Background: The increasing deployment of conversational artificial intelligence (AI) in mental health interventions necessitates an evaluation of their efficacy in rectifying cognitive biases and recognizing affect in human-AI interactions. These biases are particularly relevant in mental health contexts as they can exacerbate conditions such as depression and anxiety by reinforcing maladaptive thought patterns or unrealistic expectations in human-AI interactions. Objective: This study aimed to assess the effectiveness of therapeutic chatbots (Wysa and Youper) versus general-purpose language models (GPT-3.5, GPT-4, and Gemini Pro) in identifying and rectifying cognitive biases and recognizing affect in user interactions. Methods: This study used constructed case scenarios simulating typical user-bot interactions to examine how effectively chatbots address selected cognitive biases. The cognitive biases assessed included theory-of-mind biases (anthropomorphism, overtrust, and attribution) and autonomy biases (illusion of control, fundamental attribution error, and just-world hypothesis). Each chatbot response was evaluated based on accuracy, therapeutic quality, and adherence to cognitive behavioral therapy principles using an ordinal scale to ensure consistency in scoring. To enhance reliability, responses underwent a double review process by 2 cognitive scientists, followed by a secondary review by a clinical psychologist specializing in cognitive behavioral therapy, ensuring a robust assessment across interdisciplinary perspectives. Results: This study revealed that general-purpose chatbots outperformed therapeutic chatbots in rectifying cognitive biases, particularly in overtrust bias, fundamental attribution error, and just-world hypothesis. GPT-4 achieved the highest scores across all biases, whereas the therapeutic bot Wysa scored the lowest. Notably, general-purpose bots showed more consistent accuracy and adaptability in recognizing and addressing bias-related cues across different contexts, suggesting a broader flexibility in handling complex cognitive patterns. In addition, in affect recognition tasks, general-purpose chatbots not only excelled but also demonstrated quicker adaptation to subtle emotional nuances, outperforming therapeutic bots in 67% (4/6) of the tested biases. Conclusions: This study shows that, while therapeutic chatbots hold promise for mental health support and cognitive bias intervention, their current capabilities are limited. Addressing cognitive biases in AI-human interactions requires systems that can both rectify and analyze biases as integral to human cognition, promoting precision and simulating empathy. The findings reveal the need for improved simulated emotional intelligence in chatbot design to provide adaptive, personalized responses that reduce overreliance and encourage independent coping skills. Future research should focus on enhancing affective response mechanisms and addressing ethical concerns such as bias mitigation and data privacy to ensure safe, effective AI-based mental health support.

This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.

The unprecedented development of medical informatics is constantly transforming the concept of expertise in medical sciences in a way that has far-reaching consequences for both the theory of knowledge and the philosophy of informatics. Deep medicine is based on the assumption that medical diagnosis should take into account the wide array of possible health factors involved in the diagnostic process, such as not only genome analysis alone, but also the metabolome (analysis of all body metabolites important for e.g. drug-drug interactions), microbiome (i.e. analysis of all bodily microorganisms interacting with host cells) or exposome (analysis of all environmental factors triggering potentially harmful cell mutations, such as UV radiation, heavy metals, various carcinogens, etc.). Deep data analysis is of tantamount importance for personalized diagnosis but, at the same time, hardly achievable by a regular human being. However, adequately designed artificial intelligence (e.g. a deep neural network) can undeniably be of great help for finding correlations between symptoms and underlying diseases. Nowadays AI applies to nearly every aspect of medicine, starting from the data analysis of scientific literature, through the diagnostic process, to the act of communication between physicians and their patients. Medical image processing algorithms greatly enhance the chances of successful recognition of melanoma or intestinal polyps. Communication tools designed for physicians use techniques known from social media to provide users with an opportunity to consult the case with colleagues from the same discipline. Natural language processing tools significantly improve doctor-patient communication by the automation of note-taking. Is this enough to support the claim that the non-epistemic competences in medicine are becoming more and more important? Can we attribute expertise only to a person? How is medical informatics changing the way most people usually understand human-computer interactions?

Cognitive biases can be roughly divided into two categories, namely, the cognitive artifacts and the cognitive adaptations. Cognitive artifacts, such as the base rate neglect affecting the reliability of probabilistic decision-making strategies, are often triggered by the cognitively unnatural situations, i.e. occurrences our brain is not sufficiently adapt to interpret because of their recent origin, like in the case of scientifically rigorous thinking. The second category comprises the biases described by the error management theory (emt) or, in other words, the direct behavioral adaptations purposefully distorting the way people assess some potentially dangerous situations. The rationale behind the emt is to avoid some supposedly lethal actions by the oversensitization to certain disease-associated or danger-associated stimuli. The behavioral immune system (bis) is, together with the innate and adaptive immunity, an important barrier against a variety of pathogens. However, its cognitive consequences are certainly not-trivial ones, beginning with certain types of food aversion and ending with cultural stereotyping, such as racism or xenophobia.

The chapter explores the phenomenon of intuitive diagnostic decision-making in psychiatry through an interdisciplinary lens, focusing on the praecox feeling (PF), i.e., a clinician's immediate, pre-reflective sense of 'bizarreness' in encounters with patients with schizophrenia. The chapter also explores the related phenomena of intuitive apprehension of borderline personality disorder and autism spectrum condition (ASC). Drawing on predictive coding theory, phenomenological psychopathology, evolutionary psychology, and computational psychiatry, we argue that the intuitive recognition of mental illness arises from violations of clinicians' hierarchical social priors, manifesting as unresolved prediction errors. These discrepancies, often triggered by subtle socio-emotional or interactional anomalies, are not merely subjective impressions but reflect embodied and affective disruptions in expected intersubjective synchrony. Phenomenologically, PF marks a breakdown in typification and intercorporeal resonance, undermining the intuitive grasp of another's experiential world. Predictive coding frameworks align with this perspective, suggesting that clinicians' brains flag such violations as persistent social prediction errors, particularly salient in disorders characterized by disturbed affective attunement, impaired communicative reciprocity, or impaired mentalizing capacity. In borderline personality disorder, intense emotional demands and boundary violations elicit hypervigilance and spatial constriction in the clinician's experience. In autism, divergences in embodied empathy give rise to a pre-reflective 'sense of distinct communicative patterns. We also propose that PF may serve as an evolved clinical heuristic, i.e., one that is adaptive in ancestral environments for detecting deviations from social norms and refined through professional experience. However, it is vulnerable to cognitive biases and must be balanced with analytical reasoning and structured assessment. Ultimately, recognizing the diagnostic value of intuitive experiences fosters a more personalized psychiatry grounded in embodied cognition, affective resonance, and intersubjective understanding.