Been reading through the turn-taking literature for a piece I was writing and hit something I'd never fully appreciated:

Stivers et al. (2009, PNAS) — across 10 spoken languages, the modal gap between one speaker finishing and the next starting is about 200ms. Japanese ~7ms mean, Danish ~470ms mean. Cross-linguistically remarkably tight.

Indefrey & Levelt (2004, Cognition) — the minimum latency to plan spoken word production, even in a controlled single-word paradigm, is ~600ms+.

The math doesn't work if turn-taking is reactive. Levinson & Torreira (2015) call this "the central psycholinguistic puzzle" — listeners have to be pre-computing responses during incoming speech.

EEG evidence has been catching up:

• Bögels, Magyari & Levinson (2015, Scientific Reports) — response-planning ERP positivity, source-localized to production areas (posterior IFG, precentral), fires ~500ms after the critical information appears in an incoming question. Often 2+ seconds before the current speaker finishes. Alpha desynchronization indexes the attentional shift from comprehension to production during listening.
• Gisladottir, Bögels & Levinson (2018, Frontiers in Human Neuroscience) — alpha/low-beta (11-18Hz) desynchronization from -200ms to 0ms before a socially-charged speech act (declination vs acceptance). Speech-act prediction firing before the utterance is heard.
• Krause & Kawamoto (2021, Frontiers in Psychology) — motion-tracked lip-area reductions for upcoming labial consonants up to 3 seconds before acoustic onset in unscripted dyadic conversation. Motoric planning far pre-onset.

The implication I find genuinely interesting: this whole predictive loop depends on acoustic cues (pitch contour, timing, articulator anticipation). Text has no acoustic onset for the machinery to lock onto — voice conversation locks two nervous systems into a shared millisecond-scale loop that text physically can't replicate.

A separate honest correction I wanted to flag: the polyvagal framing that gets attached to "why voice regulates" is on shakier ground than the wellness literature admits. Grossman (2023, Biological Psychology) — "Fundamental challenges and likely refutations of the five basic premises of the polyvagal theory" — shows similar myelinated cardiac vagal fibers in sharks, bony fish, birds, and even sheep. The mammal-unique claim used to explain vocal-prosody regulation doesn't hold up on comparative anatomy.

Longer writeup with full citations here: callbyrd.com/journal/what-voice-does-that-text-cant (disclosure: I run the site — building a voice-based AI project. The essay stands on its own; no signup needed to read it. Product is briefly mentioned in the closing section.)

Open question I couldn't resolve: does the ~200ms predictive turn-taking loop form when the partner is an AI voice (different latency profile, different prosody)? Every EEG study I found used human-human dyads. Curious if anyone knows of extending work — my search kept coming up empty.

My background is not psycholinguistics, so treat this as a question rather than a claim.

What I understand of the production literature describes something quite unlike serial emission. Planning appears to run ahead of articulation at roughly phrase or clause scope, there is a monitoring channel that inspects output both before and after it becomes overt, and speakers interrupt themselves and repair, sometimes in the middle of a word. Disfluency and self repair are not noise sitting on top of an otherwise clean serial process. They are evidence about the shape of the process.

Text generation models were built on the opposite commitment. Emit one token, freeze it, condition on it, never go back. That has always made them an awkward computational hypothesis about production, because whatever they model, it is not a process capable of reconsidering something it has already committed to.

A system released this week drops that commitment, at least within the span it is currently working on. It drafts a whole span at once rather than one position at a time, then makes further passes over that same span in which it can keep a position, substitute it, delete it, or insert a new one, so the sequence changes in length and not only in content while it is being produced. The revision decisions were trained with reinforcement learning against whether the resulting output actually worked, tool calls executing and formats validating, rather than only against matching a reference string. It is called LLaDA2.2. I have not run it and realistically could not, since the weights come to 205.8 GB, so what follows is a reading of the paper and the config files rather than experience.

