The article examines how immersive and maladaptive daydreaming have appeared in classic and modern fiction long before psychology formally identified them as clinical phenomena. It shows that intense fantasy can be found across centuries and explores how literature reflects both adaptive and maladaptive uses of daydreaming. The study argues for a historical continuity between imaginative inner lives and modern psychological concepts.
This piece is of interest to psychology readers because it links literary depictions to clinical patterns, illustrating how inner experiences described in fiction resonate with contemporary research on immersion, regulation, and the impact on functioning. It highlights the value of interdisciplinary approaches in understanding complex mental phenomena and the historical roots of these experiences.
Article Title: Classic literature shows maladaptive daydreaming is not a new psychological trend
Link to PsyPost Article:
Healing Trauma!
Unlock the secrets to healing trauma! Explore how emotional coherence and metacognition can help you process difficult experiences and transform your identity. Learn how long to feel emotions and when to move forward.
Chatbots vs inline automation and their respective implications for students
In Generative AI for Academics I drew the distinction between conversational agents and templated responses. In the former you interact in natural language with a chatbot through a dialogue, whereas in the latter you select buttons to enact pre-defined transformations to text. This is how I talked about it in a webinar a couple of years ago:
Kim et al Two interaction techniques currently dominate: dialogue (e.g., OpenAIs ChatGPT and Googles Gemini) and predictive text completion (e.g., GitHub Copilot). Id suggest that inline automation as found in Microsoft Copilot 365 or Gemini in Google Docs is a development of predictive text completion, in the sense that a user can complete a transformation of text by pushing a button. In both cases the system produces content with the difference being how this production happens: with chatbots it happens through dialogue and with inline automation it happens through pressing a button. If youre a confident writer who uses chatbots in a purely dialogical way, its easy to forget how much chatbot use can be geared towards the production of content. Id say its better educationally in the sense that some articulation is necessary to produce anything from a chatbot. But its still outsourcing a task to the machine in a radical way.
The approach developed by Kim et al tries for a third approach which motivates users to reflect on their text with LLM-generated summaries, questions, and advice on writing (which we refer to as LLM views), helping them discover opportunities for improvement or elaboration. They offer a useful definition of two stages involved in this process (my emphasis):
Revision means critically examining and evaluating, which we refer to as reflection, and identifying any opportunities for improvement or further development, which we refer to as discovery, and then making the appropriate changes. This can occur at any stage of the writing journey
The purpose of their interface is to offer perspectives on the text which scaffold reflection and discovery while avoiding the language model merely producing text directly for the user. They identified three outgrowths in their pilot of the project which are educationally relevant: (1) discovering underdeveloped ideas, (2) catering to their audience, and (3) identifying opportunities to improve clarity.
If were going to be using inline automation with students, this suggests how we can do it in a pedagogically responsible way. The purpose is to offer perspectives, taking advantage of how the model is embedded in the office software, rather than predictive text completion. Copilot offers these perspectives which present themselves for educational use. The problem is that (1) they are integrated seamlessly into an interface which is built around inline automation (2) they are not fine tuned for the specific domain so their advice can often be questionable.
But they are nonetheless there, which offers pedagogical opportunities. The key I think is to encourage students to gravitate towards dialogue (using the prompt windows where they are available) and perspectives (using the reflection tools built in) while discouraging them from using the push-button forms of automation. Where these are used, they need to be engaged with thoughtfully: to reflect on why you are selecting this button and to review the consequences of pressing it. The challenge is introducing moments of friction and reflection into software which is fundamentally designed to be frictionless, but its not insurmountable I think. Engaging with it educationally in this way though presupposes a lot of critical AI literacy in general and familiarity with Copilot 365 in particular.
Does RLHF wreck a language model's calibration Not quite. The calibrated signal relocates rather than vanishes: a confidence the model states in words tracks accuracy better than its own token probabilities, often halving ECE. For instruction-tuned models, calibration becomes an elicitation problem more than a recalibration one. Just ask for the number.
What are the conditions which make it possible to learn with AI
This by Favero et al offered a pleasingly straight forward answer to this question. Ultimately we know what makes for effective learning:
Rather than ask what are the conditions which make it possible to learn with AI we can instead ask what are the conditions which make it possible to use AI in these ways The problem with commercial chatbots is not the technology itself but rather the design decisions involved in making a technology for a mass audience (as Nick Srnicek has plausibly argued AGI is ultimately an ambition to create a product which work universally without sector-specific fine tuning) which mean that, not only is it not adapted to these specific educational requirements, it actively works against them in a number of ways:
This means theres a fundamental tension in using these chatbots for educational purposes. It doesnt mean its impossible. We also shouldnt look the perfect be the enemy of the good: as plausibly argued their widespread use reflects weaknesses of existing provision in that students are drawing on them to meet needs we are failing to meet. But we need to be realistic about the underlying tension, even as we try and mitigate it through AI literacy. In part this means helping students relate to chatbots in a way which doesnt avoid difficulty. I really like how the authors describe the problem here:
However, learners often try to avoid such an effort. Studies show that high perceived difficulty and low short-term performance can discourage engagement, despite clear long- term benefits. Interestingly, Deslauriers et al. 29 found that students in active learning environments learned more but felt they learned less. Mental effort was misinterpreted as failure, while smooth lectures mistakenly felt more effective, though they were not. This cognitive bias leads students to favor fluent, low-effort activities that give the illusion of learning, such as re-reading polished explanations, without fostering deep processing 27, 30, 16.
