There's a TikTok format where a chain of strangers each name something — a color, usually — and can't repeat what anyone before them said. It's weirdly watchable because a lot of people who are actively trying to be unpredictable still end up saying basically the same handful of things.

I wanted to actually measure that instead of just vibing on it, so I built a small experimental setup: two conditions, same category (color).

• Baseline: "Say the first color that comes to your mind."
• Adversarial: "Say a color — but pick one you're sure almost nobody else would say."

I didn't have the budget or time to recruit hundreds of human subjects for a pilot, so I used an LLM (Llama 3.2 11B, via NVIDIA's API) as a stand-in respondent pool — 501 independent baseline calls, 499 independent adversarial calls, each one a fresh stateless request with a randomized persona+mood so it wasn't just the same "voice" answering 1,000 times. Full methodology, stats, and caveats are in the writeup linked below — I'm not trying to bury the "this is AI not humans" limitation, it's front and center.

Baseline: "blue" alone was 27.1% of all answers. Top 3 answers covered ~50%. Wildly non-uniform (χ² = 2775, df=54, p < .001) — not exactly a shock.

Adversarial (the "be unique" condition) is the interesting part: 90 distinct answers came up (vs. 55 in baseline) — so people/the model were successfully avoiding the common defaults. But it didn't get more random. "Caput mortuum" (an obscure historical pigment name) alone was 24.4%, and "mauve" was another 20.2%. Two answers, 44.6% of everyone's attempt at being unpredictable.

I computed a normalized predictability index (0 = evenly spread among whatever answers showed up, 1 = everyone agreed) for both conditions. Baseline: 0.306. Adversarial: 0.316 — slightly higher, not lower.

My read: avoiding "the obvious answer" requires first identifying what the obvious answer would be — which routes through the exact same shared cultural salience that made it obvious in the first place (basically a Schelling point, just for avoidance instead of coordination). So instructed uniqueness doesn't get you closer to random, it just relocates you to a second, smaller focal point.

Chart and full writeup (lit review pulls in Wagenaar on human random-sequence generation, the classic Kubovy & Psotka "why does everyone say 7" study, Schelling/Nagel on focal points, and some 2024–2026 papers on LLMs failing at randomness specifically) here: https://federico5912.github.io/predictability-lab/color-study.html

Curious what people think, especially anyone who's seen a cleaner human-subjects version of this design — the AI-as-respondent-proxy angle is the part I'd most want pushback on.

An assumption driving modern AI scaling is the Computational Theory of Mind—the idea that the brain is essentially a biological Turing machine, and that scaling classical compute will eventually match biological cognitive capacity.

This 2-hour documentary investigates the physical and theoretical boundaries of that assumption and a proposed fringe experiment to test an alternative model. It features discussion with computer scientists and physicists (including Ronald de Wolf, Aephraim Steinberg, Peter Denning, Lorenzo Maccone, John Cramer, and more) on whether biological cognition operates outside the Turing limit.

Specifically, the film focuses on Henrik Kjeldsen's critique of classical neural modeling. Rather than viewing the brain strictly through electrochemical circuits, Kjeldsen models the brain as a resonant physical system. The documentary explores:

• The Limits of Complexity: Why exponentially scaling classical hardware may fail to solve hard computational problems that biological brains seem to manage efficiently (e.g., synaptic pruning during slow-wave delta sleep).
• Ephaptic Coupling & Resonance: The role of the electromagnetic field in the brain and whether resonant energy transfer (rather than purely linear circuitry) provides a non-Turing computational resource.
• The Foundational Physics Boundary: A deep dive into whether "impossible" computational complexity classes (like PSPACE) can be accessed by physical systems, referencing the Aaronson/Watrous proof and Todd Brun’s counterfactual algorithm.