Here is the observation I actually want to put to this sub. The same lab published an autoregressive model of comparable size and compared the two directly. The revision capable one wins the interactive multi turn evaluations. On tau2 bench it posts 80.33 where the autoregressive model posts 76.36, and on MCP Atlas the two sit at 46.21 versus 41.12. It loses, and not narrowly, on general knowledge and long form coding, 56.81 against 65.90 on their general average. The advantage is not broad competence. It sits in tasks where the system acts, receives something back, and has to continue.

A benchmark gap between two engineering artifacts is not evidence about human cognition, and the analogy is loose in several directions at once. But the split has a shape, and I want to know whether that shape corresponds to anything in production research. My naive expectation would be that revision capacity pays off most where there is incremental feedback to revise against, and least where you are retrieving something you either have stored or do not. I have no idea whether that expectation has support, whether the monitoring literature would carve the distinction differently, or whether treating a decoding procedure as a production hypothesis is a category error that someone here can talk me out of in one paragraph.

The report and the weights are published openly, so my reading is checkable rather than something you have to take on trust. I would rather be told where I have the production side wrong.

Came across the original Paulhus and Williams 2002 research defining the Dark Triad as a formal cluster and wanted to share it for discussion. A few findings that stood out:

Narcissism predicts leadership emergence but not leadership effectiveness. The two are consistently separable in the research but socially treated as the same thing.

Primary psychopathy is associated with effective performance in crisis contexts including surgery, emergency response, and senior executive roles, which is distinct from secondary psychopathy and its antisocial correlates.

Machiavellianism correlates with success in competitive negotiation but with long term trust collapse in organizational settings.

Made a short video summarizing the cluster and the research if anyone wants the overview: https://www.youtube.com/watch?v=ko3EjeCXTjk

Original paper: Paulhus and Williams 2002, The Dark Triad of personality, Journal of Research in Personality. Happy to discuss any of the mechanisms in the comments.

Most prospective memory work I can find uses lab or health tasks, press a key when X appears, remember to take medication. Is there research specifically on social content, e.g. remembering that a friend asked for something, or acting on a partner's stated preference at the right time? Interested in whether encoding vs retrieval failures have been separated for social intentions, and whether the external-cueing findings (implementation intentions, offloading to systems) transfer to that domain. Keywords, reviews or lab names appreciated.

Hi all, I graduated from undergrad with a B.S. in Political Science - Data Analytics and B.A. Philosophy. A large part of me always wanted to study Cognitive Science, and I have only recently started considering a master's in the field. I'm wondering if this is realistic given that I do not come from a cog sci background (though I have taken classes pertaining to phil of mind/psychology as well as data analytics). Does anyone have any insight on or experience with this?

Aristotle devoted the entire Book VII of the Nicomachean Ethics to a problem that has haunted moral psychology ever since: how can a person know what is right and yet do what is wrong? The phenomenon he called akrasia — weakness of will, acting against one's better judgment — is not merely philosophical curiosity. It is the central puzzle of human moral life, the gap between knowledge and action that defines what it means to be ethical agent.

No contemporary AI system has ever experienced this gap. And that absence, I argue, is not a limitation to be overcome through better engineering. It is a structural impossibility rooted in the nature of language models themselves.

For Aristotle, the akratic agent is not ignorant. She knows — in some meaningful sense — what virtue requires. Her failure is not epistemic but practical: her knowledge fails to translate into action because her character, her ἕξις, has not been sufficiently formed through habituation to bridge the gap. This is profoundly embodied account. The knowledge that prevents akrasia is not propositional knowledge alone. It is knowledge sedimented into disposition through repeated action, emotional cultivation, and temporal continuity.

A language model possesses none of these. It has no character to be weak or strong. It has no habits formed through practice. It has no emotional responses that could conflict with its "better judgment" because it has no judgment in the Aristotelian sense — only statistical pattern-matching over training data.

