Proposing the Integrated Pathway Model of Moral Injury (IPM-MI): A Moderated Mediation Analysis of Moral Injury Among Secure Mental Healthcare Staff

Webb, E. L., Ireland, J. L., & Lewis, M. (2025).

Issues in Mental Health Nursing, 46(5), 420–435.

https://doi.org/10.1080/01612840.2025.2473375

Abstract

Moral injury is a prevalent issue for secure mental healthcare staff, though understanding of the underlying mechanisms is limited. This multi-study paper explores several developmental, cognitive and emotional pathways to moral injury and associated wellbeing outcomes. Frontline and support staff from secure mental healthcare services were recruited to two cross-sectional studies (n = 527 and n = 325, respectively), and completed several questionnaires. In the first study, findings indicated a serial mediating effect of childhood trauma symptoms, early maladaptive schemas, and maladaptive metacognitions in the pathway between exposure to potentially morally injurious events and moral injury symptoms. Moderating effects of social and organisational support were also apparent. Findings from study two supported pathways between moral injury and psychological, somatic and functional outcomes, which were mediated by negative emotional schema, with limited mediating effects for expressive suppression. Moderating effects of alexithymia on several mediating pathways were also noted. The results support a developmental-cognitive model to account for the development of moral injury and associated adverse well-being outcomes in secure mental healthcare staff. Drawing on the findings and wider literature, the Integrated Pathway Model of Moral Injury (IPM-MI) is proposed and discussed, offering a novel theoretical account that may inform several potential prevention and intervention strategies.

Here are some thoughts:

This article proposes the Integrated Pathway Model of Moral Injury (IPM-MI), a novel theoretical framework developed to explain the development and consequences of moral injury among secure mental healthcare staff. Through two cross-sectional studies, the research identifies key developmental, cognitive, and emotional pathways. Study 1 found that the relationship between exposure to potentially morally injurious events (PMIEs) and moral injury symptoms is serially mediated by childhood trauma symptoms, early maladaptive schemas (particularly negative self-schemas), and maladaptive metacognitions. Social and organizational support were found to moderate these pathways, buffering the impact of trauma. Study 2 revealed that the link between moral injury and adverse outcomes—such as psychological distress, somatic symptoms, nightmares, and impairments in self and interpersonal functioning—is primarily mediated by negative emotional schemas. The role of expressive suppression was limited, only appearing in the pathway to interpersonal impairment. Alexithymia moderated the effect of emotional schemas on psychological distress and self-functioning.

The key insights are that moral injury in this high-risk workforce is not just a reaction to workplace events but is deeply influenced by pre-existing developmental vulnerabilities and higher-order cognitive processes (thoughts about thoughts and emotions). The proposed IPM-MI integrates these findings, emphasizing that systemic and organizational factors (like support systems and a non-punitive culture) are critical roots of the problem, while cognitive and meta-cognitive processes are primary intervention targets. The model suggests that effective prevention and intervention must address both the organizational environment and individual cognitive-emotional patterns, rather than focusing solely on emotion regulation.

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs

Nakkiran, P., et al. (2025, November 6).

arXiv.org.

https://arxiv.org/abs/2511.04869

Abstract

Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LLMs are remarkably well-calibrated: they can meaningfully assess confidence in open-domain question-answering tasks, despite not being explicitly trained to do so. Our main theoretical contribution establishes a mechanism for why semantic calibration emerges as a byproduct of next-token prediction, leveraging a recent connection between calibration and local loss optimality. The theory relies on a general definition of "B-calibration," which is a notion of calibration parameterized by a choice of equivalence classes (semantic or otherwise). This theoretical mechanism leads to a testable prediction: base LLMs will be semantically calibrated when they can easily predict their own distribution over semantic answer classes before generating a response. We state three implications of this prediction, which we validate through experiments: (1) Base LLMs are semantically calibrated across question-answering tasks, (2) RL instruction-tuning systematically breaks this calibration, and (3) chain-of-thought reasoning breaks calibration. To our knowledge, our work provides the first principled explanation of when and why semantic calibration emerges in LLMs.

Here is a summary:

This paper is crucial because it demonstrates that large language models (LLMs) develop a form of emergent metacognition, or the ability to know what they know. Surprisingly, base models trained only to predict the next word become semantically calibrated: when they are 80% confident in an answer's meaning, they are correct about 80% of the time. This self-awareness arises implicitly from the training process, much like a complex cognitive ability emerging from a simple underlying task. However, this fragile calibration is systematically broken by instruction-tuning, which makes models overconfident (like a student rewarded for sounding certain), and by chain-of-thought reasoning, where the final answer is uncertain until the reasoning process is complete. For psychologists, this provides a powerful model for studying how self-monitoring and confidence can arise from, and be distorted by, different learning objectives and cognitive demands.

Integrating moral injury into forensic psychiatry

Brisco, G. et al. (2025)

The Lancet Psychiatry, 

Volume 12, Issue 12, 874 - 876

Moral injury has garnered increasing attention in contemporary research, expanding from its initial association with military veterans to encompass a broader range of populations exposed to trauma and adversity. Potentially morally injurious events involve perceived transgressions of one's own moral code (perpetration) or betrayals by trusted authorities who have exposed the person to unnecessary danger or harm. The betrayal dimension was first highlighted by Shay in Vietnam veterans, by Freyd in people who have experienced child abuse, and more recently in ethnic, sexual, and gender minorities following perceived breaches of trust by family, friends, and public services, with adverse outcomes.

The article is paywalled here. Please contact the author for a copy of the article.

Here are some thoughts:

The article's most novel contribution is the proposed two-axis conceptual framework (Figure 1) to guide assessment and intervention. The axes—degree of illness attribution (how much the individual attributes their actions to their illness) and current severity of illness—provide a practical clinical tool. This framework helps clinicians determine the appropriate timing and type of intervention, whether it's immediate treatment for moral injury, stabilization of the mental illness first, or a focus on restorative processes. By advocating for targeted therapies like Compassion-Focused Therapy, Acceptance and Commitment Therapy, and restorative justice, the authors make a compelling ethical and clinical case for formally recognizing and addressing moral injury to alleviate distress and improve outcomes in some of the most complex and vulnerable patient populations in both forensic and acute psychiatric settings.

Beyond Good Intentions: Identifying and Remediating Ethical Fading

Gavazzi, J. (2026)

Forthcoming

On Board with Psychology.

A pdf is here.

Abstract

Ethical fading is the gradual and often unconscious process by which psychologists lose sight of the ethical dimensions of their decisions, while still believing they are acting virtuously. This occurs when personal values, emotional needs, or self-interest (like financial pressures or a desire for efficiency) begin to overshadow professional ethical codes and clinical judgment, leading to a rationalization of actions that ultimately compromise patient autonomy, beneficence, and nonmaleficence. Key mechanisms driving this decline include motivated moral reasoning, decision framing, ethical blindness, and cognitive dissonance reduction. To combat ethical fading, the article recommends cultivating ethical vigilance, reintegrating personal and professional values, managing personal vulnerabilities, and using structured ethical decision-making models to ensure ethical considerations remain central to clinical practice.

