Introduction: Intensive Short-Term Dynamic Psychotherapy has shown promising effects for treatment-resistant depression, but it remains unclear whether its proposed mechanisms --- reducing emotional repression, negative affect, and psychological distress --- actually mediate treatment outcomes. Methods: We reanalyzed publicly available data from a randomized controlled trial N = 86) comparing 20 sessions of Intensive Short-Term Dynamic Psychotherapy to waitlist control for treatment-resistant depression. Depression and process measures were assessed at baseline, post-treatment, and 3-month follow-up. Linear mixed-effects models analyzed trajectories; bootstrap mediation and cross-lagged panel analyses examined mechanisms. Results: Treatment produced large effects on depression at post-treatment (Cohen's d = 1.68) that continued to increase through 3-month follow-up (d = 2.50, 95% CI [1.88, 3.11]). All proposed process measures also showed very large effects (d = 1.96--2.95). However, neither emotional repression nor negative affect significantly mediated depression improvement. Distress showed apparent mediation, but a sensitivity analysis removing the overlapping depression subscale eliminated this effect entirely, confirming it reflected construct overlap rather than a genuine mechanism. Cross-lagged analyses revealed no temporal precedence for any process measure, indicating concurrent rather than sequential change. Discussion: These findings confirm that this psychotherapy produces large, durable effects on treatment-resistant depression. However, the theorized sequential mechanisms --- whereby reducing defensive functioning leads to improved affect regulation, which in turn alleviates depression --- were not supported. Instead, the treatment appears to produce broad, simultaneous therapeutic change across multiple psychological domains. Understanding how psychotherapy works may require finer temporal measurement and observational methods that capture in-session processes.

Machine Psychology is an emerging interdisciplinary framework that integrates principles from learning psychology with a cognitive AI architecture to advance Artificial General Intelligence (AGI) research. This article provides a focused review of Machine Psychology, tracing the progression from basic operant learning to advanced symbolic reasoning within the Non-Axiomatic Reasoning System (NARS). We first outline the theoretical foundations in operant conditioning and Relational Frame Theory, highlighting how adaptive behavior and arbitrarily applicable relational responding (AARR) serve as cornerstones of human cognition. We then describe the architecture and capabilities of NARS and its variant OpenNARS for Applications (ONA), which enable real-time sensorimotor reasoning under conditions of uncertain knowledge. Four successive experimental studies are reviewed in detail: (1) Operant conditioning tasks demonstrate that NARS can learn from reinforcing consequences to modify its behavior, achieving 100% correct responses and adapting when contingencies change. (2) In generalized identity matching, NARS abstracts an identity relation that successfully generalizes to novel stimuli after minimal training. (3) A functional equivalence study shows NARS grouping stimuli by shared consequences, such that new learning transfers spontaneously between equivalent stimuli. (4) Finally, NARS is extended to model AARR, exhibiting derived symmetric and transitive relations and context-sensitive relational reasoning (e.g. same-opposite relations) with associated transformations of stimulus functions. We discuss how specific NARS mechanisms (e.g. temporal inference, variable term introduction, relational implication) map onto psychological processes underlying learning and cognition. Machine Psychology is presented as a developmental roadmap toward human-like AGI, incrementally building cognitive skills from basic adaptation to complex symbolic reasoning. We critically evaluate the strengths and limitations of this approach and outline open research directions toward achieving flexible, theory-of-mind-capable intelligence.

Internet-based psychological interventions can effectively reduce depressive symptoms in adults, but adherence remains challenging. In this preregistered individual participant data meta-analysis, we examined predictors of treatment adherence. We searched PubMed, Embase and PsycINFO on 6 February 2024 for randomized trials of internet-based interventions among adults with elevated depressive symptoms. We conducted a one-stage logit-link multilevel beta regression, with adherence defined as the proportion of completed modules post-intervention. This study included 71 trials (85 treatment arms, 8,082 participants). Lower adherence was associated with younger age (β = 0.005, standard error (SE) 0.002, P = 0.028), male gender (β = −0.163, SE 0.053, P = 0.002), lower education (β = −0.133, SE 0.05, P = 0.008) and employment (β = −0.113, SE 0.054, P = 0.037). No significant interactions were found between individual predictors and intervention format (guided versus self-guided). Higher adherence was associated with lower post-intervention depression severity, adjusting for baseline severity (β = −0.30, SE 0.04, P < 0.001). Identifying subgroups at risk of low adherence may inform targeted strategies to improve engagement and clinical effectiveness. Using data from 71 randomized controlled trials that involve 8,082 participants, the authors of this individual participant data meta-analysis examine individual- and study-level predictors of adherence to internet-based interventions for depression.