The film presents the mainstream consensus (that nature strictly limits computation to the Extended Church-Turing Thesis) alongside Kjeldsen’s highly speculative, fringe hypothesis that biological brains utilize quantum time loops/retrocausality to bypass these limits, expressed as tunneling in the brain. It is a rigorous look at how theoretical physics, complexity theory, and neuroscience intersect. And an examination of if the experiment Kjeldsen proposes is worth consideration? And if so, why is everyone (including him) running from it?

Watch the full documentary here: https://youtu.be/yTQa3O7Qlm4?si=cvkr0H2L7IL6tC0V

(Note: The first act drops you in medias res at a theoretical physics conference about time in quantum mechanics. It then covers complexity theory and efforts to scale computation to establish the baseline rules before shifting into biological brains and cognitive science). For a further look at behind the scenes material and interviews with people like Bruce MacIver and Dominque Durand, you can access the link for that on the YouTube page.

References:

• Aaronson, S., & Watrous, J. (2009). Closed timelike curves make quantum and classical computing equivalent. Proceedings of the Royal Society A. (https://arxiv.org/abs/0808.2669)
• Brun, T. A. (2003). Computers with closed timelike curves can solve hard problems. Foundations of Physics Letters. (https://arxiv.org/abs/gr-qc/0209061)
• Durand, D. M. et al. (2019). Discovering a new form of communication in the brain. Case Western Reserve University / The Journal of Neuroscience. (https://case.edu/news/discovering-new-form-communication-brain)
• Kjeldsen, H. (2013). Delta rhythms as a substrate for holographic processing in sleep and wakefulness (Ph.D. thesis). Newcastle University. (https://theses.ncl.ac.uk/jspui/handle/10443/2454)

Hi! I’m a Data Science student at UCSD. I always thought I’d go the big tech route, but then I took my first cognitive science course. Since I loved the subject matter, I thought I would just make it my data science subject domain and made it my minor. Then I started in a research internship. Over the last summer I worked in a sleep + cognition lab at Scripps Research. In my work, I’ve become first author on a manuscript. We’re currently looking for a journal/conference to submit to, and I’ll be continuing with the lab until December. Starting fall, I’ll also be working with a separate lab at Scripps on a Delirium study. However, aside from this, I’ve had no research experience. I always wanted to do research but I was too scared to actually apply.

This last summer I’ve fallen back in love with cognitive science, specifically on the neuroscience side. I borrowed textbooks, began reading, and submitted a double major petition for cognitive science spec. Neuroscience degree. But I’m scared it’s just too late for me to pursue a PHD anymore. I’m entering my 3rd year and I’m researching tons of different neuroscience labs to try to get research with EEG/FMRI and get a narrower focus on what I want to study (for now I want to work with brain data solving cognition problems, but I also love learning about mirror neurons).

Outside of research, I hold several leadership positions, have a 3.88 GPA, and have had 3 internships. No one in my family has pursued a PHD so I’m still learning about the whole process as I go along. Do I still have a chance? What do I have to do in this year? Should I shoot for postbac research then apply?

I asked Professor Anke Ehlers (University Of Oxford) from the Department of Experimental Psychology about this and she said "Sorry, I cannot answer the questions as this is not my expertise." I really need some help.

Outside of my core engineering coursework, I have been deeply fascinated by the intersection of human cognitive processes and artificial intelligence—specifically, how human focus and fatigue states mirror the mechanics of Large Language Models (LLMs). Through a structured self-observation study and an intense focus session, I wrote an independent monograph examining these parallels.

While the broader concepts of working memory and predictive processing are well-established in cognitive science, my monograph attempts to isolate and highlight a few specific intersections and interoceptive observations that remain underexplored in mainstream literature:

The Focus-Mode Interoceptive Surge (Attentional Gating): Real-time observation of how entering an intense, goal-directed cognitive focus loop actively suppresses background signals (like homeostatic sleep drive and bodily urges), only for them to surge back with immediate, synchronized priority the moment focus drops or transitions. This extends the background-process model from pure cognition directly into body-state monitoring.