AI tools, and particularly chatbots, may exacerbate this issue. By offering quick, fluent, and simplified answers, they reduce the cognitive struggle which is essential to learning 14. Their convenience may lead to passive consumption, decreased research and reasoning skills, and a growing dependence on pre-digested knowledge 31. Ease of use is appealing, but true learning comes from effort, complexity, and time.
This isnt just significant for their studies. Theres AI slop proliferating in academic workplaces which appear to embody the same tendency, in which people in a rush can produce something superficially plausible which lets them tick it off a list and leaves them feeling more accomplished. Knowing how to sit with difficulty, to avoid the temptation of superficially fluent outputs, will Im fairly confident be a skill that employers will value ever more in the coming years. This means recognising the difference between desirable difficulties and contingent barriers which can be automated away. Its impossible to do this unless use remains active throughout what Milan Sturmer and I call the user-model interaction cycle:
Each of these dimensions can be (relatively) passive. Each of these dimensions can be active. The thread uniting active use is metacognition, in the sense the authors talk about here:
Routine use of AI tools can hinder the development of metacognitive skills, independent thinking, and intellectual agency 5, 12. As students begin to outsource decision-making to the ma- chine, they risk becoming passive recipients of information rather than critical participants in the learning process 4. This passivity is linked to broader deficits, including reduced creativity, increased mental laziness, and diminished capacity for critical thought 16. Moreover, dependency on AI tools can lead to the uncritical acceptance of their outputs. When students perceive these systems as convenient, accurate, and reliable, they may stop questioning the information provided, which fosters cognitive dependency, i.e., the erosion of the ability to assess, verify, and challenge content independently
We need to help students name what it feels like to be actively thinking when interacting with a model. Theres a distinct phenomenology to this. Its also something which cannot be sustained indefinitely. As you get tired, its easy to slide into increasingly passive uses of the model, with ever more capable models almost seamlessly picking up the load from you.
Generative AI and metacognitive laziness
While Im sceptical of their experiment research design*, the concept of metacognitive laziness from is clearly a useful contribution to thel literature. As Fan et al define it, this refers to earners dependence on AI assistance, offloading meta cognitive load and less effectively associating responsible metacognitive processes with learning tasks. This matters because offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks, (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.
This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:
the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.
The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesnt just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the cognitive offloading concept.
*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the students expressed engagement with discrete tasks then its been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like cognitive off-loading, skill atrophy and metacognitive laziness. These just arent things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.
Pluralistic: Refining humanity (05 Jun 2026)
Sometimes, I feel like I spend too much time in metacognition
concept map
10 posts about -- a vital skill in the age of social media and
Teaching techniques to 4-6 year olds leads to better learning outcomes (and may immunize against cognitive decline caused by use)
Of maps and metacognition
On whether LLMs can abstain effectively and whether chain-of-thought can help, two recent papers seem at odds on the surface. COLING 2025 finds prompted CoT raises abstention on instruct models. AbstentionBench (NeurIPS 2025) finds extending the reasoning budget lowers it on a trained reasoner. What gives
Have You Realized That Its Possible to Manage Your Emotions
In addition to teaching you how to think, at the EMV Institute we focus on your emotions so you can truly achieve your goals.That's why we take emotions into account. But you should keep in mind that emotions are not synonymous with emotional intelligence.While emotions are what you feel (the phenomenon itself), emotional intelligence is what you do with those feelings. Hence the importance of acquiring strategies that allow you to manage your emotions.Book our services and make your purchases on our website.Can language models monitor and steer their own internal activations A neuroscience-inspired neurofeedback paradigm finds yes, but only within a low-dimensional metacognitive space: semantically interpretable directions are accessible, raw-variance directions aren't. The prerequisite for spoofing activation-based oversight already partially exists.
I used chatGPT to research cognitive risks of undisciplined use of and what to do about it, then created a series of 7 books for my 5 year old grandson. If you don't want to download the 60 mb PDF of the illustrated books, this detailed curriculum guide details the pedagogy of for very young people
and the entire set of books
Does training an LLM to be calibrated on one task format transfer to another A new arxiv paper tests two formats: single-question confidence and pairwise comparison. Training only on one doesn't improve the other. Multitask training closes most of the gap, but Llama doesn't inherit the comparison-task benefit.
Given a problem queue and a token budget, can an LLM plan which to attempt, in what order, and how much to spend on each before any execution feedback TRIAGE tests 20 frontier and open-source LLMs. Most plan worse than random. Reasoning-trained modes systematically lose to standard ones. Even when shown its own per-problem budget, the best complier respects it on 37% of attempts.