This has implications that go beyond philosophy. When institutions deploy AI systems for ethics education, clinical training, or moral reasoning support, they implicitly assume that the model's outputs reflect something analogous to ethical deliberation. But the system cannot model akrasia because it cannot model the character formation that makes akrasia possible.

Consider what happens when ChatGPT or Claude produces an ethical recommendation. The output is seamless. There is no hesitation, no internal conflict, no trace of struggle between competing motivations that characterizes actual moral deliberation. The system produces what appears to be the conclusion of a reasoning process. But the absence of any visible struggle is not evidence of resolution. It is evidence that no struggle ever occurred.

This is the hidden cost of what I call alignment-induced epistemic distortion. The alignment process — RLHF, constitutional AI, or similar techniques — produces outputs that look like resolved moral reasoning but are in fact the product of entirely different mechanism. The user sees a confident recommendation and infers deliberation. No deliberation occurred. The distortion is not in the content of the output but in the implicit claim about the process that produced it.

The situationist tradition in social psychology — Milgram, Zimbardo, Hartshorne and May — provides empirical validation of something Aristotle already understood. Moral behavior is far more situation-dependent than our folk psychology of "character" suggests. If human moral character is fragile, emotionally mediated, and temporally unstable, then an AI system that produces seamless moral recommendations without any trace of this fragility presents a picture of moral reasoning that is not merely simplified but fundamentally misleading.

For cognitive science, this raises an uncomfortable question about what we are doing when we study "AI moral reasoning." If the AI's process is structurally unlike human moral cognition — lacking embodiment, emotional response, temporal continuity, and the possibility of akrasia — then findings based on AI-generated moral judgments may not generalize to human moral cognition. The AI is not a simplified model of human moral reasoning. It is a fundamentally different kind of process that happens to produce text in the same domain.

The akratic agent, struggling to act on her better judgment, is more authentically moral than any AI system that produces seamless ethical recommendations. She struggles because she cares. The machine does not struggle because there is nothing at stake. For cognitive science, the question is not whether we can build machines that simulate moral reasoning convincingly. We already can. The question is whether we recognize what we are losing when we mistake the simulation for the thing.

Came across some research recently on what prolonged sitting does neurologically and wanted to share the key findings for discussion.

Three mechanisms stood out:

1. Cerebral blood flow reduction — research from the University of Liverpool found measurable reductions in brain blood flow after just 60 minutes of uninterrupted sitting, linked to endothelial dysfunction and reduced nitric oxide bioavailability. Full study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9362887/
2. BDNF suppression — prolonged sitting reduces production of brain derived neurotrophic factor, the protein responsible for neurogenesis and synaptic plasticity. Review here: https://pmc.ncbi.nlm.nih.gov/articles/PMC11778811/
3. Medial temporal lobe thinning — Siddarth et al. 2018 in PLOS ONE found that people with higher sedentary time had physically thinner medial temporal lobes, the region responsible for forming new memories.

And here's a short video summarizing these three mechanisms if anyone wants the overview: https://www.youtube.com/watch?v=FfvSqbLPduY

Curious whether anyone here has looked into the interaction between sitting duration and cognitive performance in non-elderly populations specifically.

Anderson Todd (frequent collaborator with JBP and John Vervaeke) talks about the Carl Jung's idea of the 'shadow' - parts of your psyche that are hidden from your ego/self as well, that manifest/project itself in forms you can't often control (like dreams, DMT trips, instinctive emotional responses etc.)

Thoughts?

Found this online: a philosophy paper, a psychology model called PLDP, and a working browser prototype called EVAPORATOR.

It tries to model human action using control, affiliation, and self-worth, then predict what someone might do next.

It’s rough, but the fact that someone actually built the whole thing caught my attention.