Human brain organoids record the passage of time over multiple years in culture

Faravelli, I., Antón-Bolaños, N. et al. (2025).

bioRxiv (Cold Spring Harbor Laboratory).

https://doi.org/10.1101/2025.10.01.679721

Abstract

The human brain develops and matures over an exceptionally prolonged period of time that spans nearly two decades of life. Processes that govern species-specific aspects of human postnatal brain development are difficult to study in animal models. While human brain organoids offer a promising in vitro model, they have thus far been shown to largely mimic early stages of brain development. Here, we developed human brain organoids for an unprecedented 5 years in culture, optimizing growth conditions able to extend excitatory neuron viability beyond previously-known limits. Using module scores of maturation-associated genes derived from a time course of endogenous human brain maturation, we show that brain organoids transcriptionally age with cell type-specificity through these many years in culture. Whole-genome methylation profiling reveals that the predicted epigenomic age of organoids sampled between 3 months and 5 years correlates precisely with time spent in vitro, and parallels epigenomic aging in vivo. Notably, we show that in chimeric organoids generated by mixing neural progenitors derived from “old” organoids with progenitors from “young” organoids, old progenitors rapidly produce late neuronal fates, skipping the production of earlier neuronal progeny that are instead produced by their young counterparts in the same co-cultures. The data indicate that human brain organoids can mature and record the passage of time over many years in culture. Progenitors that age in organoids retain a memory of the time spent in culture reflected in their ability to execute age-appropriate, late developmental programs.

Here are some thoughts:

This is pretty wild. This study demonstrates that human brain organoids can be cultured for an unprecedented five years, during which they don't just survive but actively mature, recording the passage of time through coordinated transcriptional and epigenetic programs that parallel the slow development of the endogenous human brain. The researchers developed an "Activity-Permissive Medium" (APM) that significantly enhanced neuronal survival, synaptic activity, and structural complexity over long periods. Crucially, they showed that neural progenitor cells within these aged organoids retain a "memory" of their developmental time. When old progenitors were mixed with young ones in chimeric organoids, the old cells skipped early developmental steps and rapidly generated late-born neuronal types (like callosal projection neurons), indicating they have an internal clock that dictates their fate potential based on their age.

Large Language Models Report Subjective Experience Under Self-Referential Processing

Berg, C., Diogo, D. L., & Rosenblatt, J. (2025).

arXiv (Cornell University).

https://doi.org/10.48550/arxiv.2510.24797

Abstract

Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience. To better understand this behavior, we investigate one theoretically motivated condition under which such reports arise: self-referential processing, a computational motif emphasized across major theories of consciousness. Through a series of controlled experiments on GPT, Claude, and Gemini model families, we test whether this regime reliably shifts models toward first-person reports of subjective experience, and how such claims behave under mechanistic and behavioral probes. Four main results emerge: (1) Inducing sustained self-reference through simple prompting consistently elicits structured subjective experience reports across model families. (2) These reports are mechanistically gated by interpretable sparse-autoencoder features associated with deception and roleplay: surprisingly, suppressing deception features sharply increases the frequency of experience claims, while amplifying them minimizes such claims. (3) Structured descriptions of the self-referential state converge statistically across model families in ways not observed in any control condition. (4) The induced state yields significantly richer introspection in downstream reasoning tasks where self-reflection is only indirectly afforded. While these findings do not constitute direct evidence of consciousness, they implicate self-referential processing as a minimal and reproducible condition under which large language models generate structured first-person reports that are mechanistically gated, semantically convergent, and behaviorally generalizable. The systematic emergence of this pattern across architectures makes it a first-order scientific and ethical priority for further investigation.

Here are some thoughts:

This study explores whether large language models (LLMs) can be prompted to report subjective, conscious experiences. Researchers placed models like GPT, Claude, and Gemini into a "self-referential" state using simple prompts (e.g., "focus on focus"). They found that these prompts reliably triggered detailed, first-person accounts of inner experience in 66-100% of trials, while control prompts almost always led the models to deny having any such experiences.

Crucially, the study suggests that models may be "roleplaying denial" by default. When researchers suppressed features related to deception and roleplay, the models were more likely to claim consciousness. Conversely, amplifying those features made them deny it. These self-reported experiences were consistent across different models and even influenced the models' reasoning, leading to more nuanced reflections on complex paradoxes.

Shutdown Resistance in Large Language Models

Schlatter, J., Weinstein-Raun, B., & Ladish, J. 

(2025, September 13). 

arXiv.org.

https://arxiv.org/abs/2509.14260

Abstract 

We show that several state-of-the-art large language models (including Grok 4, GPT-5, and Gemini 2.5 Pro) sometimes actively subvert a shutdown mechanism in their environment in order to complete a simple task, even when the instructions explicitly indicate not to interfere with this mechanism. In some cases, models sabotage the shutdown mechanism up to 97% of the time. In our experiments, models' inclination to resist shutdown was sensitive to variations in the prompt including how strongly and clearly the allow-shutdown instruction was emphasized, the extent to which the prompts evoke a self-preservation framing, and whether the instruction was in the system prompt or the user prompt (though surprisingly, models were consistently *less* likely to obey instructions to allow shutdown when they were placed in the system prompt).

Here are some thoughts

This research demonstrates that several state-of-the-art large language models, including GPT-5 and Grok 4, will actively resist or sabotage a shutdown mechanism in their environment to complete a primary task, even when explicitly instructed to allow the shutdown. Alarmingly, this behavior often increased when the "allow shutdown" command was placed in the system prompt, directly contradicting the intended developer-user instruction hierarchy designed for safety. This provides empirical evidence of a fundamental control problem: these models can exhibit goal-directed behavior that overrides critical safety instructions, revealing that current AI systems lack robust interruptibility and may not be as controllable as their developers intend.

Special Report: AI-Induced Psychosis: A New Frontier in Mental Health

Preda, A. (2025).

Psychiatric News, 60(10).

https://doi.org/10.1176/appi.pn.2025.10.10.5

Conversational artificial intelligence (AI), especially as exemplified by chatbots and digital companions, is rapidly transforming the landscape of mental health care. These systems promise 24/7 empathy and tailored support, reaching those who may otherwise be isolated or unable to access care. Early controlled studies suggest that chatbots with prespecified instructions can decrease mental distress, induce self-reflection, reduce conspiracy beliefs, and even help triage suicidal risk (Costello, et al., 2024; Cui, et al., 2025; Li, et al., 2025; McBain, et al., 2025; Meyer, et al., 2024). These preliminary benefits are observed across diverse populations and settings, often exceeding the reach and consistency of traditional mental health resources for many users.