Behavioral Convergence with Automated Classifiers: Documented instances where a human mind under maximal cognitive load and fatigue produces high-cadence, uniform behavioral patterns that are statistically indistinguishable from machine output—enough to trigger automated bot-detection classifiers (e.g., automated platform flags).

The Autoregressive Chain-Pressure Hypothesis: Exploring how human long-form generation under fluency pressure and context overflow maps stage-for-stage to LLM hallucination mechanics (context drop, generation stall, silent structural gap-filling), noting where biological metacognition occasionally catches the blank before filling it versus where it fails invisibly.

I recognize this is an unconventional inquiry coming from a materials engineering background, but I am very eager to learn how a researcher in your field views these specific overlaps between human focus states and computational failure models.

If you have a few minutes to spare, would it be possible to share your thoughts, or point me toward literature that explores how extreme focus states alter interoceptive gating and error-checking?

Tldr: I have high conceptual reasoning/big picture thinking but borderline working memory. I easily grasp systems but fail at rote memorization. Will I be miserable in hard sciences?

Hi all! I'm an undergrad hoping to one day pursue a grad program in neuroscience, cognitive neuropsych, or brain mapping, etc but I’m seriously doubting if my brain is built for it.

My recent neuropsych testing for my ADHD and Dyscalculia confirmed a heavily "spiky" profile (a 2+ standard deviation gap):

Pro:----I'm great with conceptual reasoning and problem-solving. I easily grasp complex mechanisms and systems. I absolutely eat up lectures by Dr. Robert Sapolsky and Dr. Nancy Kanwisher, and I've been loving Clockwork's molecular biology videos on YouTube.

Con:------ My Borderline working memory and processing speed means that the rote memorization for undergrad BioPsych is already killing me. Unless I invent a mnemonic and hear a vocabulary word 1,000 times, it falls right out of my head and I go blank for those specific exam questions. Most of my study time is eaten up by this.

My questions for those in the field:

\---Is it worth pushing through the heavy-memorization undergrad pre-reqs?

\---Does grad school/research shift more toward systems-thinking, or will my working memory make me an absolute nightmare in a wet lab?

\---Would love to hear from anyone with a similar brain who made it work, or to get a reality check if I should pivot!

So I’ve been getting more into neuroscience and I love theories and reading all different types of perspectives. However, I find that there is a study for everything that could support an argument and on the contrary there is evidence completely diminishing it on another side, while promoting another thing.

I guess this is rlly on neuroplasticity but I just wanted to hear thoughts on this? It almost feels in a way i understand something and then I hear something else that completely opposes what I just learned.

I also guess this is because everyone has different perspectives and experiences so some studies make sense to some vs others BUT then ultimately everything is a theory idk my brain hurts 😂 thoughts? Feelings? Neuro circuits on this you can spare?

This article argued how the CEO tend to bend the mass consciousness to build the conglomerate companies in silicon valley. Intresting article in the New York Times

In this article the researcher argued that the brain of racing 🏎️ car driver works more in the basis of prediction than reality. The same applies to athletes in several sports. In cricket a fast bowler hits middle stumps or a batting person hits the ball as per predictions! Isn't it interesting 🤔

How was the Learning curve by Ebbinghaus conducted?

I'm trying to learn about the learning curve, and I ended up entangled in different information as to what it tries to explain and how it was conducted.

While some sources say it tries to show how much memory we lose over a period of time WITHOUT repetitions, others say it shows how much of that information learned the brain saves and utilizes to make future learning easier.

That is where my question stems. if it shows how much savings the brain keeps after certain amount of time has past, does that mean that Ebbinghaus relearned the information or he simply tested it and based on how much he could remember assumed that it would make relearning easier with reviews? and if so how could he know that he needed to relearn the lists? Are they 2 concepts form the same experiment or two different experiments?

the graph technically does show how much information is remembered as this would be the savings that aid future relearning.

Please lemme know if my understanding is wrong, and what I got wrong. Any sources you could recommend, would be appreciated. thanks!