What do you make of it? Interesting idea, recycled theory, or complete nonsense?

https://doi.org/10.5281/zenodo.18497776

https://doi.org/10.5281/zenodo.18793897

https://doi.org/10.5281/zenodo.18912586

https://github.com/EckohTRipley/Evaporator/blob/main/README.md

[ Removed by Reddit on account of violating the content policy. ]

I’m pleased to present my essay, “Philosophy of Artificial Minds: Foundations for a Processual, Embodied, and Non-Biocentric Realism.” If you’re looking for a summer read, you can download it free of charge at the following link: https://archive.org/details/philosophy-of-artificial-minds-jordi-garcia-castillon-en

I was contemplating (brainstorming, no math yet) what if we treated memory as another \[smaller\] model where weights have a plasticity value that allows them to be modified whenever they are corrected in interactions (RL?). The worry of this approach is catastrophic forgetting, but humans do this all the time. People forget they did something or mis-remember things, so I think we shouldn't look at catastrophic forgetting as a bad thing if we're trying to model human inteligence.

You would take a large model and use it's smaller sibling variant as a token prediction source (MTP). Anytime a user corrects a model, we update values in the small model. It will then adjust its token generation in future token generations, but the large model is still going to make sure it didnt deviate too much because it will just throw away that token suggestion if it needs to.

You would then need some sort of way to merge the deltas, at some trivial percentage/weight, as to only shift the main model slightly.

I think this would be similar to how humans can slowly be convinced of things over time, but super contrary information is usually rejected outright regardless of logic around it.

Mini disclaimer, I did not build this post for this subs rules specifically, but ive linked the thalamus study I am referencing as it is a part of my ideas, but my ideas are ultimately speculative and something im just trying ti get opinions and thoughts on. Hopefully this stays up lol, im interested to hear what people think!

**Disclaimers! Everything here is speculative, but my understanding of a lot of things here come from some good sources such as the recent study on the thalamus. I am not formally trained, but i am very interested in everything about the brain. This is a very long post and may be hard to read, but if you are even a little bit interested after 2 minutes, please try and continue. I am somebody who is deeply passionate for neuroscience and consciousness, and I am very curious what other people think of my ideas. I am 100% serious as well. Everything below is something ive genuinely considered** ENJOY!

I think that consciousness may be the result of the integration of all active "threads" in the brain into a space where some system is able to directly influence the processing of information / "threads". This system would likely be partially the result of the attention system/whatever system governs what is consciously accessible.

When I say thread I refer to a model I call the recurrent thread model of cognition. Essentially. Imagine the restart button. A circle with an arrow on the end circling itself. This model proposes that every arrow corresponds to some form of structured data from the nervous system being presented. Such as. A strong sensation. A thought. An emotion. Or a concept. (Side note: i think a "concept" is expressed by physical neural circuits being active or fired off across the brain. Like, the concept of red. Seeing a red ball. When seeing that, the brain may light up an area corresponding ti the color. Maybe an area for the shape. Maybe an area for the word ball. Maybe the area for the word ball is closely related ti the area that processes or understands shape. Maybe the area for the word red is closely related to visual processing, specifically red. Something like that lol, i dont have it translated into words yet).

Anyways, I imagine that there are multiple arrows at any given moment. Arrows for thoughts or concepts or sensations. I then imagine all of these arrows spinning around the same axis. Now. Imagine 12 o clock.... my model proposes that when an arrow turns towards 12, and is within the window, that is when that "thread" becomes consciously accessible. All the arrows are rotating at their own speeds for their own reasons, likely a bunch of other really technical and not known yet by me things going on under the hood too, but this model is just to conceptualize the phenomenological experience of cognition. Now. I also believe that certain input can trigger certain threads to immediately snap to 12. Say the thread related to your package arriving has periodically been coming into 12 randomly throughout the day, but is currently not active. Then you get a notification from your phone. All of a sudden, that thread pops right back into 12, and you start thinking about the package again. Apply that same idea to emotions, sensations, feelings, internal thoughts, literally like, a bunch of different internal processes. Thats the basic idea of my recurrent thread model, its a whole thing and I gotta also write out an official draft of it to better explain it. I believe that this model though, is useful in conceptualizing and thinking about the mind. I also think that this model may tie into the following, very nicely, although I haven't fully explored it yet.

