human interaction. On a deeper neurobiological level, our study reveals that thinking about an interaction partner’s trustworthiness activates the mentalizing network more strongly if the trustee is a human rather than an avatar. We discuss implications of these findings for future research

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Bayesian Hierarchical Predictive Coding of Human Social Behaviour

‘Bayesian hierarchical predictive coding of human social behaviour.’ Biological agents are the most complex systems humans encounter in their natural environment and it is critical to model other’s mental states correctly to predict their behaviour. To do this one has to generate a mental representation based on an internal neural model of the other agent (Chapter 1). Here we show, in a series experiments, that people use and update their Bayesian priors in social situations and explain how they create mental representations of others to guide action selection. We investigate the neural mechanisms and the brain connectivity that underlie these social processes and how they develop with age. In chapter 2, we show how experimentally induced prior experience with other people (here social inclusion or exclusion) influences the level of trust towards those people. In chapter 3, we describe an fMRI study using a social perspective-taking task that examines the developmental differences between adolescents and adults in the control of action selection by social information. Using the same task, in chapter 4, we investigate the effective connectivity between the activated regions with Dynamic causal modelling. In Chapter 5, we explore effective connectivity of fMRI data from the Human connectome project (Van Essen et al., 2012). During the task participants viewed animations of triangles moving either randomly or so that they evoke mental state attribution (Castelli et al., 2000). Chapter 6 concludes with a summary of the experiments and integrate them into existing research, as well as provide a critical synthesis of the findings in order to suggest future research directions. We interpret our findings in a hierarchical predictive coding framework, where agents try to create a neural model of the external world to minimize prediction errors, Bayesian surprise and free energy

Predicting human behavior in smart environments: theory and application to gaze prediction

Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors

Cognitive Decay And Memory Recall During Long Duration Spaceflight

This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

Addictions

Addiction, increasingly perceived as a heterogeneous brain disorder, is one of the most peculiar psychiatric pathologies in that its management involves various, often non-overlapping, resources from the biological, psychological, medical, economical, social, and legal realms. Despite extensive efforts from the players of these various fields, to date there are no reliably effective treatments of addiction. This may stem from a lack of understanding of the etiology and pathophysiology of this disorder as well as from the lack of interest into the potential differences among patients in the way they interact compulsively with their drug. This book offers an overview of the psychobiology of addiction and its current management strategies from pharmacological, social, behavioural, and psychiatric points of view