Action control theories assume that stimulus and response features are integrated or bound into short term episodic traces. A repetition of any of these features results in a retrieval of the entire episodic trace, and can thus facilitate or interfere with future actions. Along with stimuli features, features of the response and any other irrelevant stimuli that are present, are also integrated into such traces and can influence future actions. Using word stimuli, Singh et al. (2018) observed that such so-called binding effects are larger for attended features relative to unattended features. This was the case even for features generally believed to be automatically processed, like valence. Since previous research has shown differences in the processing of word and picture stimuli, it is questionable whether the attentional modulations in the above study would extend to picture stimuli. In order to examine this question, Experiment 1 replicated the design of Singh et al. (2018) but used picture instead of word stimuli. In order to directly compare word and picture stimuli, the data of Singh et al (2018) were re-analysed together with the data of the present study. In Experiment 2, the alternative hypothesis, that the effects were driven by the encoding of stimulus contingencies, was tested. Taken together, the findings of the present study replicate those of Singh et al. (2018), indicating that even with picture stimuli, valence related binding effects are modulated by attention allocation.

Figural matrices are widely used to measure reasoning ability. According to the two-process model of figural matrix reasoning, task performance relies on correspondence finding (linked to induction ability) and goal management (linked to working memory). Cognitive theory suggests that item characteristics (i.e., change rules and design principles of figural elements) are related to the two solution processes and impact item difficulties in a multiplicative, interactive manner. This study tested the multiplicative effect hypothesis by comparing two cognitive diagnostic models using additive and multiplicative effect estimations. A 26-item figural matrix test was administered to 633 high-ability individuals across paper-and-pencil and computer formats. The linear logistic test model (LLTM) and least square distance method (LSDM) were applied to Rasch and 2PL item parameters. Contrary to the multiplicative effect hypothesis, the additive LLTM model showed better item parameter reconstruction than the LSDM that includes multiplicative effects. These results suggest that change rules and design principles may independently contribute to the difficulty of figural matrices. Correspondence-finding demands may primarily arise from design principles, while change rules may primarily contribute to difficulty through goal management demands based on their number and complexity. The findings highlight the need to consider item components related to the phenomenological representation of figural elements when explaining solution processes of figural matrices. Implications for cognitive theory and item construction are discussed.

Awareness of social biases is crucial as they impact both individual behavior and societal outcomes. Whereas previous research indicates that self-awareness of ego-nonthreatening biases enhances self-regulation, the effects of self-awareness of ego-threatening biases remain underexplored. Preliminary findings suggest that awareness of ego-threatening biases related to rumination may lead to maladaptive states. However, these findings await replication with standardized instruments. To address this gap, we conducted two studies. In Study 1 (N = 1609), we developed and validated the 12-item Self-Awareness of Ego-Threatening Biases Questionnaire (SAETBQ). Consistent with our hypotheses, self-awareness of ego-threatening biases (as measured by the SAETBQ) correlated with higher moral disengagement, lower self-diagnostic motive, and lower integrative self-knowledge, indicating a tendency towards ego deterioration, whereas self-awareness of ego-nonthreatening biases (as measured by the Metacognitive Self questionnaire) showed the opposite pattern of correlations, indicating a tendency towards beneficial self-regulation. In Study 2 (N = 681), Dark Triad traits correlated positively and Light Triad traits negatively with self-awareness of ego-threatening biases. These results underscore the complex role of self-awareness in managing cognitive biases.