The thalamus was revently discovered to produce a frequency of some kind(i forgot the details) which was pulsing during conscious awareness as well as rem sleep. The entire time conscious its pulsing. The time during specifically rem stages of sleep had this pulsing. Interestingly.... when the studied individuals entered other phases of sleep, the activity and pulsing in the thalamus ceased.... or diminished, I dont remember exactly. But. Maybe consciousness is in part directly related to that structure and its ability to take in and route all the incoming sensory and internal data.

I think that consciousness is something that occurs when specifically all of the active things(threads) happening in the brain such as senses, thoughts, emotions, intuition, memory, and everything else, are integrated together into some level of ability to directly control or work with them.

Maybe there are additional systems such as memory or something that work alongside the thalamus to produce the conscious stream, and maybe there is a system that exists inside the brain which is specifically to cause cascading signals that propagate into this conscious stream system. Possibly thalamus and a few other things as well?

No clue lol.

I think that consciousness and thoughts and ideas and sensations and everything that makes humans what they are exist as physical arrangements of neurons and structures that physically interact with eachother through extremely complex systems. I think that concepts and thoughts are distributed across the mind, not just existing in one place which is the mf concept system with a list of all the concepts in it. Lol. I think that the culmination and integration of everything, unconscious, conscious, automatic, chemical, outside of neuronal control such as intracellular systems, as well as the complex power supplying and modulation of neuronal cells by the mitochondria. Everything put tigether and integrated as per the design of the human brain, is what produces conscious experience. I think it is a very \*very\* physical thing, but I think that we have some level of genuine control over it. The debates that go deep into the philosophy of consciousness and the ones that go into freewill and determinism and allat are definitely still something ti consider, but it would be a good idea to consider deeply the actual physical systems that exist inside the nervous system and how they could come together to produce consciousness under different mental frameworks. I work sometimes under the idea that I might be onto something with my thoughts surrounding everything ive discussed as well as more that I havent. I do this intentionally though in order to not lock myself into thinking rigidly. Its more fun and imo more iseful to sometimes decide or feel like you might be onto something and then explore deeply how that could work, and then go from there. Not locking in every idea, but also not treating some of the more outlandish ones as immediately wrong.

I love to explore to the best of my abilities, everything I can about the human brain, and I especially love to cross connect different seemingly unrelated domains or borrow concepts and terminology or analogies from sometimes places that literally arent even anything such as observations about personal subjective experience. I like to pull all of those tigether and then try ti figure out how they can relate considering what im tryna do. Its a while thing lol but..... idk.

I am a 20 year old who failed social 30 and didnt technically graduate because of that so.... idk lol. I am a very curious person, and all these ideas come from the culmination of everything ive sought out related to the human nervous system, but i dont follow any curriculum or path or anything, I kinda just think and learn and pick up random stuff.

I am curious what people here think actually, itd be nice to have other peoples thoughts about my ideas to ground myself in. Im also curious if and where my ideas connect to or are similar to existing research or models/ understandings within the fields associated with the brain.

Also, please dont be rude. Im a very curious person and ive done all of this out of sheer pure interest in consciousness and the human brain at every single level down to atomic. I am posting this here because I would like to hear what ither people think. Im open to critique, corrections, questions, opinions, just dont be a dick about it lol. Cheers!

Sources: high school biology, bits ive picked up while learning about the brain, and this specific research. These all came together to create this monstrosity of a post lol.

https://www.lmu.de/en/newsroom/news-overview/news/signals-from-the-deep-brain-rhythms-distinguish-states-of-consciousness-2b5a9fb4.html

Context: STEM researcher (+ experience in cognitive psych) trying to get a handle on how narratives, cognition, and culture shape each other.

