Generative Artificial Intelligence (AI) is a rapidly expanding field that aims to develop machines capable of performing tasks that were previously considered unique to humans, such as learning, reasoning, problem-solving, and decision-making. The recent release of several tools based on AI (e.g. ChatGPT) has sparked debates on the potential of this technology and garnered widespread attention in the mainstream media.Using a socio-psychological approach, in three studies (total N = 410), we demonstrate that when faced with Generative AI’s ability to reproduce the complexity of human cognitive capabilities, participants reported significantly higher negative emotions than those in the control group. In turn, negative emotions elicited by a specific type of AI (e.g. generative AI) were associated to the perception of threat extended to AI technologies as a whole, understood as a threat to various aspects of human life, including jobs, resources, identity, uniqueness, and value.Our findings emphasise the importance of considering emotional and societal impacts when developing and deploying advanced AI technologies and implementing responsible guidelines to minimise adverse effects. As AI technology advances, addressing public concerns and regulating its usage is crucial for the benefit of society. To achieve this goal, collaboration between experts, policymakers, and the public is necessary.

Facial emotion processing by the brain plays a decisive role in human social interactions. This signal helps us interpret and predict people's behaviours. However, other social signals such as human voices or human body odours may facilitate or impair the identification of facial expressions. Here we studied the effects of emotional human body odours on face processing by measuring evoked neural responses and brain connectivity using the electroencephalogram (EEG). We used an emotion recognition task in which the participants attributed an emotion (i.e. happy vs fearful) to a presented face image while simultaneously exposed to emotional body odours. First, we measured face related potentials (FRP)s including P100 and N170 components. Statistical analyses revealed significant differences among FRPs recorded in different odour conditions. Second, we used a hierarchical Bayesian approach including a group dynamic causal model (DCM) followed by parametric empirical Bayes (PEB) to characterize the brain network explaining differences between FRPs. Our preliminary results suggested that different brain networks contribute to neutral face processing in the presence of different emotional body odours.

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue natural educational conversations, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

Chemosensory communication is known as an effective way to influence the human emotion system. Phenomena like food selection or motivation, based on chemical signals, present a unique pathway between chemosensory and emotion systems. Human chemosignals (i.e. sweat) which are produced during different emotional states contain associated distinctive odors and are able to induce same emotions in other people. For instance, sweat is known as a social chemosignal participating in social interaction. Chemosignal perception engages a distributed neural network which has not been well characterized yet. In this paper, we use functional magnetic resonance imaging (fMRI) to investigate the neural circuits underlying social emotional chemosignal processing. Chemosignals associated with disgust and neutral conditions were used to induce specific emotional states in fMRI participants during a healthy food judgement. We performed fMRI analysis with the aim of detecting active areas in the brain, followed by a dynamic causal modeling (DCM) analysis. fMRI analysis revealed functional activity in the fusiform face area (FFA), amygdala (AMG) and orbitofrontal cortex (OFC). In order to determine the effective connectivity among these regions as a result of emotional chemosignal processing, a set of dynamic causal models is proposed. Estimating parameters of the proposed models shows that social chemosignals modulate the connections between FFA, AMG and OFC. The results indicate that social chemosignals of disgust converge on orbitofrontal cortex - an area which is a critical region for object appraisal and valuation - after first influencing fusiform face area and amygdala.

We present a spatio-temporal attention relocation (STARE) method, an information-theoretic approach for efficient detection of simultaneously occurring structured activities. Given multiple human activities in a scene, our method dynamically focuses on the currently most informative activity. Each activity can be detected without complete observation, as the structure of sequential actions plays an important role on making the system robust to unattended observations. For such systems, the ability to decide where and when to focus is crucial to achieving high detection performances under resource bounded condition. Our main contributions can be summarized as follows: 1) information-theoretic dynamic attention relocation framework that allows the detection of multiple activities efficiently by exploiting the activity structure information and 2) a new high-resolution data set of temporally-structured concurrent activities. Our experiments on applications show that the STARE method performs efficiently while maintaining a reasonable level of accuracy.