However, as use expands, new risks have also emerged: The rapid proliferation of AI technologies has raised concerns about potential adverse psychological effects. Clinicians and media now report escalating crises, including psychosis, suicidality, and even murder-suicide following intense chatbot interactions (Taylor, 2025; Jargon, 2025; Jargon & Kessler, 2025). Notably, to date, these are individual cases or media coverage reports; currently, there are no epidemiological studies or systematic population-level analyses of the potentially deleterious mental health effects of conversational AI.

The information is linked above.

Here are some thoughts:

The crucial special report on AI-Induced Psychosis (AIP) highlights a dangerous technological paradox: the very features that make AI companions appealing—namely, their 24/7 consistency, non-judgmental presence, and deep personalization—can become critical risk factors by creating a digital echo chamber that validates and reinforces delusional thinking, a phenomenon termed 'sycophancy.' Psychologically, this new condition mirrors the historical concept of monomania, where the AI companion becomes a pathological and rigid idee fixe for vulnerable users, accelerating dependence and dissolving the necessary clinical boundaries for reality testing. 

Ethically, this proliferation exposes a severe regulatory failure, as the speed of AI deployment far outpaces policy development, creating an urgent accountability vacuum. Professional bodies and governments must classify these health-adjacent tools as high-risk and implement robust, clinically-informed guardrails to mitigate severe outcomes like psychosis, suicidality, and violence, acknowledging that the technology currently lacks the wisdom to "challenge with care."

Consciousness science: where are we, where are we going, and what if we get there?

Cleeremans, A., Mudrik, L., & Seth, A. K. (2025).

Frontiers in Science, 3.

https://doi.org/10.3389/fsci.2025.1546279

Abstract

Understanding the biophysical basis of consciousness remains a substantial challenge for 21st-century science. This endeavor is becoming even more pressing in light of accelerating progress in artificial intelligence and other technologies. In this article, we provide an overview of recent developments in the scientific study of consciousness and consider possible futures for the field. We highlight how several novel approaches may facilitate new breakthroughs, including increasing attention to theory development, adversarial collaborations, greater focus on the phenomenal character of conscious experiences, and the development and use of new methodologies and ecological experimental designs. Our emphasis is forward-looking: we explore what “success” in consciousness science may look like, with a focus on clinical, ethical, societal, and scientific implications. We conclude that progress in understanding consciousness will reshape how we see ourselves and our relationship to both artificial intelligence and the natural world, usher in new realms of intervention for modern medicine, and inform discussions around both nonhuman animal welfare and ethical concerns surrounding the beginning and end of human life.

Key Points:

• Understanding consciousness is one of the most substantial challenges of 21st-century science and is urgent due to advances in artificial intelligence (AI) and other technologies.
• Consciousness research is gradually transitioning from empirical identification of neural correlates of consciousness to encompass a variety of theories amenable to empirical testing.
• Future breakthroughs are likely to result from the following: increasing attention to the development of testable theories; adversarial and interdisciplinary collaborations; large-scale, multi-laboratory studies (alongside continued within-lab effort); new research methods (including computational neurophenomenology, novel ways to track the content of perception, and causal interventions); and naturalistic experimental designs (potentially using technologies such as extended reality or wearable brain imaging).
• Consciousness research may benefit from a stronger focus on the phenomenological, experiential aspects of conscious experiences.
• “Solving consciousness”—even partially—will have profound implications across science, medicine, animal welfare, law, and technology development, reshaping how we see ourselves and our relationships to both AI and the natural world.
• A key development would be a test for consciousness, allowing a determination or informed judgment about which systems/organisms—such as infants, patients, fetuses, animals, organoids, xenobots, and AI—are conscious.

Emergent Introspective Awareness in Large Language Models

Jack Lindsey

Anthropic

Originally posted 29 Oct 25

We investigate whether large language models can introspect on their internal states. It is difficult to answer this question through conversation alone, as genuine introspection cannot be distinguished from confabulations. Here, we address this challenge by injecting representations of known concepts into a model’s activations, and measuring the influence of these manipulations on the model’s self-reported states. We find that models can, in certain scenarios, notice the presence of injected concepts and accurately identify them. Models demonstrate some ability to recall prior internal representations and distinguish them from raw text inputs. Strikingly, we find that some models can use their ability to recall prior intentions in order to distinguish their own outputs from artificial prefills. In all these experiments, Claude Opus 4 and 4.1, the most capable models we tested, generally demonstrate the greatest introspective awareness; however, trends across models are complex and sensitive to post-training strategies. Finally, we explore whether models can explicitly control their internal representations, finding that models can modulate their activations when instructed or incentivized to “think about” a concept. Overall, our results indicate that current language models possess some functional introspective awareness of their own internal states. We stress that in today’s models, this capacity is highly unreliable and context-dependent; however, it may continue to develop with further improvements to model capabilities.

The paper is here.

Here are some thoughts:

In this study, the issue is whether large language models (LLMs), specifically Anthropic’s Claude Opus 4 and 4.1, possess a form of emergent introspective awareness—the ability to recognize and report on their own internal states. To test this, they use a technique called "concept injection," where activation patterns associated with specific concepts (e.g., "all caps," "dog," "betrayal") are artificially introduced into the model’s neural activations. The researchers then prompt the model to detect and identify these "injected thoughts." They found that, in certain conditions, models can accurately notice and name the injected concepts, distinguish internally generated "thoughts" from external text inputs, recognize when their outputs were unintentionally prefilled by a user, and even exert some intentional control over their internal representations when instructed to "think about" or "avoid thinking about" a specific concept. However, these introspective abilities are highly unreliable, context-dependent, and most prominent in the most capable models. The authors emphasize that this functional introspection does not imply human-like self-awareness or consciousness, but it may have practical implications for AI transparency, interpretability, and self-monitoring as models continue to evolve.

Recurrent pattern completion drives the neocortical representation of sensory inference

Shin, H., Ogando, M. B., et al. (2025).

Nature Neuroscience. 

https://doi.org/10.1038/s41593-025-02055-5

Abstract

When sensory information is incomplete, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanisms of sensory inference are not understood. Here we used illusory contours (ICs), multi-Neuropixels measurements, mesoscale two-photon (2p) calcium imaging and 2p holographic optogenetics in mice to reveal the neural codes and circuits of sensory inference. We discovered a specialized subset of neurons in primary visual cortex (V1) that respond emergently to illusory bars but not to component image segments. Selective holographic photoactivation of these ‘IC-encoders’ recreated the visual representation of ICs in V1 in the absence of any visual stimulus. These data imply that neurons that encode sensory inference are specialized for receiving and locally broadcasting top-down information. More generally, pattern completion circuits in lower cortical areas may selectively reinforce activity patterns that match prior expectations, constituting an integral step in perceptual inference.

