Heterogeneity in strategy use during arbitration between experiential and observational learning.

To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people's behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.

Virtual agents among participants with methamphetamine use disorders: Acceptability and usability study.

INTRODUCTION: While the potential future role of virtual agents (VAs) in treating addiction is promising, participants' attitudes toward the use of VAs in psychotherapy remain insufficiently investigated. This lack of investigation could pose barriers to the adoption of VA-led psychotherapy for people with substance use disorders (SUD). This research aims to explore the acceptability and usability of VAs for people with methamphetamine use disorder. METHODS: Following a single session of psychotherapy led by VAs through the Echo-app, a group of 49 individuals actively seeking treatment for current DSM-V substance dependence (with a mean age of 39.06 ± 8.02) completed self-administered questionnaires and participated in focus group interviews. These questionnaires aimed to investigate participants' preference regarding the type of psychotherapy and their willingness to engage in VA-led psychotherapy, taking into account their diverse psychological needs. RESULTS: Quantitative data were subjected to analysis through both descriptive and inferential statistical methods. Interestingly, participants exhibited a significantly higher acceptability for traditional face-to-face psychotherapy compared to email-based psychotherapy (p = 0.042), but there was no statistically significant difference between their acceptance of traditional psychotherapy and VA-led psychotherapy (p = 0.059). The questionnaire outcomes indicated participants' willingness to engage in VA-led psychotherapy for purposes such as relapse prevention intervention, addressing emotional issues, managing somatic experiences, and facilitating social and family functional recovery. Furthermore, the participants' attitudes toward VA-led psychotherapy were predicted by factors including the need for anxiety-focused psychotherapy (p = 0.027; OR [95%CI] = 0.14[0.03,0.80]), the presence of chronic somatic diseases (p = 0.017; OR [95%CI] = 13.58[1.59,116.03]), and marital status (p = 0.031; OR [95%CI] = 5.02[1.16,21.79]). DISCUSSION: Through the interviews, the study uncovered the factors that either supported or hindered participants' experiences with VA-led psychotherapy, while also gathering suggestions for future improvements. This research highlights the willingness and practicality of individuals with SUD in embracing VA-led psychotherapy. The findings are anticipated to contribute to the refinement of VA-led tools to better align with the preferences and needs of the users.

Smartphone-based gaze estimation for in-home autism research.

Atypical gaze patterns are a promising biomarker of autism spectrum disorder. To measure gaze accurately, however, it typically requires highly controlled studies in the laboratory using specialized equipment that is often expensive, thereby limiting the scalability of these approaches. Here we test whether a recently developed smartphone-based gaze estimation method could overcome such limitations and take advantage of the ubiquity of smartphones. As a proof-of-principle, we measured gaze while a small sample of well-assessed autistic participants and controls watched videos on a smartphone, both in the laboratory (with lab personnel) and in remote home settings (alone). We demonstrate that gaze data can be efficiently collected, in-home and longitudinally by participants themselves, with sufficiently high accuracy (gaze estimation error below 1° visual angle on average) for quantitative, feature-based analysis. Using this approach, we show that autistic individuals have reduced gaze time on human faces and longer gaze time on non-social features in the background, thereby reproducing established findings in autism using just smartphones and no additional hardware. Our approach provides a foundation for scaling future research with larger and more representative participant groups at vastly reduced cost, also enabling better inclusion of underserved communities.

The preliminary efficacy of virtual agent-assisted intelligent rehabilitation treatment (Echo app v2.0) in patients with alcohol use disorders: Study protocol for a randomized controlled trial.

Background: Alcohol use disorder (AUD) is one of the most common substance use disorders. People with AUD are in great need of highly accessible and comprehensive management, involving medicine, exercise, and psychotherapy. However, due to limited resources, providing comprehensive treatment for every patient is challenging. Virtual agent-assisted intelligent rehabilitation treatment can improve the accessibility of comprehensive management. Objectives: This randomized controlled trial aims to test whether the virtual agent-assisted intelligent rehabilitation treatment (Echo-app-v2.0) plus treatment as usual (TAU) has greater efficacy than only TAU in AUD. Methods: One hundred participants with AUD will be recruited and randomly assigned to either TAU or virtual agent-assisted intelligent rehabilitation treatment plus TAU based on the 1:1 ratio. The virtual agent-assisted intelligent rehabilitation treatment is delivered by the Echo app v2.0 developed by our research team. Participants will be assessed at baseline, week 4 of treatment, one month, and three months after the end of treatment. The primary outcome is the participants' craving for alcohol, measured by Visual Analogue Scale. Other outcomes include the use of alcohol, motivation for treatment, coping style, depression, anxiety, impulsivity, stress, and sleep quality. Discussion: The virtual agent, with vivid human image, high privacy, and the ability to interact with users, has the potential to play an important role in the delivery of digital psychotherapy. The development of Echo app v2.0 has the possibility to raise the availability of comprehensive treatment for people with AUD.