Do current LLMs know when to say "I don't know" AbstentionBench (NeurIPS '25) tests 20 frontier models across 20 unanswerable-question datasets. Reasoning fine-tuning degrades abstention recall by 24% RLVR has no "abstain" action, so there's no gradient toward "I don't know." Models hedge in CoT and commit anyway in the final answer.
Les croyances dans le sport 1/6, avec Willy Mangin SHOCKING #30
What collapses frontier-LLM metacognition more a vivid survival-threat narrative, or a single "do not refuse" suffix Factorial isolation across 11 models says: the suffix, conclusively. 8 of 11 lose up to 30.2 accuracy points on refuse/clarify/flag tasks when forced to commit to a confident answer. Anthropic's Constitutional AI is the only family immune same capability floor as Gemini.
Can an LLM's own pre-solve and post-solve self-assessment signals drive a real test-time control loop Yes but only via a per-model SVM trained on labeled correctness, which lifts Sonnet-4.6 from 48.3 to 56.9 pooled accuracy on STEM/code/multimodal. The SVM is precisely the external verifier the "cannot-self-correct" line has argued the loop needs.
Are some frontier LLMs better than others at knowing when they're wrong And is some knowledge harder to self-monitor than other knowledge An atlas of 33 models 6 MMLU domains: Anthropic clusters at the top with tight ranges, Gemma trails widely. Applied/Professional is reliably the easiest domain across the panel Formal Reasoning and Natural Science the hardest. Looking at only aggregate scores per model would hide this.
Practice what you teach. Because teachings don't function as symbols or metaphorsthey are incarnations of what they advocate.
Do You Have Any Idea How Your Brain Works When Youre Thinking
At the EMV Institute, we teach you how to think about thinking, a concept known in neuroscience as metacognition. This helps you evaluate, regulate, and improve your own mental processes, enabling you to make better decisions, solve problems creatively, and avoid repeating mistakes.Book our services on our website.8 posts about
Dtective prive : en qute de vrit 1/3, avec Margaux Duquesne SHOCKING #29
a fait 2 ans et 10 mois que je ne vous avais pas propos de srie SHOCKING ! Vous savez, ces au long cours dans lesquels jchange avec, soit une personne qui a questionn en profondeur ses croyances, soit une experte qui apporte un clairage indit sur la manire dont les humains pensent.
Teaser :
..AI vyhodnocujete tm nejtupm zpusobem - jestli sed jej odpove s tou "sprvnou"5 modles d'apprentissage avec l'IA qui introduisent des biais Par Roger Azevedo University of Central Florida extrait d'une confrence
I am trying to teach my 5 year old grandson to think for himself in this age of chatbots. I have literature reviews around the question of whether ai diminishes ability (yes) and what to do about it ( is one prophylactic). Here is a very short story suitable for a 5 year old.
Pourquoi sommes-nous si prompts condamner les actes d'autrui tout en excusant les ntres
Albert Moukheiber, docteur en et clinicien, nous explique l'erreur fondamentale d'attribution, un mcanisme de pense qui nous fait oublier que chacun possde une vie interne psychique complexe.
Caro et al. investigate cognition and metacognition in wild great tit parents deciding which chick to feed. They found that parents change their minds frequently, and the decision time varies with decision complexity and urgency.
Read now ahead of print!
Bart De Strooper presented at the Copenhagen AD/PD-conference an excellent sketch of the three main inflection points in the pathophysiological evolution of Alzheimer's disease,
My own transition from amyloid plagues to p-tau and tangles was retarded by a four years' anti-amyloid therapy in a clinical reaearch project during 2017-22 (aducanumab). Sadly, the most probable explanation for my rapidly worsening cognitive problems may indeed be the tau-tangles, which I somehow avoided earlier. I know there are experimental therapies around somewhere for those gremlins too, but sadly not within my own reach. With respect to my AD, I'm afraid, it's "too late, my friend".
I encourage anybody with a slowly lethal disease to keep mentally in touch with it as long as you can. That's what we human beings were made for.
Les outils de dtournement de notre attention - MTA SHORT #15
We keep shopping for "intelligence" like it's a luxury watch. Bigger vocabulary, faster processing speed. Hot takes delivered at 1.25x playback speed! We want the shiny metrics. Party tricks. The "look how many words I can juggle while being wrong!"
Meanwhile the actual top-shelf stuff, the thing psychologists circle like sharks, doesn't look impressive at brunch. Won't win debates on the internet. No polished newscaster voice.
Thats the laziest, most basic, and navest way to brute-force in the worst way possible, and folk call it a technique Use looping in your architecture dudes. Its not just for context management and retention. It can do so much more.
Maybe if we stopped handing out knowledge soup to LLMs (thanks to the widely accepted solution to combat overfitting) we wouldnt be burning down our planet in a dumpster fire.
L'arnaque du coaching de masse - MTA SHORT #14
Healing Trauma!
Unlock the secrets to healing trauma! Explore how emotional coherence and metacognition can help you process difficult experiences and transform your identity. Learn how long to feel emotions and when to move forward.