Same/opposite relational responding, a fundamental aspect of human symbolic cognition, allows the flexible generalization of stimulus relationships based on minimal experience. In this study, we demonstrate the emergence of arbitrarily applicable same/opposite relational responding within the Non-Axiomatic Reasoning System (NARS), a computational cognitive architecture designed for adaptive reasoning under uncertainty. Specifically, we extend NARS with an implementation of acquired relations, enabling the system to explicitly derive both symmetric (mutual entailment) and novel relational combinations (combinatorial entailment) from minimal explicit training in a contextually controlled matching-to-sample (MTS) procedure. Experimental results show that NARS rapidly internalizes explicitly trained relational rules and robustly demonstrates derived relational generalizations based on arbitrary contextual cues. Importantly, derived relational responding in critical test phases inherently combines both mutual and combinatorial entailments, such as deriving same-relations from multiple explicitly trained opposite-relations. Internal confidence metrics illustrate strong internalization of these relational principles, closely paralleling phenomena observed in human relational learning experiments. Our findings underscore the potential for integrating nuanced relational learning mechanisms inspired by learning psychology into artificial general intelligence frameworks, explicitly highlighting the arbitrary and context-sensitive relational capabilities modeled within NARS.

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI's state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

Insomnia is prevalent, emphasizing the need for effective and sustainable treatments. While short-term use of sleep medication is recommended, long-term use remains common, underscoring the necessity for psychological treatments like Cognitive Behavioral Therapy for Insomnia (CBT-I) in clinical practice. This study aimed to evaluate the effectiveness of guided Internet-Based Cognitive Behavioral Therapy for insomnia (ICBT-I) when integrated into general practice. Participants (n = 177) were recruited from 33 primary health care centers (PCCs) and enrolled in an eight-week guided ICBT-I program. Eligible participants were at least 18 years old and reported sleep problems significantly affecting their daily lives. Significant reductions in insomnia were observed, with large improvements in sleep disturbances. ISI scores decreased significantly from pre- to post-treatment (β = 9.368, p <.001, Hedges’ g = 1.40). Depression (β = 5.496, g = 0.68) and anxiety (β = 3.982, g = 0.56) also showed moderate improvements (p <.001). All sleep diary measures improved significantly (p <.001), and sleep medication use dropped from 48.6% at pretreatment to 17.5% at posttreatment (p <.001). These findings suggest that guided ICBT-I in primary care effectively reduces insomnia and improves mental health, with outcomes comparable to specialized care.

Objective: We examined whether the treatment effects from a previous RCT of Internet-delivered Emotional Awareness and Expression Therapy (I-EAET) for somatic symptom disorder were maintained 12 months after treatment.

Method: 12-month assessments of self-reported somatic symptoms, pain severity, and several secondary outcomes were compared with baseline and post-treatment levels within the I-EAET condition only, given that the waitlist control condition had already received treatment. Twenty-eight out of the original 37 participants (76%) in the I-EAET condition provided follow-up data.

Results: The beneficial effects of I-EAET on somatic symptoms observed at post-treatment were maintained at the 12-month follow-up (d = -0.22, 95% CI: -0.72 to 0.28), as well as for pain intensity (d = -0.02, 95% CI: -0.52 to 0.48). From pre-treatment to 12-month follow-up, there was a medium effect on somatic symptoms (d = 0.74, 95% CI 0.23 to 1.24), and a small, non-significant effect for pain intensity (d = 0.43, 95% CI -0.06 to 0.93). Response rates (at least 50% symptom reduction) at 12-month follow-up were 25% for somatic symptoms, and 12% for pain intensity.

Conclusion: I-EAET seems to have positive long-term effects for somatic symptom disorder. Larger studies with controls and comparisons to other treatments are needed.

Objectives: Interpersonal problems are a fundamental feature of depression, but study-level meta-analyses of their association with treatment outcome have been limited by heterogeneity in primary studies' analyses and reported results. We conducted a pre-registered individual participant data meta-analysis (IPD-MA) to examine this relationship for adult depression. This meta-analytic strategy can reduce variability by standardizing data analysis across primary studies. Methods: We included studies examining the efficacy of five treatments for adult depression and assessing interpersonal problems at baseline. One-stage IPD-MA was conducted with three-level mixed models to determine whether baseline overall interpersonal distress, agency, and communion predicted depressive symptom level at post-treatment, 12-month, and 24-month follow-up. The moderating effect of treatment type was also investigated. Results: Ten studies (including n = 1282 participants) met inclusion criteria. Only overall interpersonal distress was negatively related with outcomes at post-treatment (γ = 0.11, CI95[0.06, 0.16], r = 0.11), 12-month follow-up (γ = 0.17, CI95[0.08, 0.25], r = 0.17), and 24-month follow-up (γ = 0.16, CI95[0.05, 0.26], r = 0.16), indicative of smaller effect sizes. The agency and communion dimensions were not significantly related to outcome. Treatment type did not significantly moderate interpersonal distress-outcome associations. Discussion: Results show a small association between patient baseline overall interpersonal distress and subsequent depression treatment outcome in brief treatments for depression. Further studies might require to account for therapist effects.