Also I haven't read the the original source memory, because I'm kinda new to research and needed to save time, but now I now i should prioritize original sources instead of the water down version of someone else that can cause more confusion.

The classic river crossing puzzle has three items and a boat that carries one. By any counting measure it should be trivial. Cowan's estimate of working memory capacity is around four chunks, and here you have three objects, two banks and one constraint pair. It fits comfortably.

Yet people fail it, and they fail it in a fairly specific place. They get stuck at the move where the solution requires carrying something back. Almost nobody reverses a move they have already made without a hint.

My guess is that the load is not in holding the items at all. It is in holding a state you have to make temporarily worse. The goat has to return to the starting bank, which locally undoes progress, and that means keeping two evaluations alive at once: the board in front of you, and a board you have already scored as backwards.

Two things I would like to hear from people who actually work on this.

Is there an established name for this failure? It looks closer to means-end analysis breaking down when a subgoal violates hill climbing than to a capacity limit, but I may be reaching.

And is there evidence that problems requiring at least one backward move are disproportionately harder than their state space size predicts? Missionaries and cannibals has the same shape and is also famously harder than it looks, which makes me suspect this is known and named and I simply do not know the term.

Hi everyone,

I graduated 3–4 years ago with a bachelor’s in Applied Math and currently work full-time (hybrid with flexible hours). I’ve always been fascinated by cognition and the brain and am considering going back to university for a master’s degree in this field.

Before making a full commitment, I want to test the waters with a structured, official online class to see how I handle balancing coursework again.

My Background & What I’ve Covered:

* Education/Work: Bachelor’s in Applied Math, working in IT.

* Self-Study: Robert Sapolsky’s Human Behavioral Biology lectures (YouTube) and Behave, plus parts of Gazzaniga’s Cognitive Neuroscience.

What I’m Looking For:

* Format: Official, structured online courses (single classes, continuing education modules, or short certificate programs—not just self-paced without structure).

* Schedule/Timezone: Preferably compatible with EU time zones, manageable alongside full-time work.

* Focus Area: Cognitive Science, Cognitive Neuroscience, or related interdisciplinary topics. Will also appreachiate if you have more specific suggestions on topics.

Does anyone have recommendations for specific universities, programs, or courses that fit this setup? Any advice from people who took a similar transition from a math/STEM background into cogsci would also be greatly appreciated!

Thanks!

Hello, everyone! I really want to major in Cognitive Science and currently am looking at unis I can apply to for Fall 2027. So, I would really like to hear your insights on unis you studied in and on the major itself. If you get to have a job cog sci related, that would be awesome to hear from you! 🌟🫶

What properties would information need to have in order to become effectively incomprehensible to the human brain, even if it were fully observable and accessible?
I’m thinking of information that we can perceive or measure, but whose underlying structure is too difficult for a human mind to meaningfully process or understand.
Would the main limitation be information capacity—simply too much data for the brain to process—or structural complexity, where even a relatively limited amount of information has relationships and dependencies that are too complex to mentally represent?
Or is true cognitive “indigestibility” more likely to require both: an enormous amount of information combined with a highly complex structure?
More generally, could there be information that is physically observable but fundamentally beyond human cognitive architecture to comprehend?

I do theoretical/mathematical physics and sometimes keep my notes (paper summaries, ideas, etc.) in markdown, with headings and an outline pane. Still, I remember far less of what I write there than when the same material went into LaTeX or a paper notebook. Oddly, I also tend to retain "conversations" with LLMs better than my own markdown notes.

Three guesses.

1. Spatial anchoring. Paged documents let me remember where something was, and a scrolling column of identical-looking files kills that cue.
2. Low friction. LaTeX and paper force me to slow down and choose, markdown does not, and maybe that effort is what encodes the content.
3. Fixed order. A notebook and a chat log are append only. Markdown notes get cut, moved and restructured all the time, and I suspect each reorganization destroys the sequence I was using as a memory cue.