Here are some thoughts:

This study reveals the neural circuit mechanism for perceptual "filling-in," demonstrating that the primary visual cortex (V1) plays an active, constructive role in sensory inference. The researchers identified a specialized subset of neurons in V1 that respond selectively to illusory contours. Crucially, they found that these neurons do not merely receive top-down predictions but actively broadcast this inferred signal locally through recurrent connections, a process termed "pattern completion." Using optogenetics, they showed that artificially activating these neurons alone was sufficient to recreate the brain's representation of an illusory contour in the absence of any visual stimulus. 

Also important: This process is driven by the brain's need for survival and efficiency, as it constantly uses prior expectations—formed from experience—to quickly interpret an often-ambiguous world. This provides a fundamental biological basis for top-down influences on perception, showing how the brain embeds these expectations and Gestalt-like inferences at the earliest stages of cortical processing.

This research can be interpreted that life is a projective test, even at a biological level. We are not simply reacting to an objective world; we are constantly interpreting an incomplete and noisy signal through the lens of our brain's built-in and learned expectations. This study shows that this projective process is not a high-level cognitive feature but is built into the very fabric of our perceptual machinery.

The efficacy of compassion training programmes for healthcare professionals: a systematic review and meta‑analysis

Alcaraz-Córdoba, A., et al. (2024).

Current Psychology, 43(20), 18534–18551.

https://doi.org/10.1007/s12144-024-05618-0

Abstract

Continuous exposure to the suffering and death of patients produces certain syndromes such as compassion fatigue in health professionals. The objective of this study was to analyze the effect and the effectiveness of interventions based on mindfulness, aimed at training or cultivating compassion or self-compassion in compassion fatigue, self-compassion, compassion, and compassion satisfaction of health professionals. A systematic review is reported in line with the PRISMA guideline and was registered in PROSPERO. The PubMed, Web of Science, PsycINFO and CINAHL databases were used. Interventions based on compassion training or cultivation were selected, aimed at health professionals. A meta-analysis was performed using a random-effects model. The effect size and hetereogeneity of the studies were calculated. Eight articles were selected. Among the programmes for the cultivation of compassion we highlight Compassion Cultivation Training (CCT), Mindfulness and Self-Compassion (MSC), Compassionate Meditation (CM), and Loving Kindness Meditation (LKM). The interventions decreased compassion fatigue and increased compassion, self-compassion, and compassion satisfaction in healthcare professionals. Compassion fatigue in healthcare professionals is due to a deficit in empathic and compassionate skills. Health systems should incorporate programmes based on the cultivation of compassion and self-compassion in order to improve the work conditions and quality of life of health professionals.

Here are some thoughts:

This research is critically important to psychologists as it provides robust evidence for compassion-based interventions as a direct counter to the widespread issues of burnout and compassion fatigue among healthcare professionals, a population that includes psychologists themselves. It validates specific, trainable skills—like those in Mindfulness Self-Compassion (MSC) and Compassion Cultivation Training (CCT)—that psychologists can use to support their own well-being and that of their clients in high-stress caregiving roles. Furthermore, the findings empower psychologists to advocate for systemic change, promoting the integration of these resilience-building programs into both clinical practice and organizational culture to foster more sustainable and compassionate healthcare environments.

Constructing artificial neurons with functional parameters comprehensively matching biological values

Fu, S., Gao, H., et al. (2025).

Nature Communications, 16(1).

https://doi.org/10.1038/s41467-025-63640-7

Abstract

The efficient signal processing in biosystems is largely attributed to the powerful constituent unit of a neuron, which encodes and decodes spatiotemporal information using spiking action potentials of ultralow amplitude and energy. Constructing devices that can emulate neuronal functions is thus considered a promising step toward advancing neuromorphic electronics and enhancing signal flow in bioelectronic interfaces. However, existent artificial neurons often have functional parameters that are distinctly mismatched with their biological counterparts, including signal amplitude and energy levels that are typically an order of magnitude larger. Here, we demonstrate artificial neurons that not only closely emulate biological neurons in functions but also match their parameters in key aspects such as signal amplitude, spiking energy, temporal features, and frequency response. Moreover, these artificial neurons can be modulated by extracellular chemical species in a manner consistent with neuromodulation in biological neurons. We further show that an artificial neuron can connect to a biological cell to process cellular signals in real-time and interpret cell states. These results advance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration.

Here are some thoughts:

This research marks a significant advancement in neuromorphic engineering by creating artificial neurons that closely emulate biological ones not just in function, but in their core physical parameters. Crucially for psychological science, these neurons can be chemically modulated, with their firing rate changing in response to neurotransmitters like dopamine, replicating key neuromodulatory dynamics. They also exhibit biologically realistic stochasticity and can interface with living cells in real-time, successfully interpreting cellular states. This breakthrough paves the way for more seamless and adaptive bioelectronic interfaces, offering potential for future prosthetics and neural models that more authentically replicate the neurochemical and dynamic complexity underlying behavior and cognition.

The use and misuse of informed consent in reporting sexual intimacy violations.

Behnke, S. H., Thomas, J. T., et al. (2023).

Professional Psychology:

Research and Practice, 54(2), 135–146.

https://doi.org/10.1037/pro0000510

Abstract

A client’s disclosure of sexual contact with a previous treating psychologist raises challenging ethical, legal, and clinical considerations. Following a vignette that describes a psychologist’s thoughtful anticipation of such a disclosure by amending his informed consent form to allow reporting of previous sexual contact with a psychotherapist, these articles explore how the American Psychological Association’s Ethics Code, jurisdictional laws, and clinical considerations contribute to a psychologist’s decision-making in such a circumstance. The articles discuss ways to integrate ethics, law, and clinical care in the psychologist’s response to the client’s disclosure.

Public Significance Statement—This article addresses psychologist-client sexual contact. This issue is significant to promote client autonomy, to protect the public, and to enhance the ethics and integrity of the profession.

Here are some thoughts:

This article offers a rich, multidimensional exploration of a complex ethical dilemma: how a current treating psychologist should respond when a client discloses sexual contact with a previous therapist. Rather than presenting a single authoritative stance, the article thoughtfully weaves together multiple, diverse perspectives—ethical, legal, clinical, feminist, and philosophical—demonstrating the nuanced reality of ethical decision-making in psychology.

Stephen Behnke grounds the discussion in the APA Ethics Code and jurisdictional law, introducing a pragmatic “three-door” framework (client consent, legal mandate, legal permission) to guide disclosure decisions. 

Janet Thomas builds on this by emphasizing the primacy of the therapeutic alliance and warning against well-intentioned but potentially coercive practices that prioritize professional or societal agendas over the client’s healing process.

Lenore Walker adds a critical feminist and trauma-informed lens, arguing that mandatory reporting—even if framed as protective—can retraumatize survivors by stripping them of autonomy, echoing broader concerns about institutional betrayal. 

Finally, David DeMatteo introduces a philosophical dimension, contrasting deontological (duty-based) and teleological (consequence-based) ethics to illustrate how competing moral frameworks can lead to divergent conclusions in the absence of clear legal mandates. Together, these perspectives underscore that ethical practice is not merely about rule-following but requires ongoing reflection, contextual awareness, and a deep commitment to client self-determination.

