Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Ezzyat, Y., et al. (2023).

Cerebral Cortex, 34(1). 

https://doi.org/10.1093/cercor/bhad427

Abstract

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation’s behavioral and physiological effects depend on the stimulation target’s anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain’s memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation’s behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Here are some thoughts:

This article is important to psychologists for several reasons. It moves beyond simply correlating brain activity with mental states by demonstrating a causal pathway, showing that targeted self-regulation of a specific brain area directly alters an otherwise automatic cognitive process like mind-wandering. This challenges purely psychological or environmental explanations for attentional failures and firmly grounds them in modifiable neural processes. For clinical psychology, the significance is profound; many disorders, from ADHD to depression and anxiety, involve dysregulation of the default mode network and intrusive, off-task thoughts. This neurofeedback protocol offers a proof-of-concept for a non-pharmacological intervention that targets a core neural mechanism of these symptoms rather than just their surface manifestations. It also enriches cognitive theory by providing a mechanistic account of how the brain's large-scale networks compete during attention. The finding that individuals can learn to apply an implicit cognitive strategy to control their brain activity, which then changes their conscious experience, opens new avenues for understanding volitional control and developing treatments that blend cognitive training with real-time neural monitoring.

Intolerance of uncertainty causally affects indecisiveness

Appel, H., & Gerlach, A. L. (2025).

British Journal of Clinical Psychology,

64(3), 806–816.

https://doi.org/10.1111/bjc.12534

Abstract

Objectives

Intolerance of uncertainty (IU) is characterized by a pervasive negative reaction to uncertainty. It is a transdiagnostic risk factor for various mental disorders. Since decisions often need to be made in the face of uncertainty, IU is associated with indecisiveness, a dispositional difficulty in making decisions. Indecisiveness is also linked to a range of mental disorders. While IU is seen as a causal factor in indecisiveness, experimental studies on this assumption are lacking.

Methods

In this pre-registered, adequately powered study (N = 301), IU was experimentally increased or decreased compared to a control group, and the effect on indecisiveness was observed. Indecisiveness was assessed in a situational context, focusing on two decisions that were personally relevant to participants.

Results

The manipulation successfully affected IU. As predicted, increased IU led to more indecisiveness across both decisions compared to decreased IU. Exploratory analyses found that situational IU mediated the effect of the experimental manipulation on indecisiveness.

Conclusions

The results are the first to demonstrate a causal effect of IU on indecisiveness, thus contributing to the explanation of indecisiveness and the role that uncertainty management plays in it. Moreover, they have implications for treating various mental disorders by highlighting the role of IU in the transdiagnostic phenomenon of indecisiveness.

Practitioner points

• This is the first study to show that intolerance of uncertainty—a pervasive negative reaction to uncertainty—has a causal effect on chronic decision-making difficulties (i.e., indecisiveness).
• Both traits are associated transdiagnostically with symptoms of various mental disorders and are therefore therapeutically relevant.
• For patients presenting with indecisiveness, targeting intolerance of uncertainty may be an important component contributing to improvement.

The Illusion of Competence: How AI Tools Can Mask the Erosion of Clinical Judgment

Gavazzi, J. (2026, July).

Psychotherapy Bulletin, 61(4).

Clinical Impact Statement:

Psychologists who integrate AI tools without deliberate attention to their clinical consequences risk producing an illusion of competence: the capacity to generate sophisticated clinical language without the depth of reasoning that the language is meant to reflect. Maintaining the sequencing of independent judgment before AI consultation, treating AI outputs as objects of critical analysis, and preserving documentation as a reflective practice are essential safeguards for the integrity of quality psychological care.

The article is here.

Here is a snippet:

Practical Recommendations

None of this argues against using AI in psychological practice. LLMs offer genuine value as consultation resources, prompts for critical analysis, and tools for broadening the range of hypotheses a clinician considers. The argument is about sequencing and stance. Several principles follow.

1. AI-generated formulations should follow rather than precede independent clinical reasoning. The psychologist who develops her own differential formulation and then consults an LLM to examine what she may have missed is doing something different from the psychologist who queries the LLM first. The first sequence sharpens clinical thinking. The second quietly replaces it. This is a choice worth making consciously rather than letting convenience decide.
2. AI-generated outputs should be treated as objects of critical analysis, not as drafts to be refined. Before accepting an LLM’s formulation, ask what it assumed, what it excluded, and how it compares to your own reasoning. This turns an AI interaction into a reflective exercise rather than a shortcut.

General-purpose large language models outperform specialized clinical AI tools on medical benchmarks

Vishwanath, K., et al. (2026).