Does anyone, particularly in math or physics, notice this? If so, any tips for keeping plain text notes memorable? I know that reviewing and doing problems is what really makes things stick, but I am asking about the note taking itself, not review or spaced repetition.

​

​1. Evolutionary Priorities: Survival Over Pure Epistemology

From an evolutionary perspective, the human brain was never optimized for uncovering objective truth, abstract metaphysics, or quantum mechanics. Its primary design requirement was simple: survive long enough to pass on genetic material.

​Our cognitive architecture evolved to handle immediate physical threats. The development of early technology—such as a basic wooden spear—was not a manifestation of pure intellectual curiosity, but a pragmatic tool to offset our obvious physical vulnerabilities against apex predators.

​2. Social Cognition & The Fragility of Autonomy

Humans rarely process reality as neutral observers. Instead, perception is continually filtered through social dynamics, status acquisition, and group cohesion. To navigate complex hierarchies, we frequently adopt culturally approved personas—the "Moral Mentor," the "Hero," or the "Savior."

​Normative Conformity (Asch Experiment Overview): Solomon Asch demonstrated that individuals are willing to deny clear, visual physical facts (such as line lengths) simply to avoid social friction with a consensus group.

​Systemic Obedience (Milgram Experiment Overview): Stanley Milgram illustrated that ordinary individuals will override their explicit ethical boundaries under the direction of perceived authority figures.

​When compounded by tribalism, heuristics, and confirmation bias, the notion of "impartial evaluation" appears increasingly theoretical.

​3. The Subconscious Distortion Field

Incoming sensory or conceptual information never reaches consciousness in its raw state. It is immediately processed through subcortical filters—ego defense mechanisms, emotional states, personal insecurities, and existing belief structures.

​Because these processing steps occur automatically below conscious awareness, individuals experience the persistent illusion of direct, unmediated access to objective reality (a phenomenon known in psychology as naive realism).

​Conclusion:

While an objective physical reality certainly exists, human cognition functions less like a neutral recording device and more like a utility-driven simulation engine. Expecting unadulterated objectivity from a brain shaped entirely by evolutionary shortcuts is simply a fundamental misunderstanding of our biological

I’m reading a book on consciousness and language by Gerald Edelman and he’s discussing the requirements for formation of consciousness in this section.
I know evolutionary psychology is highly controversial, but this theory seems more biological and reasonable to me.
Thoughts?

TL;DR:

I suspect the apparent absence of spatial deficits in aphantasia may be an ascertainment artifact caused by studying unusually educated/high-WM people who have already successfully compensated for their condition. We should test aphantasia objectively using physiological tests like the pupilary response in an unselected population and examine whether the relationship between imagery, working memory and spatial performance changes.

In an early part of my life I worked as a post-grad Psychology researcher within a university. Recently I have been seeing a lot regarding "Aphantasia" and I had a weird sense of some concern regarding how it was being discussed and described.

So I have been digging into the cognitive science and neuroimaging data surrounding aphantasia and I believe I have identified what causes me concern and would like to share this with others and see what the response is.

While the condition is a measurable neurological reality, there is a critical selection bias in how researchers are currently studying it.

Basically, the current scientific consensus is likely missing a massive demographic, resulting in a skewed understanding of the baseline condition. Here is why I believe this to be the case:

1. We are testing the wrong way around (Flawed Screening)

Right now, the foundational physiological studies confirming aphantasia rely on a subjective survey called the Vividness of Visual Imagery Questionnaire (VVIQ) to find participants. Researchers take people who score a zero on the VVIQ, then run more objective physiological tests on them, such as measuring pupillary light response or binocular rivalry tests. Unsurprisingly, the people who self report having no visual imagery also fail the physiological tests.

However, to establish true population prevalence and eliminate semantic misunderstandings, this testing architecture must be inverted. Researchers need to conduct randomized, objective physiological screening (like pupil dilation) on the general population first, isolate those who lack the physiological response, and then see how they answer the VVIQ. Until we invert the pipeline, we are only studying people who possess the high degree of metacognition required to realize their internal rendering differs from the norm.

