Amir Tal

UNSTRUCTURED Generative artificial intelligence (GenAI) is rapidly entering psychotherapy, from clinician training and decision support to patient-facing tools and direct involvement in therapeutic sessions. Yet much of the discussion remains focused on what these systems can do, rather than on where and for what purpose their capabilities can be translated into meaningful clinical use. This reflects the broader capability-to-utility gap between demonstrated technological performance and clearly defined value in care. We propose the SMART framework to organize GenAI applications across five levels of increasing integration into psychotherapy: Skill building, where GenAI supports clinical training without patient contact; Mentoring, where it supports clinician judgment around real cases; Assisting, where it interacts directly with patients between sessions as an adjunct to therapist-led care; Reflecting, where it observes and analyzes the therapeutic session without intervening; and Transforming, where it actively participates within the session under clinician leadership. Across these levels, increasing proximity to the patient and deeper integration into the therapeutic encounter are accompanied by changing clinical, ethical, evidentiary, and oversight requirements. By linking GenAI applications to their specific place and purpose in care, SMART provides a shared language for clinicians, researchers, developers, and policymakers. It offers a basis for more proportionate evaluation and regulation and for a research agenda focused on when GenAI capabilities translate into safe, clinically useful, and accountable psychotherapy.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. Here, we provide ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.

Newly formed memories are not passively stored for future retrieval; rather, they are reactivated offline and thereby strengthened and transformed. However, reactivation is not a uniform process: it occurs throughout different states of consciousness, including conscious rehearsal during wakefulness and unconscious processing during both wakefulness and sleep. In this study, we explore the consequences of reactivation during conscious and unconscious awake states. Forty-one participants learned associations consisting of adjective-object-position triads. Objects were clustered into distinct semantic groups (e.g., multiple fruits, vehicles, musical instruments) which allowed us to examine the consequences of reactivation on semantically-related memories. After an extensive learning phase, some triads were reactivated consciously, through cued retrieval, or unconsciously, through subliminal priming. In both conditions, the adjective was used as the cue. Reactivation impacted memory for the most distal association (i.e., the spatial position of associated objects) in a consciousness-dependent and memory-strength-dependent manner. First, conscious reactivation of a triad resulted in a weakening of other semantically related memories, but only those that were initially more accurate (i.e., memories with lower pre-reactivation spatial errors). This is similar to what has been previously demonstrated in studies employing retrieval-induced forgetting designs. Unconscious reactivation, on the other hand, benefited memory selectively for weak cued items. Semantically linked associations were not impaired, but rather integrated with the reactivated memory. Taken together, our results demonstrate that conscious and unconscious reactivation of memories during wakefulness have qualitatively different consequences on memory for distal associations. Effects are memory-strength-dependent, as has been shown for reactivation during sleep. Results support a consciousness-dependent inhibition account, according to which unconscious reactivation involves less inhibitory dynamics than conscious reactivation, thus allowing more liberal spread of activation. Our findings set the stage for additional exploration into the role of consciousness in memory structuring.

Sequence learning is the cognitive faculty enabling everyday skill acquisition. In the lab, it is typically measured in speed of response to sequential stimuli, whereby faster responses are taken to indicate improved anticipation. However, response speed is an indirect measure of anticipation, that can provide only limited information on underlying processes. As a result, little is known about what is learned during sequence learning, and how that unfolds over time. In this work, eye movements that occurred before targets appeared on screen in an ocular serial reaction time (O-SRT) task provided an online indication of where participants anticipated upcoming targets. When analyzed in the context of the stimuli preceding them, oculomotor anticipations revealed several simultaneous learning processes. These processes influenced each other, as learning the task grammar facilitated acquisition of the target sequence. However, they were dissociable, as the grammar was similarly learned whether a repeating sequence inhabited the task or not. Individual differences were found in how the different learning processes progressed, allowing for similar performance to be produced for different latent reasons. This study provides new insights into the processes subserving sequence learning, and a new method for high-resolution study of it.