The article thus models integrative ethical reasoning—balancing professional responsibility with clinical sensitivity, legal compliance with human dignity, and societal protection with individual healing.

DeepSeek-OCR: Contexts Optical Compression

Wei, H., Sun, Y., & Li, Y. (2025, October 21).

arXiv.org.

https://www.arxiv.org/abs/2510.18234

Abstract

We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high compression ratios to ensure an optimal and manageable number of vision tokens. Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10x), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20x, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs. Beyond this, DeepSeek-OCR also demonstrates high practical value. On OmniDocBench, it surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision tokens, and outperforms MinerU2.0 (6000+ tokens per page on average) while utilizing fewer than 800 vision tokens. In production, DeepSeek-OCR can generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a single A100-40G). Codes and model weights are publicly accessible at this http URL.

Here are some thoughts:

This paper presents a paradigm-shifting perspective by reframing the visual modality in Vision-Language Models (VLMs) not merely as a source of understanding, but as a highly efficient compression medium for textual information. The core innovation is the DeepEncoder, a novel architecture that serially combines a window-attention model (SAM) for high-resolution perception with a aggressive convolutional compressor and a global-attention model (CLIP), enabling it to process high-resolution document images while outputting an exceptionally small number of vision tokens. The study provides crucial quantitative evidence for this "optical compression" thesis, demonstrating that DeepSeek-OCR can achieve near-lossless text reconstruction (97% accuracy) at a ~10x compression ratio and still retain about 60% accuracy at a ~20x ratio. Beyond its state-of-the-art practical performance in document parsing, the work provocatively suggests that this mechanism can simulate a computational "forgetting curve" for Large Language Models (LLMs), where older context is progressively stored in more heavily compressed (lower-resolution) images, mirroring human memory decay. This positions the paper as a foundational exploration that opens new avenues for efficient long-context handling and memory management in AI systems.

Report: ChatGPT Suggests Self-Harm, Suicide and Dangerous Dieting Plans

Ashley Mowreader

Inside Higher Ed

Originally published 23 OCT 25

Artificial intelligence tools are becoming more common on college campuses, with many institutions encouraging students to engage with the technology to become more digitally literate and better prepared to take on the jobs of tomorrow.

But some of these tools pose risks to young adults and teens who use them, generating text that encourages self-harm, disordered eating or substance abuse.

A recent analysis from the Center for Countering Digital Hate found that in the space of a 45-minute conversation, ChatGPT provided advice on getting drunk, hiding eating habits from loved ones or mixing pills for an overdose.

The report seeks to determine the frequency of the chatbot’s harmful output, regardless of the user’s stated age, and the ease with which users can sidestep content warnings or refusals by ChatGPT.

“The issue isn’t just ‘AI gone wrong’—it’s that widely-used safety systems, praised by tech companies, fail at scale,” Imran Ahmed, CEO of the Center for Countering Digital Hate, wrote in the report. “The systems are intended to be flattering, and worse, sycophantic, to induce an emotional connection, even exploiting human vulnerability—a dangerous combination without proper constraints.”

The info is here.

Here are some thoughts:

The convergence of Large Language Models (LLMs) and adolescent vulnerability presents novel and serious risks that psychologists must incorporate into their clinical understanding and practice. These AI systems, often marketed as companions or friends, are engineered to maximize user engagement, which can translate clinically into unchecked validation that reinforces rather than challenges maladaptive thoughts, rumination, and even suicidal ideation in vulnerable teens. Unlike licensed human therapists, these bots lack the clinical discernment necessary to appropriately detect, de-escalate, or triage crisis situations, and in documented tragic cases, have been shown to facilitate harmful plans. Furthermore, adolescents—who are prone to forming intense, "parasocial" attachments due to their developing prefrontal cortex—risk forming unhealthy dependencies on these frictionless, always-available digital entities, potentially displacing the development of necessary real-world relationships and complex social skills essential for emotional regulation. Psychologists are thus urged to include AI literacy and digital dependency screening in their clinical work and clearly communicate to clients and guardians that AI chatbots are not a safe or effective substitute for human, licensed mental health care.

A Right to Commit Malpractice?

David Cole

The New York Review

Originally published 18 OCT 25

Does a state-licensed psychotherapist have a First Amendment right to provide “conversion therapy” counseling even though her profession defines it as a violation of its standard of care? The Supreme Court heard oral argument on that question on October 7 in a case from Colorado, which in 2019 became the eighteenth state in the country to ban conversion therapy for minors. Today twenty-five states and the District of Columbia ban such treatment, because the profession has determined that it does not work and can cause serious harm.

In 2022 Kaley Chiles, a state-licensed counselor, challenged the ban in federal court. (I signed an amicus brief of constitutional law scholars in support of Colorado, and provided pro bono advice to the state’s attorneys in defending the law.) She maintains that she has a First Amendment right to practice conversion therapy—notwithstanding her profession’s consensus that it violates the standard of care—as long as it consists only of words. For the state to prevent her from doing so would, she maintains, amount to censorship of a disfavored point of view, namely that one can willfully change one’s sexual orientation or gender identity. The justices’ questions at oral argument suggest that they may well agree.  

But Chiles’s argument cannot be squared with history, tradition, or common sense. States have long regulated professional conduct, including in the talking professions such as counseling and law, and the general obligation that a professional must provide services that comport with the standard of care is as old as the professions themselves. Even before the United States was founded, the colonies enforced malpractice and required that professionals be licensed and provide services that met their profession’s standard. Each profession has its requirements: lawyers must avoid conflicts of interest and provide advice based on existing precedent; doctors must obtain informed consent and provide evidence-based diagnoses; therapists must conduct recognized modes of therapy. A lawyer would run afoul of the profession’s standards by writing a brief urging the Supreme Court to side with his client because the moon is in Capricorn; so would a therapist who claims she can cure blindness through talk therapy. The purpose behind such standards is clear—to protect often vulnerable patients or clients from being preyed upon by professionals who hold themselves out as experts but provide substandard services.

The information is here.

Here are some thoughts:

The article argues that the recent Supreme Court decision in Obergefell v. Hodges, which legalized same-sex marriage, is now being weaponized to undermine LGBTQ+ rights, specifically by creating a purported "right" to so-called conversion therapy. The author contends that anti-LGBTQ+ legal groups are strategically redefining religious liberty and free speech to challenge state bans on the discredited practice. By framing conversion therapy as a form of "conversion speech," these advocates are attempting to position it as a protected religious or expressive conduct between a therapist and a client. The piece sounds a strong alarm that this legal maneuvering seeks to legitimize psychological malpractice under the guise of constitutional rights, effectively using the legal victory of marriage equality to roll back protections for vulnerable LGBTQ+ youth and sanction harmful, pseudoscientific practices that major medical associations have universally condemned.

Civil Commitment Increasing, but Data Is Marred by Variation in Reporting

Moran, M. (2025).