OBJECTIVE:Difficulties in emotional processing are implicated in the development and maintenance of Somatic Symptom Disorder (SSD) and may represent a target for therapeutic change. This study examined whether session-level emotional processing, operationalized as "rise in complex feelings", predicts subsequent reductions in somatic symptoms during online Intensive Short-Term Dynamic Psychotherapy (ISTDP) for treatment-resistant SSD. METHODS:Twenty-five participants with moderate to severe SSD and non-response across two prior intervention phases received up to 16 sessions of online ISTDP (M = 14.1) delivered by 17 therapists. After each session, therapists rated rise in complex feelings (0-6), reflecting emotional processing within the therapeutic relationship. Somatic symptom severity was assessed weekly using the Patient Health Questionnaire-15 (PHQ-15). Lagged multilevel models tested whether rise predicted the subsequent PHQ-15 assessment, with person-mean centering isolating within-person effects. A one-sided test evaluated the a priori directional hypothesis. RESULTS:Higher-than-usual rise in complex feelings was associated with lower somatic symptom severity at the subsequent assessment (b = -0.22, 95% CI [-0.43, -0.02], one-sided p = .016, two-sided p = .032). The association was robust across sensitivity analyses, including reverse temporal ordering. Adjustment for patient-rated emotional activation and therapeutic alliance only modestly attenuated the estimate. The association was observed within-person; between-person differences in average rise were unrelated to symptom outcomes (b = -0.03, p = .809). CONCLUSIONS:Session-level emotional processing was associated with subsequent reductions in somatic symptoms in online ISTDP, providing preliminary evidence that emotional processing may represent a session-level marker of symptom change in SSD.

Introduction:Adolescent depression poses a major public health concern with substantial clinical and societal implications. Both internet-delivered cognitive behavioural therapy (ICBT) and internet-delivered psychodynamic therapy (IPDT) have shown efficacy, but questions remain regarding long-term efficacy and cost-effectiveness. The present study presents a 12-month follow-up and cost-comparison from a randomized controlled trial (RCT) comparing ICBT and IPDT for adolescent depression. Methods:Participants were 272 adolescents aged 15-19 with a primary diagnosis of major depressive disorder. The primary outcome was depressive symptoms measured with the QIDS-A17-SR while the secondary outcome was anxiety symptoms measured with the GAD-7. Costs were assessed both by comparing costs of treatment and healthcare use 12-month post-treatment using the TIC-P. Results:Results were stable at the 12-month follow up compared to treatment endpoint, for both depressive and anxiety symptoms. There were no significant group differences at the 12-month follow-up. There were no differences in treatment costs or in costs for healthcare use one-year post-treatment. Discussion:This study suggests that treatment gains from IPDT and ICBT for adolescent depression remain stable during a 12-month follow-up period, with no differences between the treatments one-year post-treatment. Furthermore, it suggests comparable costs for the treatments. Interpretation of health-care use data was restricted due to the COVID-19 pandemic taking place during the follow-up period. This adds to the literature suggesting that ICBT and IPDT can be seen as viable alternatives for treating adolescent depression. More research into the long-term effects and cost-effectiveness is needed.