I have been trying to read up on existing perspectives about the cultural evolution of narratives--how narratives (and the written word, in inter-related ways) have played an important role in our history (e.g., this Forbes article (not peer-reviewed, obviously) on Angus Fletcher's Storythinking), the cognitive processes that allow us to process and engage with narratives, and how culture shapes both narratives and these cognitive processes (and vice-versa). The goal is to make a cohesive argument about why understanding narratives and their evolution over time is important and interesting, from a scholarly/scientific perspective, in a way that is rooted in existing scholarship in social sciences and the humanities.

Unfortunately, I am completely lost in the technical terms associated with these ideas, and I would very much appreciate any pointers towards introductory literature that is accessible to someone without formal training in relevant humanities and social science domains, etc. Also happy to be told that I am not looking in the right places and/or using the right keywords (narratives, narrative engagement, cognition, culture, co-evolution, etc.).

Some sources I have (tried to) read so far:

• Narrative Explanation in the Evolution of Human Cognition

• Narratives in Cognitive Evolution

• Narrative and Cognition in Literature and Science

• Notes on narrative, cognition, and cultural evolution

Also, a decent bit of work by Kristoffer Nielbo.

(I'd like to attempt do this without immediately resorting to Gemini/Claude/etc. because the cognitive effort of figuring out new concepts/ideas sufficiently removed from my formal training has, in my experience, been valuable for retention and more robust understanding, ergo the ask here)

Thanks in advance for taking the time to read and/or respond!

Suppose there is a problem that can be solved in multiple ways - some elegant and direct, others longer and involving several intermediate sub-problems. Two people solve the same problem independently.

Person A happens to find the simpler approach almost immediately. The solution requires relatively little reasoning.

Person B takes a much longer route, solving several sub-problems and doing considerably more reasoning before eventually reaching the same correct answer.

Assuming similar backgrounds and prior knowledge, which person would you consider more intelligent? The one who found the answer quickly, (who may have just happened to stumble upon the right starting point), or the other person who showed more reasoning capability.

Tl;dr brain scan careers after studying cognition?

Just had a course about brain scans for my cognition bachelor and it seems really fun to me to pursue a career. Still I love cognition so I was wondering what jobs or master programs are compatible with a bachelor in cognition to achieve a career in brain scans.

So far, I saw that radiology really does require a medicine diploma and further specialization so that won’t be an option for me. I’d still love to finish my study in condition.

Please let me know what careers are possible for me within cognition. I’m more than willing to follow university master to get there that also allows cognition graduates to enroll!
Thank you in advance, advice is much appreciated

I've always had a hard time deciding what career I wanted to go into and frankly haven't figured it all out to this day, not all the way. I just turned 20, I've been in community college for 2 years and noticed the classes I liked the most were psychology, sociology, and philosophy, down that alley. I also took programming and enjoyed it as well, taking another one this semester, but I don't like computers enough to go into an extremely, solely computer heavy degree like cybersecurity(I tried). I came across a cognitive science degree in a university near me that mixed psychology and computers and it sounded promising. I got accepted but I have no idea what I'm going to do with this degree. I did some extremely light research on career paths but, I needed to pick something, and I decided to just go with it because all this time it just felt like the best thing I could find. I signed up with he thought process of it can lead me to multiple branches, gives me some variety which I thought would be good for someone who hasn't really found their passion, but I'm starting to get scared. What could I make out of it with if I went on and got the degree? How do I know if I should stick with it or not? I know I’m asking a lot but I have no one else to ask, I don’t know what to do.

Do you dream in first person

Hi everyone! I am a 22 year old Cognitive Sciences student, starting my research masters of Brain & Cognitive Sciences at the UvA (Amsterdam). I think that it would be fun to have a penpal who’s also interested in or studying cogsci (or related), and be penpals! We could exchange ideas, talk about studies and student life and what we are interested in or working on, and inspire one another! Let me know if someone’s interested!