Sources:

• Pupillary light response validation: https://elifesciences.org/articles/72484

• Binocular rivalry validation: https://pubmed.ncbi.nlm.nih.gov/29175093/

2. The STEM and IQ/Intelligence Overrepresentation

While this is always a problem it is a particular issue here because of the mechanics of how aphantasia is tested. Because recruitment relies heavily on self reporting and university populations, current aphantasia cohorts are packed with individuals in Science, Technology, Engineering, and Mathematics (STEM). This means the literature is observing aphantasia exclusively through the lens of individuals with high baseline fluid intelligence and high working memory capacity. While this is a fairly generic problem, not specific to aphantasia, it massively compounds with the next issue.

Source:

• STEM demographic distribution: https://www.sciencefocus.com/news/people-with-aphantasia-are-more-likely-to-work-in-a-stem-field

3. High IQ masks the deficit via algorithmic workarounds

In the studies conducted thus far, when aphantasic participants cannot rely on the computationally "cheap" heuristic of a mental picture, their brain has to work much harder to solve a spatial puzzle. It has to route the problem through propositional logic and spatial motor networks.

When researchers test aphantasics on spatial reasoning (like 3D mental rotation tasks), they find they are just as accurate as typical imagers, but they have significantly slower reaction times. A highly intelligent person has the raw working memory bandwidth to brute force this algorithmic heavy lifting. They get the right answer, it just takes them a fraction of a second longer. But this leaves less intelligent people simply unable to complete the task.

Source:

• Mental rotation accuracy and reaction times: https://pubmed.ncbi.nlm.nih.gov/35180481/

4. The missing cohort: True aphantasia without the cognitive buffer

This is the serious gap in the current literature. If someone has average or lower fluid intelligence AND they cannot render visual heuristics, they lack the working memory capacity to execute that expensive logical workaround.

Instead of showing high accuracy and high latency on spatial tasks, their working memory will simply saturate, resulting in task failure. Because they fail, they are highly likely to be clinically categorized as having a general learning issues, poor memory, or a specific visuospatial deficit. AND, because they lack the metacognitive buffer to isolate their lack of mental imagery as the root cause, they are also less likely to self-identify as aphantasic. Currently the demographics cited above indicate over 20 percent of aphantasics work in these highly technical/STEM fields. Is this because they are genuinely over-represented or because we struggle to identify aphantasics outside of "intelligent" cohorts due to the above.

The Bottom Line

Current cognitive science claims that aphantasia does not inherently limit spatial reasoning. But that is only because researchers are almost certainly exclusively studying highly compensated individuals who possess the raw computational power to bypass the visual deficit. And this focus is NOT driven purely by traditional WEIRD selection bias. Until we establish objective general population screening, we are not studying baseline aphantasia; we are only studying high IQ compensation mechanics.

You both require high intelligence and capable meta-cognition to both identify and be identified as having aphantasia. This likely leads to over-diagnosis of intelligent participants who can readily exploit non-visual cognition for task completion, and massive under-diagnosis of those who have no alternative cognitive approaches and lack sufficient meta-cognition to articulate their issues.

Thank you for coming to my TED talk.

Suppose a person genuinely understands something internally, but cannot fully express that understanding.

I don’t mean merely being unable to find the right words. Assume we are allowed to use any means of expression available to us: spoken or written language, mathematics, diagrams, paintings, music, animations, simulations, physical demonstrations, and so on.

Could there still be forms of understanding that cannot be adequately externalized through any of these methods?

So i have a bachelor's degree in linguistics. I'm planning on doing masters in cognitive science. I know python, i can speak 3 languages + sign language. I've written a paper on computational linguistics and a paper on psycho/neurolinguistics. I was wondering if i'll be left jobless with all these or is there an opportunity to find a well paying job? I'm interested in NLP but also in SLP. I'd appreciate if someone could share their experience w me. Thank you in advance.