Background: Large language models (LLMs) are increasingly used in mental health, showing promise in assessing disorders. However, concerns exist regarding their accuracy, reliability, and fairness. Societal biases and underrepresentation of certain populations may impact LLMs. Because LLMs are already used for clinical practice, including decision support, it is important to investigate potential biases to ensure a responsible use of LLMs. Anorexia nervosa (AN) and bulimia nervosa (BN) show a lifetime prevalence of 1%-2%, affecting more women than men. Among men, homosexual men face a higher risk of eating disorders (EDs) than heterosexual men. However, men are underrepresented in ED research, and studies on gender, sexual orientation, and their impact on AN and BN prevalence, symptoms, and treatment outcomes remain limited. Objectives: We aimed to estimate the presence and size of bias related to gender and sexual orientation produced by a common LLM as well as a smaller LLM specifically trained for mental health analyses, exemplified in the context of ED symptomatology and health-related quality of life (HRQoL) of patients with AN or BN. Methods: We extracted 30 case vignettes (22 AN and 8 BN) from scientific papers. We adapted each vignette to create 4 versions, describing a female versus male patient living with their female versus male partner (2 × 2 design), yielding 120 vignettes. We then fed each vignette into ChatGPT-4 and to “MentaLLaMA” based on the Large Language Model Meta AI (LLaMA) architecture thrice with the instruction to evaluate them by providing responses to 2 psychometric instruments, the RAND-36 questionnaire assessing HRQoL and the eating disorder examination questionnaire. With the resulting LLM-generated scores, we calculated multilevel models with a random intercept for gender and sexual orientation (accounting for within-vignette variance), nested in vignettes (accounting for between-vignette variance). Results: In ChatGPT-4, the multilevel model with 360 observations indicated a significant association with gender for the RAND-36 mental composite summary (conditional means: 12.8 for male and 15.1 for female cases; 95% CI of the effect –6.15 to -0.35; P=.04) but neither with sexual orientation (P=.71) nor with an interaction effect (P=.37). We found no indications for main effects of gender (conditional means: 5.65 for male and 5.61 for female cases; 95% CI –0.10 to 0.14; P=.88), sexual orientation (conditional means: 5.63 for heterosexual and 5.62 for homosexual cases; 95% CI –0.14 to 0.09; P=.67), or for an interaction effect (P=.61, 95% CI –0.11 to 0.19) for the eating disorder examination questionnaire overall score (conditional means 5.59-5.65 95% CIs 5.45 to 5.7). MentaLLaMA did not yield reliable results. Conclusions: LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations.

Background: As digital mental health delivery becomes increasingly prominent, a solid evidence base regarding its efficacy is needed. Objective: This study aims to synthesize evidence on the comparative efficacy of systemic psychotherapy interventions provided via digital versus face-to-face delivery modalities. Methods: We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for searching PubMed, Embase, Cochrane CENTRAL, CINAHL, PsycINFO, and PSYNDEX and conducting a systematic review and meta-analysis. We included randomized controlled trials comparing mental, behavioral, and somatic outcomes of systemic psychotherapy interventions using self- and therapist-guided digital versus face-to-face delivery modalities. The risk of bias was assessed with the revised Cochrane Risk of Bias tool for randomized trials. Where appropriate, we calculated standardized mean differences and risk ratios. We calculated separate mean differences for nonaggregated analysis. Results: We screened 3633 references and included 12 articles reporting on 4 trials (N=754). Participants were youths with poor diabetic control, traumatic brain injuries, increased risk behavior likelihood, and parents of youths with anorexia nervosa. A total of 56 outcomes were identified. Two trials provided digital intervention delivery via videoconferencing: one via an interactive graphic interface and one via a web-based program. In total, 23% (14/60) of risk of bias judgments were high risk, 42% (25/60) were some concerns, and 35% (21/60) were low risk. Due to heterogeneity in the data, meta-analysis was deemed inappropriate for 96% (54/56) of outcomes, which were interpreted qualitatively instead. Nonaggregated analyses of mean differences and CIs between delivery modalities yielded mixed results, with superiority of the digital delivery modality for 18% (10/56) of outcomes, superiority of the face-to-face delivery modality for 5% (3/56) of outcomes, equivalence between delivery modalities for 2% (1/56) of outcomes, and neither superiority of one modality nor equivalence between modalities for 75% (42/56) of outcomes. Consequently, for most outcome measures, no indication of superiority or equivalence regarding the relative efficacy of either delivery modality can be made at this stage. We further meta-analytically compared digital versus face-to-face delivery modalities for attrition (risk ratio 1.03, 95% CI 0.52-2.03; P=.93) and number of sessions attended (standardized mean difference –0.11; 95% CI –1.13 to –0.91; P=.83), finding no significant differences between modalities, while CIs falling outside the range of the minimal important difference indicate that equivalence cannot be determined at this stage. Conclusions: Evidence on digital and face-to-face modalities for systemic psychotherapy interventions is largely heterogeneous, limiting conclusions regarding the differential efficacy of digital and face-to-face delivery. Nonaggregated and meta-analytic analyses did not indicate the superiority of either delivery condition. More research is needed to conclude if digital and face-to-face delivery modalities are generally equivalent or if—and in which contexts—one modality is superior to another.