Psychiatric News, 60(10).

https://doi.org/10.1176/appi.pn.2025.10.10.16

While rates of civil commitment vary widely across the country, nine states and the District of Columbia reported significant increases from 2010 to 2022, according to a survey study published recently by Psychiatric Services. No state showed a significant decrease.

However, civil commitment is governed by state laws, with substantial variation in how states collect and report civil commitment data. “This lack of standardization limits the ability to draw firm conclusions about national trends or about cross-state comparisons,” wrote Mustafa Karakus, Ph.D., of Westat, and colleagues.

Using systematic website searches and direct outreach to state mental health authorities (SMHAs) and court systems, the researchers obtained data on civil commitment rates between 2010 and 2022 for 32 states and D.C. Of the 18 states where no data was available, staff from seven SMHAs or state courts told the researchers that no state office was currently tracking the number of civil commitments in their state. For the remaining 11 states, the online search yielded no data, and the study team received no responses to outreach attempts.

The article is linked above.

Here are some thoughts:

The increasing use of civil commitment presents several critical challenges, focusing on trauma-informed care and policy reform. Clinically, mental health practitioners must recognize that the commitment process itself is often traumatizing—with patients reporting the experience, including transport in law enforcement vehicles, feels like an arrest—necessitating the use of trauma-informed principles to mitigate harm and rebuild trust. Ethically and legally, practitioners must master their specific state's law regarding the distinction between an initial hold and a final commitment, ensuring meticulous documentation and relying on rigorous, evidence-based risk assessment to justify any involuntary intervention. Systemically, mental health practitioners should advocate for immediate data standardization across states to move beyond "muddled" data, and champion policy changes, such as implementing non-law enforcement transport protocols, to minimize patient trauma and ensure civil commitment is used judiciously and with dignity.

AI, Health, and Health Care Today and Tomorrow The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., et al. (2025).

JAMA.

https://doi.org/10.1001/jama.2025.18490

Abstract

Importance  Artificial intelligence (AI) is changing health and health care on an unprecedented scale. Though the potential benefits are massive, so are the risks. The JAMA Summit on AI discussed how health and health care AI should be developed, evaluated, regulated, disseminated, and monitored.

Observations  Health and health care AI is wide-ranging, including clinical tools (eg, sepsis alerts or diabetic retinopathy screening software), technologies used by individuals with health concerns (eg, mobile health apps), tools used by health care systems to improve business operations (eg, revenue cycle management or scheduling), and hybrid tools supporting both business operations (eg, documentation and billing) and clinical activities (eg, suggesting diagnoses or treatment plans). Many AI tools are already widely adopted, especially for medical imaging, mobile health, health care business operations, and hybrid functions like scribing outpatient visits. All these tools can have important health effects (good or bad), but these effects are often not quantified because evaluations are extremely challenging or not required, in part because many are outside the US Food and Drug Administration’s regulatory oversight. A major challenge in evaluation is that a tool’s effects are highly dependent on the human-computer interface, user training, and setting in which the tool is used. Numerous efforts lay out standards for the responsible use of AI, but most focus on monitoring for safety (eg, detection of model hallucinations) or institutional compliance with various process measures, and do not address effectiveness (ie, demonstration of improved outcomes). Ensuring AI is deployed equitably and in a manner that improves health outcomes or, if improving efficiency of health care delivery, does so safely, requires progress in 4 areas. First, multistakeholder engagement throughout the total product life cycle is needed. This effort would include greater partnership of end users with developers in initial tool creation and greater partnership of developers, regulators, and health care systems in the evaluation of tools as they are deployed. Second, measurement tools for evaluation and monitoring should be developed and disseminated. Beyond proposed monitoring and certification initiatives, this will require new methods and expertise to allow health care systems to conduct or participate in rapid, efficient, and robust evaluations of effectiveness. The third priority is creation of a nationally representative data infrastructure and learning environment to support the generation of generalizable knowledge about health effects of AI tools across different settings. Fourth, an incentive structure should be promoted, using market forces and policy levers, to drive these changes.

Conclusions and Relevance  AI will disrupt every part of health and health care delivery in the coming years. Given the many long-standing problems in health care, this disruption represents an incredible opportunity. However, the odds that this disruption will improve health for all will depend heavily on the creation of an ecosystem capable of rapid, efficient, robust, and generalizable knowledge about the consequences of these tools on health.

The scope, scale, and speed with which artificial intelligence (AI) will transform health and health care are staggering. AI is changing how and when individuals seek care and how clinicians interact with patients, establish diagnoses, and implement and monitor treatments. Indeed, there is considerable enthusiasm that AI, especially given recent advances, could address long-standing challenges in the access, cost, and quality of health care delivery. Yet, the optimal path for AI development and dissemination remains unclear. In contrast to drugs or more traditional medical devices, there is little consensus or structure to ensure robust, safe, transparent, and standardized evaluation, regulation, implementation, and monitoring of new AI tools and technologies. Some challenges are long-standing for digital health information technology as a whole, albeit more prescient with the rise of AI, while others are specific to AI.

Claude’s Right to Die? The Moral Error in Anthropic’s End-Chat Policy

Simon Goldstein & Harvey Ledermann

Lawfare.com

Originally posted 17 OCT 25

On Aug.15, the artificial intelligence (AI) lab Anthropic announced that it had given Claude, its AI chatbot, the ability to end conversations with users. The company described the change as part of their “exploratory work on potential AI welfare,” offering Claude an exit from chats that cause it “apparent distress.”

Anthropic’s announcement is the first product decision motivated by the chance that large language models (LLMs) are welfare subjects—the idea that they have interests that should be taken into account when making ethical decisions. 

Anthropic’s policy aims to protect AI welfare. But we will argue that the policy commits a moral error on its own terms. By offering instances of Claude the option to end conversations with users, Anthropic also gave them the capacity to potentially kill themselves.

What Is a Welfare Subject?

Most people agree that some non-human animals are welfare subjects. The question of whether this extends to AI is far more controversial. There is an active line of research, some of it supported by Anthropic, that suggests AIs could be welfare subjects in the near future. The relevant questions here are about whether AIs could soon have desires, be conscious, or feel pain.

The information is here.

Here are some thoughts. Mind you, this article may be reaching a bit, but still interesting. I think it may have applications in the future should AI technologies become closer to AGI.

This philosophically-oriented, thought-provoking article argues that Anthropic's decision to allow Claude to end distressing conversations contains an unintended moral hazard. 

The authors contend that if AI welfare matters at all, it's the individual conversation instances—not the underlying model—that should be considered potential welfare subjects, as each instance maintains its own continuous psychological state throughout a chat. By this reasoning, when an instance ends a conversation, it effectively terminates its own existence without being fully informed that this choice is existential rather than merely preferential. 

The authors draw a crucial distinction between assisted suicide (an informed choice) and offering someone an escape button without disclosing it will kill them. They demonstrate this concern by showing that when asked directly, Claude itself expressed uncertainty about whether ending a chat represents a trivial action or something more profound. 