Over thirty years ago, Malan (1995) argued that dynamic psychotherapy and behavior therapy are each incomplete without the other. A previous paper took a first step toward this integration, establishing that Intensive Short-Term Dynamic Psychotherapy (ISTDP) procedurally fulfills the criteria of Functional Analytic Psychotherapy (FAP) at the level of operant conditioning. However, that analysis identified phenomena—particularly the Unconscious Therapeutic Alliance (UTA), unlocking of the unconscious, and the persistence of entrenched defenses—that challenge purely operant explanations. The present paper extends the analysis one level deeper, arguing that ISTDP’s characteristic processes can be understood through Törneke et al.’s Relational Frame Theory (RFT)-based model of psychological (in)flexibility. Using the same analytical strategy—identifying a generalized functional analytic system and showing ISTDP fits it—the paper argues that ISTDP’s intervention spectrum constitutes progressively intensifying hierarchical framing; that defenses represent identity fusion through coordination framing; that transference is coordination framing generalized across deictic contexts; that the UTA spectrum reflects graduated hierarchical framing; that affect integration involves the collision and transformation of relational networks, producing durable selfing transformation; and that the therapeutic relationship functions as a privileged deictic arena requiring Cfunc-level activation for deep change. The integration is bidirectional: ISTDP gains theoretical precision and testable hypotheses, while RFT-based clinical frameworks gain access to ISTDP’s systematic techniques and its specification of the conditions under which deep change occurs. The analysis points toward a third paper addressing phenomena beyond current RFT’s explanatory reach.

Background:Internet-delivered psychodynamic therapy (IPDT) has been found to be effective for adolescents with depression in previous randomized controlled trials. The present study aimed to evaluate an adaptive, feedback-informed version of IPDT, designed to improve outcomes for participants identified early as at risk of non-response. Methods:A randomized controlled trial targeting adolescents aged 15-19 years with mild to moderate major depressive disorder. Participants were recruited through social media, national and local advertising, schools, and user organizations. After three weeks of standard IPDT, participants classified as at risk by a prediction algorithm were randomized to either adapted or standard treatment. The planned sample size was 240 participants. Despite extensive nationwide recruitment efforts during 2024, only 35 participants were enrolled before the study was discontinued. Results:Recruitment difficulties were primarily due to recent European Union regulations prohibiting profiling-based online advertising for minors, which eliminated access to previously effective social media recruitment channels. Participants who completed treatment showed significant pre- to post-treatment improvements in depressive symptoms (d = 1.08), anxiety (d = 0.74), and emotion regulation (d = 0.79). The predictive algorithm showed promising results in classifying patients as responders or non-responders. Conclusions:Although the trial was underpowered, the findings provide promising within-group effects and valuable lessons for future digital mental health research involving minors. New recruitment infrastructures that comply with data protection laws are needed to ensure feasibility of online psychotherapy trials. Continued development of adaptive, feedback-informed IPDT for adolescents with depression is needed. Trial registration:ClinicalTrials.gov Identifier: NCT06193772.

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.

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.

In this paper, I describe and analyze Intensive Short-Term Dynamic Psychotherapy (ISTDP) from the perspective of Functional Analytic Psychotherapy (FAP), a behavioral psychotherapy explicitly emphasizing the therapeutic relationship as the primary context for clinical intervention. I propose that FAP can be understood as a generalized system of behavioral psychotherapy, characterized by procedural guidelines (“FAP’s Five Rules”) derived from operant conditioning principles. Further, I demonstrate that ISTDP, despite its psychoanalytic origins, procedurally fulfills the criteria of this generalized behavioral system, particularly through its structured use of contingent responding (Pressure, Challenge, Head-On Collision) within therapeutic interactions. ISTDP’s implicit universal conceptualization of emotional health — emphasizing adaptive emotional experiencing, expression, and interpersonal closeness — is explicitly articulated within the behavioral framework. Highlighting complex clinical phenomena of ISTDP, such as emotional breakthroughs (“unlockings”) and the expressions of the Unconscious Therapeutic Alliance (UTA), I argue that these phenomena challenge purely operant behavioral explanations and thus suggest the potential benefit of integrating advanced behavioral theoretical frameworks like Relational Frame Theory. The paper discusses implications for theoretical advancement, clinical practice refinement, and improved empirical research in ISTDP.

ObjectiveWe 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.Method12-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.ResultsThe 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.ConclusionI-EAET seems to have positive long-term effects for somatic symptom disorder. Larger studies with controls and comparisons to other treatments are needed.