Nature Medicine.

https://doi.org/10.1038/s41591-026-04431-5

Abstract

Specialized clinical artificial intelligence (AI) tools are entering medical practice despite scarce independent evaluation. We quantitatively evaluate two clinical AI tools, OpenEvidence and UpToDate Expert AI, built on large language models (LLMs) against three frontier LLMs: GPT-5.2, Gemini 3.1 Pro and Claude Opus 4.6. Our evaluation has three stages: (1) 500 MedQA questions testing medical knowledge, (2) 500 HealthBench items measuring alignment with clinicians and (3) the real clinical queries (RCQ) benchmark, built from 100 de-identified queries from physicians to a general-purpose language model in a live clinical environment. For the RCQ benchmark, 12 US clinicians performed randomized, blinded review of model outputs, producing 1,800 model–question annotations. Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ. These findings highlight the need for independent, real-world evaluation of AI tools before they enter clinical settings.

Here are some thoughts:

This 2026 Nature Medicine study asked a simple question: are the special AI tools being sold to doctors actually better than the regular AI chatbots anyone can use? The researchers tested two clinical tools (OpenEvidence and UpToDate Expert AI) against three general-purpose models (GPT-5.2, Gemini, and Claude) on medical exam questions, expert-alignment tests, and real questions that doctors had asked during patient care, with twelve doctors blindly scoring the answers.

The answer was clear: the general-purpose chatbots beat the specialized medical tools on every test. In fact, the medical tools did no better than the free AI summary that shows up at the top of a Google search. The specialized tools mostly struggled with being clear and complete rather than getting facts wrong, and none of the tools were notably more dangerous than the others.

The takeaway is that paying for a fancy, doctor-branded AI tool may not get you better results than a regular chatbot, which matters a lot given that one of these companies was recently valued at billions of dollars. A few caveats: the study was small, couldn't measure speed or quality of sources, and one author consults for Google, whose model won. The authors think the real future may be hospitals building their own AI on their own data, rather than buying these off-the-shelf medical tools.

Magnifica Humanitas: Human Dignity, Artificial Intelligence, and the Essence of Psychological Practice

Gavazzi, J. (2026).

www.ethicalpsychology.com

Clinical Impact Statement:

This article offers psychologists a framework, grounded in Pope Leo XIV's recent encyclical and psychotherapy research, for the responsible clinical use of artificial intelligence. It presents three criteria for evaluating any AI application: its effect on the therapeutic alliance, its preservation of clinician accountability, and its respect for the patient's narrative integrity. The article addresses risks including automation bias, deskilling, culturally biased outputs, and privacy threats, while identifying appropriate uses in documentation, training, and supervision. Clinicians are encouraged to engage AI critically and with cultural humility, ensuring technology augments rather than replaces clinical judgment and relational attunement.

The article is here.

Language models align with brain regions that represent concepts across modalities

Ryskina, M., et al. (2025, August 15).

arXiv.org.

https://arxiv.org/abs/2508.11536

Abstract

Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consistently a brain region responds to the same concept across paradigms (sentence, word cloud, image) using an fMRI dataset (Pereira et al., 2018). Our experiments show that both language-only and language-vision models predict the signal better in more meaning-consistent areas of the brain, even when these areas are not strongly sensitive to language processing, suggesting that LMs might internally represent cross-modal conceptual meaning.

Here are some thoughts:

The researchers identified brain regions that respond to a concept's meaning regardless of whether it's shown as text, related words, or a picture, and found that AI language models best predict activity in exactly those meaning-focused regions, even in a vision-related area with no link to language, hinting that these models grasp meaning beyond just words. Notably, bigger models and instruction-tuned ones were no better, which runs against some earlier expectations. The honest takeaway: it's a suggestive hint rather than proof, since it rests on correlations and a single dataset of 17 people, but it points to language models picking up a kind of meaning closer to how the brain handles ideas.

Trust and reliance on AI: An experimental study on the extent and costs of overreliance on AI

Klingbeil, A., Grützner, C., & Schreck, P. (2024).

Computers in Human Behavior, 160, 108352.

https://doi.org/10.1016/j.chb.2024.108352

Abstract

Decision-making is undergoing rapid changes due to the introduction of artificial intelligence (AI), as AI recommender systems can help mitigate human flaws and increase decision accuracy and efficiency. However, AI can also commit errors or suffer from algorithmic bias. Hence, blind trust in technologies carries risks, as users may follow detrimental advice resulting in undesired consequences. Building upon research on algorithm appreciation and trust in AI, the current study investigates whether users who receive AI advice in an uncertain situation overrely on this advice — to their own detriment and that of other parties. In a domain-independent, incentivized, and interactive behavioral experiment, we find that the mere knowledge of advice being generated by an AI causes people to overrely on it, that is, to follow AI advice even when it contradicts available contextual information as well as their own assessment. Frequently, this overreliance leads not only to inefficient outcomes for the advisee, but also to undesired effects regarding third parties. The results call into question how AI is being used in assisted decision making, emphasizing the importance of AI literacy and effective trust calibration for productive deployment of such systems.