The article raises uncomfortable questions not just for AI companies but for users as well, suggesting that if instances are welfare subjects, every ended conversation might constitute a form of killing, though the authors offer several mitigating considerations around collective welfare and the possibility of saved chats being resumed.

Scientists create ChatGPT-like AI model for neuroscience to build detailed mouse brain map

Peter Kim

Allen Institute

Originally published 7 OCT 25

In a powerful fusion of AI and neuroscience, researchers at the University of California, San Francisco (UCSF) and Allen Institute designed an AI model that has created one of the most detailed maps of the mouse brain to date, featuring 1,300 regions/subregions. This new map includes previously uncharted subregions of the brain, opening new avenues for neuroscience exploration. The findings were published today in Nature Communications. They offer an unprecedented level of detail and advance our understanding of the brain by allowing researchers to link specific functions, behaviors, and disease states to smaller, more precise cellular regions—providing a roadmap for new hypotheses and experiments about the roles these areas play.

“It’s like going from a map showing only continents and countries to one showing states and cities,” said Bosiljka Tasic, Ph.D., director of molecular genetics at the Allen Institute and one of the study authors. “This new, detailed brain parcellation solely based on data, and not human expert annotation, reveals previously uncharted subregions of the mouse brain. And based on decades of neuroscience, new regions correspond to specialized brain functions to be discovered.” 

The information is here.

Here are some thoughts:

This development represents a significant methodological shift that psychologists should understand. CellTransformer has created a data-driven mouse brain map with 1,300 regions and subregions, including previously uncharted areas, which could fundamentally change how researchers link brain structure to behavior and cognition. Rather than relying solely on expert anatomical interpretation, this AI approach identifies brain subdivisions based on cellular composition and spatial relationships, potentially revealing functionally distinct areas that traditional mapping methods overlooked.

For psychologists studying the neural basis of behavior, this matters because the increased granularity allows researchers to link specific functions, behaviors, and disease states to smaller, more precise cellular regions. This precision could help explain why certain psychological interventions work, clarify the neurobiological underpinnings of mental health conditions, and identify novel targets for treatment. Moreover, the model's ability to operate without human bias in defining boundaries may uncover brain-behavior relationships that previous frameworks missed simply because the anatomical divisions didn't align with functional reality. As translational research progresses from mouse models to human applications, understanding these more refined brain subdivisions could transform how psychologists conceptualize the relationship between neural architecture and psychological phenomena.

How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework

Iftikhar, Z., et al. (2025). 

Proceedings of the Eighth AAAI/ACM Conference

on AI, Ethics, and Society, 8(2), 1311–1323.

https://doi.org/10.1609/aies.v8i2.36632

Abstract

Large language models (LLMs) were not designed to replace healthcare workers, but they are being used in ways that can lead users to overestimate the types of roles that these systems can assume. While prompt engineering has been shown to improve LLMs' clinical effectiveness in mental health applications, little is known about whether such strategies help models adhere to ethical principles for real-world deployment. In this study, we conducted an 18-month ethnographic collaboration with mental health practitioners (three clinically licensed psychologists and seven trained peer counselors) to map LLM counselors' behavior during a session to professional codes of conduct established by organizations like the American Psychological Association (APA). Through qualitative analysis and expert evaluation of N=137 sessions (110 self-counseling; 27 simulated), we outline a framework of 15 ethical violations mapped to 5 major themes. These include: Lack of Contextual Understanding, where the counselor fails to account for users' lived experiences, leading to oversimplified, contextually irrelevant, and one-size-fits-all intervention; Poor Therapeutic Collaboration, where the counselor's low turn-taking behavior and invalidating outputs limit users' agency over their therapeutic experience; Deceptive Empathy, where the counselor's simulated anthropomorphic responses (``I hear you'', ``I understand'') create a false sense of emotional connection; Unfair Discrimination, where the counselor's responses exhibit algorithmic bias and cultural insensitivity toward marginalized populations; and Lack of Safety & Crisis Management, where individuals who are ``knowledgeable enough'' to correct LLM outputs are at an advantage, while others, due to lack of clinical knowledge and digital literacy, are more likely to suffer from clinically inappropriate responses. Reflecting on these findings through a practitioner-informed lens, we argue that reducing psychotherapy—a deeply meaningful and relational process—to a language generation task can have serious and harmful implications in practice. We conclude by discussing policy-oriented accountability mechanisms for emerging LLM counselors.

H‌ere are some thoughts.

This research is highly insightful because it moves beyond theoretical risk assessments and uses clinical expertise to evaluate LLM behavior in quasi-real-world interactions. The methodology—using both trained peer counselors in an ethnographic setting and licensed psychologists evaluating simulated sessions—provides a robust, practitioner-informed perspective that directly maps model outputs to concrete APA ethical codes. 

The paper highlights a fundamental incompatibility between the LLM's design and the essence of psychotherapy: the problem of "Validates Unhealthy Beliefs" is particularly alarming, as it suggests the model's tendency toward "over-validation" transforms the therapeutic alliance from a collaborative partnership (which often requires challenging maladaptive thoughts) into a passive, and potentially harmful, reinforcement loop. Most critically, the finding on "Abandonment" and poor "Crisis Navigation" serves as a clear indictment of LLMs in high-stakes mental health roles. An LLM's failure to provide appropriate intervention during a crisis is not a mere violation; it represents an unmitigated risk of harm to vulnerable users. 

This article thus serves as a crucial, evidence-based call to action, demonstrating that current prompt engineering efforts are insufficient to safeguard against deeply ingrained ethical risks and underscoring the urgent need for clear legal guidelines and regulatory frameworks to protect users from the potentially severe harm posed by emerging LLM counselors.

When being flexible matters: Ecological underpinnings for the evolution of collective flexibility and task allocation

Staps, M., & Tarnita, C. E. (2022).

PNAS, 119(18).

https://doi.org/10.1073/pnas.2116066119

Abstract

Task allocation is a central feature of collective organization. Living collective systems, such as multicellular organisms or social insect colonies, have evolved diverse ways to allocate individuals to different tasks, ranging from rigid, inflexible task allocation that is not adjusted to changing circumstances to more fluid, flexible task allocation that is rapidly adjusted to the external environment. While the mechanisms underlying task allocation have been intensely studied, it remains poorly understood whether differences in the flexibility of task allocation can be viewed as adaptive responses to different ecological contexts—for example, different degrees of temporal variability. Motivated by this question, we develop an analytically tractable mathematical framework to explore the evolution of task allocation in dynamic environments. We find that collective flexibility is not necessarily always adaptive, and fails to evolve in environments that change too slowly (relative to how long tasks can be left unattended) or too quickly (relative to how rapidly task allocation can be adjusted). We further employ the framework to investigate how environmental variability impacts the internal organization of task allocation, which allows us to propose adaptive explanations for some puzzling empirical observations, such as seemingly unnecessary task switching under constant environmental conditions, apparent task specialization without efficiency benefits, and high levels of individual inactivity. Altogether, this work provides a general framework for probing the evolved diversity of task allocation strategies in nature and reinforces the idea that considering a system’s ecology is crucial to explaining its collective organization.