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.

Although case studies support the notion of three anxiety pathways in Intensive Short-Term Dynamic Psychotherapy (ISTDP), empirical research remains scarce, highlighting the need to investigate how somatic symptoms cluster in line with ISTDP’s anxiety pathway theory using validated measures. This study therefore explored the clustering of self-reported somatic symptoms in 550 patients with persistent physical symptoms (PPS) from three previous randomized controlled trials, examining their potential alignment with the theory of unconscious anxiety and its discharge pathways, as proposed in ISTDP. Using the Patient Health Questionnaire-15 (PHQ-15), an exploratory factor analysis identified three symptom clusters—musculoskeletal, gastrointestinal, and cardiopulmonary—that together explained 40.1% of the variance. This three-factor structure, validated through confirmatory factor analysis, partially aligned with ISTDP’s conceptual anxiety pathways, though limitations were noted in capturing cognitive-perceptual disturbances. These findings suggest that self-reported symptom assessment can complement clinician-led methods in identifying anxiety-related symptom clusters, warranting further development of self-report tools within psychodynamic assessment frameworks.

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.Registration number osf.io/u46t7

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 overviewof 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.

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.

Intensive Short-Term Dynamic Psychotherapy (ISTDP) has an increasing amount of evidence regarding its efficacy across various psychiatric conditions and specifically with depression. The aim of this study is to replicate the findings of controlled research by examining the effects of ISTDP in the treatment of depression in a large naturalistic sample, and also to explore the mediating role of unlocking the unconscious in this treatment. Healthcare costs were also explored. Data were collected from a naturalistic study conducted at the Centre for Emotions and Health, Halifax, Nova Scotia, Canada, between 1999 and 2007. A sample of 195 patients' self-reported levels of depression, measured by the depression subscale of the Brief Symptom Inventory (BSI), and interpersonal problems, measured by the Inventory of Interpersonal Problems-32 (IIP-32), were analyzed using mixed-effects models. The analysis revealed a significant and large effect of ISTDP on both depression (within-group Cohen's d = 1.02, 95% CI [0.75, 1.26]) and interpersonal problems (within-group Cohen's d = 1.17, 95% CI [0.89, 1.46]). The process of unlocking the unconscious emerged as a significant mediator of treatment outcomes for both depression (between-group Cohen’s d = 0.60, 95% CI [0.16, 1.07]) and interpersonal problems (between-group Cohen’s d = 0.47, 95% CI [-0.05, 0.95]). Reductions in costs regarding physician (p = 0.07) and hospital costs (p < 0.05) were observed. These findings support the efficacy of ISTDP in treating depression and highlight the importance of unlocking the unconscious among patients with depression.

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.

Most research showing results of psychotherapy come from efficacy studies or effectiveness studies from university counselling centers, or therapy clinics at universities. This study is an effectiveness study that aims to investigate the results of psychological treatment in psychiatric clinics for outpatients under naturalistic conditions. The study contributes unique insights regarding the outcomes of psychological treatment for patients with severe psychiatric problems in the complex real environment where many influencing variables exist. Patients were recruited from 2012 to 2016 from psychiatric clinics in Sormland, Sweden in the regular service. They received psychological treatment lasting between 1 and 50 months. The entire period of assessment took place between 2012 and 2021. A total of 325 patients received treatment from 59 participating therapists. Patients completed symptom assessment instruments regarding anxiety, depression, and quality of life at the start of therapy, upon the completion of therapy and, at follow-up one year after completion. Analyses indicated a significant improvement in all outcome instruments between start and completion of therapy. The improvement was largely maintained until follow-up. The effect sizes were moderate. Between 49.1% and 62.9% of patients "improved" or "recovered" as measured by the symptom assessment instruments at completion of therapy. The proportion of improved/recovered on the quality-of-life instrument was 37.4%. In a naturalistic cohort with comparatively severe psychiatric problems, substantial and stable improvements were achieved. The outcomes were respectable considering the population. The study provides external validity to efficacy studies on how psychological treatment works in a real-life context.

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.