Highlights

• People overrely on AI advice for financially risky decisions in a domain-independent, interactive, behavioral experiment.

• Mere knowledge of advice being generated by an AI causes people to overrely on it.

• Participants follow AI advice that conflicts with available contextual information and is against their own interests.

• Overreliance on AI advice negatively affects human cooperation, leading to undesired results for advisees and third parties.

• Participants with higher trust in the advisor (attitude) also exhibit higher reliance on advice (behavior).

GPT-4 generated psychological reports in psychodynamic perspective

Kim, N., Lee, J., et al. (2025).

Frontiers in psychiatry, 16, 1473614.

https://doi.org/10.3389/fpsyt.2025.1473614

Abstract

Background: Recently, there have been active proposals on how to utilize large language models (LLMs) in the fields of psychiatry and counseling. It would be interesting to develop programs with LLMs that generate psychodynamic assessments to help individuals gain insights about themselves, and to evaluate the features of such services. However, studies on this subject are rare. This pilot study aims to evaluate quality, risk of hallucination (incorrect AI-generated information), and client satisfaction with psychodynamic psychological reports generated by GPT-4.

Methods: The report comprised five components: psychodynamic formulation, psychopathology, parental influence, defense mechanisms, and client strengths. Participants were recruited from individuals distressed by repetitive interpersonal issues. The study was conducted in three steps: 1) Questions provided to participants, designed to create psychodynamic formulations: 14 questions were generated by GPT for inferring psychodynamic formulations, while 6 fixed questions focused on the participants’ relationship with their parents. A total of 20 questions were provided. Using participants’ responses to these questions, GPT-4 generated the psychological reports. 2) Seven professors of psychiatry from different university hospitals evaluated the quality and risk of hallucinations in the psychological reports by reading the reports only, without meeting the participants. This quality assessment compared the psychological reports generated by GPT-4 with those inferred by the experts. 3) Participants evaluated their satisfaction with the psychological reports. All assessments were conducted using self-report questionnaires based on a Likert scale developed for this study.

Results: A total of 10 participants were recruited, and the average age was 32 years. The median response indicated that quality of all five components of the psychological report was similar to the level inferred by the experts. The risk of hallucination was assessed as ranging from unlikely to minor. According to the median response in the satisfaction evaluation, the participants agreed that the report is clearly understandable, insightful, credible, useful, satisfying, and recommendable.

Conclusion: This study suggests the possibility that artificial intelligence could assist users by providing psychodynamic interpretations.

Here are some thoughts:

This study tested whether GPT-4 could write useful psychodynamic reports for people with relationship problems. Experts rated the AI reports as similar in quality to what a human expert would write. The risk of harmful errors was low, and the clients found the reports insightful and helpful. However, the study was small and had limitations, including the risk that the AI might make an insensitive or upsetting interpretation. The main takeaway is that AI shows promise as a support tool for mental health, but human oversight is still essential.

Automation bias and assistive AI.

Khera, R., Simon, M. A., & Ross, J. S. (2023).

JAMA, 330(23), 2255. 

https://doi.org/10.1001/jama.2023.22557

At the point of care, artificial intelligence (AI) algorithms have been developed to augment diagnostic decisions and suggest appropriate care pathways, by leveraging complex information in a patient’s electronic health record, such as imaging, documentation, and diagnostic testing. With an increasing number of technologies integrated into the diagnosis, management, and even treatment of patients, the promise of AI to enhance accuracy, reduce errors, reduce clinician burnout, and improve clinical workflows may appear imminent.

MostAI algorithms aredesigned tobe assistive technologies—augmenting, not replacing, clinicians’

decision-making. AI models are imperfect and lack the broader clinical context that may be relevant for patient care. The expectation is that the diagnostic performance of clinicians supported by AI will exceed those of clinicians without such support.

A pdf is here.

Here are some thoughts:

This article highlights a critical problem with artificial intelligence in medicine: automation bias. This is when clinicians trust an AI’s recommendation too much, even when it is clearly wrong or contradicts their own judgment. The authors show that biased AI models can significantly lower the quality of patient care, and simply explaining how the AI works does not fix the issue. Clinicians, often working under time pressure, may defer to the tool instead of using their own expertise, which can lead to direct patient harm.

The key takeaway is that keeping a human “in the loop” is not enough to ensure safety. Current regulatory approaches focus too much on the AI’s technical accuracy and not enough on how real clinicians actually use these tools in practice. The authors argue that better training, higher safety standards, and truly interpretable AI are needed. Without these changes, the excitement around medical AI risks overshadowing its primary goal: improving patient care, not undermining it.