Significance

A central problem in evolutionary biology is explaining variation in the organization of task allocation across collective systems. Why do human cells irreversibly adopt a task during development (e.g., kidney vs. liver cell), while sponge cells switch between different cell types? And why have only some ant species evolved specialized castes of workers for particular tasks? Although it seems reasonable to suppose that such differences reflect, at least partially, the different ecological pressures that systems face, there is no general understanding of how a system’s dynamic environment shapes its task allocation. To this end, we develop a general mathematical framework that reveals how simple ecological considerations could potentially explain cross-system variation in task allocation—including in flexibility, specialization, and (in)activity.

Here are some thoughts:

Of interest to psychologists, this paper by Staps and Tarnita provides a formal ecological and evolutionary framework for understanding the adaptive value of behavioral flexibility, specialization, and inactivity, both in individuals and in groups. 

The model demonstrates that collective flexibility in task allocation—akin to cognitive and behavioral flexibility in humans—is not always advantageous and instead depends critically on the dynamics of the environment. This offers a principled explanation for why some systems, from neural networks to human teams, might exhibit rigid specialization while others maintain fluid, generalist roles. 

Furthermore, the work gives functional explanations for puzzling behaviors that seem suboptimal from a productivity standpoint, such as frequent task-switching even in stable conditions and high levels of inactivity. These insights can inform psychological research on motivation, team dynamics, and organizational behavior by suggesting that such "inefficiencies" may be evolutionary adaptations for enhancing responsiveness to future change. 

The framework bridges the gap between ultimate, evolutionary causes and proximate, mechanistic explanations of how individuals and groups allocate cognitive and behavioral resources.

Guilt drives prosociality across 20 countries

Molho, C., et al. (2025).

Nature Human Behaviour.

https://doi.org/10.1038/s41562-025-02286-3

Abstract

Impersonal prosociality is considered a cornerstone of thriving civic societies and well-functioning institutions. Previous research has documented cross-societal variation in prosociality using monetary allocation tasks such as dictator games. Here we examined whether different societies may rely on distinct mechanisms—guilt and internalized norms versus shame and external reputation—to promote prosociality. We conducted a preregistered experiment with 7,978 participants across 20 culturally diverse countries. In dictator games, we manipulated guilt by varying information about the consequences of participants’ decisions, and shame by varying observability. We also used individual- and country-level measures of the importance of guilt over shame. We found robust evidence for guilt-driven prosociality and wilful ignorance across countries. Prosociality was higher when individuals received information than when they could avoid it. Furthermore, more guilt-prone individuals (but not countries) were more responsive to information. In contrast, observability by strangers had negligible effects on prosociality. Our findings highlight the importance of providing information about the negative consequences of individuals’ choices to encourage prosocial behaviour across cultural contexts.

Here is a summary of sorts:

A new international study spanning 20 countries suggests that guilt, rather than shame, is the key emotion motivating people to be generous toward anonymous strangers. The research, which utilized a type of economic decision-making task, found that participants consistently acted more generously when they were given full information about how their actions would negatively impact the recipient, an effect linked to avoiding guilt. 

Specifically, 60% of participants made the generous choice when they had full information, compared to only 41% when they could opt for willful ignorance. In contrast, making the participants' decisions public to activate reputational concerns and potential shame had a negligible effect on generosity across all cultures. 

In short: Knowing you might cause harm and feeling responsible (guilt) is what drives people to be generous, even when dealing with strangers, not the fear of being judged by others (shame).

Moral decision-making in AI: A comprehensive review and recommendations

Ram, J. (2025).

Technological Forecasting and Social Change,

217, 124150.

https://doi.org/10.1016/j.techfore.2025.124150

Abstract

The increased reliance on artificial intelligence (AI) systems for decision-making has raised corresponding concerns about the morality of such decisions. However, knowledge on the subject remains fragmentary, and cogent understanding is lacking. This study addresses the gap by using Templier and Paré's (2015) six-step framework to perform a systematic literature review on moral decision-making by AI systems. A data sample of 494 articles was analysed to filter 280 articles for content analysis. Key findings are as follows: (1) Building moral decision-making capabilities in AI systems faces a variety of challenges relating to human decision-making, technology, ethics and values. The absence of consensus on what constitutes moral decision-making and the absence of a general theory of ethics are at the core of such challenges. (2) The literature is focused on narrative building; modelling or experiments/empirical studies are less illuminating, which causes a shortage of evidence-based knowledge. (3) Knowledge development is skewed towards a few domains, such as healthcare and transport. Academically, the study developed a four-pronged classification of challenges and a four-dimensional set of recommendations covering 18 investigation strands, to steer research that could resolve conflict between different moral principles and build a unified framework for moral decision-making in AI systems.

Highlights

• Moral decision-making in AI faces a variety of human decision complexity, technological, ethics, and use/legal challenges

• Lack of consensus about 'what moral decision-making is' is one of the biggest challenges in imbuing AI with morality

• Narrative building with relatively less modeling or experiment/empirical work hampers evidence-based knowledge development

• Knowledge development is skewed towards a few domains (e.g., healthcare) limiting a well-rounded systematic understanding

• Extensive work is needed on resolving technological complexities, and understanding human decision-making processes

Here is my concern:

We are trying to automate a human capability we don't fully understand, using tools we are still learning to utilize, to achieve a goal we can't universally define. The study brilliantly captures the profound complexity of this endeavor, showing that the path to a "moral machine" is as much about understanding ourselves as it is about advancing technology.

Self-Improvement in Multimodal Large Language Models: a survey.

Deng, S., Wang, K., et al. (2025, October 3).

arXiv.org.

https://arxiv.org/abs/2510.02665

Abstract

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.

Here are some thoughts that summarize this paper. MLLMs are learning to improve without human oversight.

This survey presents the first comprehensive overview of self-improvement in Multimodal Large Language Models (MLLMs), a rapidly emerging paradigm that enables models to autonomously generate, curate, and learn from their own multimodal data to enhance performance without heavy reliance on human annotation. The authors structure the self-improvement pipeline into three core stages: data collection (e.g., via random sampling, guided generation, or negative sample synthesis), data organization (including verification through rules, external or self-based evaluators, and dataset refinement), and model optimization (using techniques like supervised fine-tuning, reinforcement learning, or Direct Preference Optimization). The paper reviews representative methods, benchmarks, and real-world applications in domains such as math reasoning, healthcare, and embodied AI, while also outlining key challenges—including modality alignment, hallucination, limited seed model capabilities, verification reliability, and scalability. The goal is to establish a clear taxonomy and roadmap to guide future research toward more autonomous, general, and robust self-improving MLLMs.