Arbitrarily Applicable Relational Responding (AARR) is a cornerstone of human language and reasoning, referring to the learned ability to relate symbols in flexible, context-dependent ways. In this paper, we present a novel theoretical approach for modeling AARR within an artificial intelligence framework using the Non-Axiomatic Reasoning System (NARS). NARS is an adaptive reasoning system designed for learning under uncertainty. We introduce a theoretical mechanism called acquired relations, enabling NARS to derive symbolic relational knowledge directly from sensorimotor experiences. By integrating principles from Relational Frame Theory—the behavioral psychology account of AARR—with the reasoning mechanisms of NARS, we conceptually demonstrate how key properties of AARR (mutual entailment, combinatorial entailment, and transformation of stimulus functions) can emerge from NARS’s inference rules and memory structures. Two theoretical demonstrations illustrate this approach: one modeling stimulus equivalence and transfer of function, and another modeling complex relational networks involving opposition frames. In both cases, the system logically demonstrates the derivation of untrained relations and context-sensitive transformations of stimulus functions, mirroring established human cognitive phenomena. These results suggest that AARR—long considered uniquely human—can be conceptually captured by suitably designed AI systems, emphasizing the value of integrating behavioral science insights into artificial general intelligence (AGI) research. Empirical validation of this theoretical approach remains an essential future direction.

There is a growing interest in clinical interventions targeting emotion regulation difficulties across mental health conditions. Experiential dynamic therapies (EDTs) are transdiagnostic, affect-focused, short-term psychodynamic therapy models that emphasize in-session emotional processing. This review provides a 10-year update on the efficacy of EDTs for mood, anxiety, personality and somatic symptom disorders in adults and children/adolescents. A comprehensive search identified 57 randomized controlled trials (n = 4330) conducted in Western (k = 38; n = 3178) and non-Western countries (k = 19; n = 1152) between 1978 and 2024. Random-effects meta-analyses on primary outcomes indicated large, significant effects for EDTs compared to inactive controls at post-treatment (Hedge's g = −0.96; k = 41) and follow-up (g = −1.11; k = 20). Compared to active controls, effects were small and non-significant post-treatment (g = −0.17; k = 27) but became significant at follow-up (g = −0.40; k = 19), suggesting a potential modest long-term advantage of EDTs. Despite substantial heterogeneity (I2 > 75%), results remained robust in sensitivity analyses. Moderator analyses revealed few significant findings, indicating relative consistency across diagnostic groups, treatment formats and active comparators. Non-Western and lower quality studies reported larger effects compared to inactive, but not active, controls. While cautious interpretation is warranted due to unexplained heterogeneity, findings support EDTs as efficacious transdiagnostic interventions for emotional disorders, with sustained benefits over time. Future research should prioritize large-scale, methodologically rigorous trials that explore mechanisms of change, optimize treatment delivery and identify moderators of long-term outcomes.

Background: Alcohol-related issues are widespread worldwide and are fairly substantial. Numerous studies have identified and clarified the effects and prevalence of alcohol use across different contexts. However, when it comes to the prevalence of alcohol in psychiatry and its impact on treatment outcomes compared to other patient groups, studies are relatively scarce, and results often vary, sometimes with different outcomes. This study focuses on investigating the effectiveness of psychological treatment in psychiatric clinics for outpatients, considering those with and without hazardous alcohol use under naturalistic conditions.

Methods: Patients were recruited between 2012 and 2016 from psychiatric clinics in Sormland, Sweden, as part of the regular services. Patients completed symptom assessment instruments regarding depression, anxiety, quality-of-life, and alcohol consumption at the beginning of their psychological treatment, upon completion, and during a follow-up 1 year after completion. Completion of questionnaires was ongoing for some patients until 2021. A total of 324 patients were included in the study, distributed among 59 participating therapists.

Results: Among all patients in the study, 30.2% showed hazardous alcohol use at the start of their psychological treatment, with a higher proportion being men. There was a significant reduction in the proportion of patients with hazardous use and a notable decrease in the mean audit score upon completion of psychological treatment. At follow-up, there was no significant change compared to completion. There were 31.2% of the patients who achieved recovery or improvement in the audit score upon completion of treatment. Patients with hazardous alcohol use consistently scored higher mean values on the symptom assessment instruments and lower on the quality-of-life instrument at the beginning. More individuals with hazardous alcohol use typically achieved better results across all outcome instruments at both at completion and follow-up.

Conclusion: Patients with hazardous alcohol use demonstrate significant improvements in their alcohol consumption through standard psychological treatment in psychiatry, despite the treatment not specifically focusing on alcohol consumption. The progress/improvement appears to be largely maintained at follow-up. Moreover, patients with hazardous alcohol use tend to show greater progress across all outcome instruments. No significant gender differences were detected in this context.

This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS’s adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS’s ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.

We present the integration of a Non-Axiomatic Reasoning System (NARS) with mobile robots for planning and decision making. NARS enables robots to effectively handle uncertainty in real-time with complete sensor and actuator integration, thereby ensuring adaptability to evolving scenarios. We discuss essential parts of the logic, the architecture and working principles of NARS, and the integration of NARS as a ROS node. A case study is provided demonstrating the system's proficiency to carry out a garbage collection task in an open-air environment by operating a mobile robot with manipulator arm, and we demonstrate its ability to learn about the place-dependent accumulation of garbage items. Case study also reveals that our approach performs more effectively on the overall task than the Belief-Desire-Intention model we compared with.