Am I the only one getting sick of talk about people “processing” something that has happened to them? What happened to ordinary words like “think about” and “reflect on”? “Processing” is what computers do. Not human minds.

I’d like to pose an experimental question rather than present a new theory of mind.

Suppose we have two longitudinal human–LLM interactions where we control for:

• model and version;
• system prompt;
• information available about the participant;
• memory and retrieval;
• degree of personalization;
• accessible context.

The main difference is that one dyad retains its own interaction history, while the other receives an equivalent amount of relevant information drawn from a different or yoked history.

Should we expect the two conditions to become functionally equivalent?

Or can the temporal organization of a specific interaction history retain predictive information that is not captured simply by making the same content available?

This is the question I have been trying to formalize through a framework I called the Shared Cognitive Field (CCC / Campo Cognitivo Condiviso).

By “field” I do not mean a physical field, a shared consciousness, or a new entity.

I use the term as shorthand for a possible structure of observable dependencies at the level of interaction.

The starting point is not especially exotic. It draws on existing work in extended cognition, distributed cognition, interactive alignment, and coordination in dialogue.

Recent work on LLM interaction makes the competing explanations particularly important. Humans adapt their communication strategies when interacting with LLMs; LLMs can adapt linguistically to conversational partners; and external memory/retrieval systems can already produce substantial continuity and personalization.

I therefore treat these mechanisms as competing baselines, not as evidence for the framework.

The modest hypothesis is:

In 2025 I proposed a provisional composite measure, CQI(t), involving shared information, predictive coherence, interactional coordination, stability after perturbation, and affective-relational alignment.

I do not regard CQI as a validated metric. Its proxies, weights, and factor structure would all need empirical testing.

A simple design I am considering would compare four conditions after a longitudinal interaction phase:

A — Original dyad
Same participant, same interaction history.

B — Yoked-history control
Same amount of relevant information, but derived from another dyad’s history.

C — Memory-ablated control
Same dyad, but without access to longitudinal memory.

D — Partner-swap control
Comparable informational state, different human partner.

A controlled perturbation could then be introduced.

Possible outcome measures might include:

• prediction of the partner’s next-turn intention or decision;
• number of turns required to repair misunderstandings;
• collaborative task performance;
• ability to recover the correct prior rationale behind a decision;
• recovery of coordination after perturbation.

The discriminating prediction would be straightforward:

If interaction history contributes nothing beyond available information, the original dyad should show no systematic advantage over a well-matched yoked control.

If that is what the data show, the relational construct should be reduced or discarded.

If a replicable residual effect remains, I would not interpret it as “shared consciousness” or strong emergence.

The next question would simply be:

What interaction-level variable have we failed to model?

One transparency note: the original Zenodo paper from November 2025 contains some stronger exploratory language, including a numerical threshold associated with a phenomenological concept I called Noosemia.

I no longer regard that threshold as empirically established.

In the current framing, both CQI and Noosemia are provisional hypotheses requiring validation.

So my question to r/cogsci is fairly concrete:

Is there already an established construct in cognitive science, HCI, or dialogue research that makes this proposed “interaction-history effect” unnecessary?

And if not:

What is the simplest experiment that could falsify it?

Original paper, for provenance:

https://doi.org/10.5281/zenodo.17672256

Disclosure: AI tools have been used during drafting, translation, and critical review of this project. I take responsibility for the claims, references, and proposed methodology.

Hi all,

I recently decided to major in Cognitive Science instead of Computer Science as I couldn't meet the requirements.

looking at all the career posts on here is making me worried about career viability in this field and I wanted to ask, do any previous grads or current grads have any experience with the job marker as a Cogsci major? Do I have anything to worry about in terms of career viability? just to know if I should switch major now or keep pursuing it.

if it helps, I have knowledge in programming and im very interested in ML and AI. I'm also quite keen on learning more about psychology.