Barry Dauphin

Psychoanalytic communities increasingly face a practical and ethical question: who may legitimately claim the title “psychoanalyst” in a landscape where training routes, institutional standards, and legal regulation vary widely. This commentary argues that psychoanalytic legitimacy should be grounded primarily in demonstrable competence and ongoing accountability, rather than in institutional residence alone. To clarify the debate, the paper distinguishes four analytically distinct registers of legitimacy—legal permission, professional credentialing, community recognition, and psychological legitimacy—which often overlap but can diverge in practice. It then defines competence as a multi‐domain capacity that includes sustained analytic process skill, reflective function, theoretical literacy, and ethical accountability, while noting that these domains may not align neatly within individuals or institutions. The paper examines the enduring strengths of institute‐based formation and the contemporary pressures it faces (including economic barriers and adult‐learning constraints), alongside the promises and vulnerabilities of self‐directed pathways (including informal hierarchy, charismatic authority, prestige markets, and “supervision shopping”). As a concrete exemplar of competency‐based recognition, the American Board and Academy of Psychoanalytic and Psychodynamic Psychology (ABAPPP) is presented as a portfolio‐plus‐expert‐review model that can accommodate plural training routes while preserving rigor—without being treated as universally transferable in its specific requirements. Finally, the paper proposes “portable” process standards (e.g., transparency, qualified supervision, peer review, honest self‐representation) paired with jurisdiction‐ and discipline‐specific mechanisms, with attention to DEI risks and safeguards in portfolio‐driven evaluations.

Eye movements (EMs) offer valuable insights into cognitive and perceptual processes, serving as potential biomarkers for disordered thinking. This study explores the relationship between EM indices and perception and thinking problems in the Rorschach Performance Assessment System (R-PAS). Sixty non-clinical participants underwent eye-tracking while completing the Rorschach test, focusing on variables from the Perception and Thinking Problems Domain (e.g., WSumCog, SevCog, FQo%). The results reveal that increased cognitive disturbances were associated with greater exploratory activity but reduced processing efficiency. Regression analyses highlighted the strong predictive role of cognitive variables (e.g., WSumCog) over perceptual ones (e.g., FQo%). Minimal overlap was observed between performance-based (R-PAS) and self-report measures (BSI), underscoring the need for multi-method approaches. The findings suggest that EM patterns could serve as biomarkers for early detection and intervention, offering a foundation for future research on psychotic-spectrum processes in clinical and non-clinical populations.

Affective computing presents significant challenges, particularly in the context of human-machine interactions. Traditional approaches often involve developing data-driven models that rely on physiological and image-based data to infer the emotional states of individuals during interactions with machines. However, these models frequently lack contextual relevance and perform poorly in complex scenarios involving conflicting cues. Additionally, they suffer from weak generalization due to the absence of structured methodologies that disentangle interconnected external factors. To address these limitations, we propose leveraging the Affective Quality of Representations (AFF) score, a standardized metric for assessing human affective responses across varied contexts. The AFF score is derived from expert ratings of participant narratives elicited by questions or visual cards, following established SCORS-G guidelines. The primary challenge of this approach is its reliance on trained psychology experts for scoring. To mitigate this, we developed the AffectLLM, a large language model designed to predict AFF scores efficiently and support the analysis of human-machine interactions. A strong contribution of AffectLLM is providing a structured metric that can guide the design of humanmachine interaction environments that promote more positive affect quality outcomes.

Eye movements (EMs) offer valuable insights into cognitive and perceptual processes, serving as potential biomarkers for disordered thinking. This study explores the relationship between EM indices and perception and thinking problems in the Rorschach Performance Assessment System (R-PAS). Sixty non-clinical participants underwent eye-tracking while completing the Rorschach test, focusing on variables from the Perception and Thinking Problems Domain (e.g., WSumCog, SevCog, FQo%). Results revealed that increased cognitive disturbances were associated with greater exploratory activity but reduced processing efficiency. Regression analyses highlighted the strong predictive role of cognitive variables (e.g., WSumCog) over perceptual ones (e.g., FQo%). Minimal overlap was observed between performance-based (R-PAS) and self-report measures (BSI), underscoring the need for multi-method approaches. Findings suggest that EM patterns could serve as biomarkers for early detection and intervention, offering a foundation for future research on psychotic-spectrum processes in clinical and non-clinical populations.

Introduction: Artificial intelligence (AI) has tremendous potential for use in psychology. Among the many applications that may benefit from development of AI applications is narrative-personality assessment. Use of these tools and research methods is notably time-consuming and resource intensive. AI has potential to address these issues in ways that would greatly reduce clinician and researcher burden. Nonetheless, it is unclear if current AI models are sufficiently sophisticated to perform the complex downstream tasks, such as narrative assessment.Methodology: The purpose of this study is to explore if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the Social Cognition and Object Relations scales – Global Rating Method (SCORS-G). Experts generated prompt inputs by engaging in a detailed review of SCORS-G training materials. Prompts were then improved using an systematic process in which experts worked with Llama-2-70b to refine prompts. The utility of the prompts was then tested on two AI-empowered chatbots, ChatGPT-4 (OpenAI, 2023) and CLAUDE-2-100k, that were not used in the prompt refinement process.Results: Results showed that the refined prompts allowed chatbots to reliably rate narratives at the global level, though accuracy varied across subscales. Averaging ratings from two chatbots notably improved reliability for the global score and all subscale scores. Experimentation indicated that expert-refined prompts outperformed basic prompts regarding interrater reliability and absolute agreement with gold standard ratings. Only the expert-refined prompts were able to generate acceptable single-rater interrater reliability estimates.Discussion: Findings suggest that AI could significantly reduce the time and resource burdens on clinicians and researchers using narrative rating systems like the SCORS-G. Limitations and implications for future research are discussed.

Considering the continuing interest in the use of eye-tracking technology for study of the Rorschach response process, the present study examines the internal consistencies for several eye-tracking indices during Rorschach administration. Many experimental psychologists have recently maintained that researchers should be interested in and report the internal consistency statistics of performance measures, including eye tracking in order to improve effect size, the power of hypothesis testing, and the replicability of findings. In the current study, eye-tracking variables more relevant to understanding top–down (goal-driven) processes showed good-to-excellent internal consistencies, while variables largely affected by bottom–up (stimulus-driven) processes showed questionable or poor internal consistency. The current findings provide support for recent research strategies of utilizing protocol-level eye-tracking averages to link to Rorschach coding variables. In addition, the current study found differences across cards for the eye-tracking variables, showing medium-to-large effect sizes, which provides evidence for the stimulus pull of the cards for visual search strategies, including variables not previously used in Rorschach research.

Psychologists have long been interested in the underlying visual perceptual processes associated with forming responses to certain psychological tests, including the Rorschach Ink Blot Test, which modern users conceptualize as a conceptual problem-solving task. Accordingly, we used eye tracking technology to assess the internal consistency of saccadic responses to both the Rorschach Ink Blot Test and a facial expression task. Internal consistency was highest for eye Fixation Duration (FD) and Saccade Amplitude (SA), and both FD and SA measures in the Rorschach were positively related to the same measures in the facial expression task. Given this high internal consistency of FD and SA for viewing Rorschach ink blots and viewing pictures from well-known collections of facial expressions, and given high correlations between these eye tracking measures across the two tasks, FD and SA may now be used in further studies of eye movements in visuo-attentive psychological/neuropsychological tests (e.g., the Thematic Apperception Test). Reliability of these eye movement measures across tasks enables their use for better understanding of underlying visual processes and improved interpretations of the meaning of behavioral responses to psychological/neuropsychological tests.