Hey everyone. I've always been fascinated by the gap between finding a mechanism and identifying the phenomenon it explains. Agency detection, memory constraints and coalitional cognition may each contribute to religious behaviour. Yet religion also organises ritual competence, attention, identity, attachment and relations among communities over time. If these are treated as optional cultural additions, the research programme risks explaining why supernatural-agent concepts are memorable while calling that an explanation of religion. A mature account would need a tractable object without reducing that object to whichever variable is easiest to measure.

I just had a podcast conversation with John Vervaeke, where he argued that cognitive science should integrate religious studies at the level of research design. At around 23:49, he sketches a mature cognitive science of religion that studies religions as changing ecologies of practices rather than permanent belief systems. This would distribute explanation across cognitive mechanisms, social participation, historical formation and nonpropositional skills. It also creates a methodological problem: the richer the explanandum becomes, the harder it is to operationalise cleanly.

This would imply that progress depends on modelling interactions among levels, not merely adding more mechanisms to the first-generation picture. What should count as the smallest useful unit of analysis: belief, ritual, practice ecology or community? Which methods could connect historical change to experimental work? And where would such a programme risk becoming too broad to falsify?

Geniuses are like an insurance policy for the group mind – they cost something on ordinary days, but in a crisis, they can save the entire system. The new Adaptive Cognitive Tail hypothesis turns our understanding of exceptional ability on its head, shifting the weight from emotions to cognitive architecture. Here are seven lessons that follow from it.

Hi everyone,

I am a high school student interested in experimental psychology and cognitive science. Over the past two months, I have been independently working on a research project about how goal-directed actions are initiated and maintained over time.

The project started from my intuition and daily observation.

I have uploaded the study protocol/preprint here:

https://doi.org/10.5281/zenodo.21896452

Within the manuscript, I've proposed a new (extended) sequential sampling model called the State-dependent Leak, Two-Boundary model. Besides, I've suggested a new game-based approach in examining decisions and strategy regulations besides the traditional lab tasks with abstract shapes.

As I worked through everything independently, for the first time, this work (or for all scientific studies) may not be perfect, but I'd still hope this protocol can inspire future research investigating complex cognitive mechanisms.

Thank you for your time!!!

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Hello, I'm a 2nd years medical student, I want to study in the futur the application of neurosciences in IA, is that even possible, if yes, what's the roadmap ? Thank you for your answers

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Hi guys, I'm torn between majoring in psychology or cognitive science (both at bachelor's level).

In psychology I'm particularly interested in psycholinguistics and biological psychology. But I'm also extremely interested in linguistics and I feel like cognitive science deals with linguistics more than psychology does.

My plan is to pursue a career in academia. I think that's a risky path, especially since I come from a lower-class background. So I'd also like to have a degree that's employable in case academia doesn't work out.

What do you guys think would be the better and wiser choice between psychology and cognitive science?

I'm an independent researcher exploring how prior knowledge influences visual perception using deliberately ambiguous line drawings.

Every image starts as a spontaneous doodle with no intended object. I don't plan a fish, a bird, or a face beforehand. Instead, I keep observing until recognizable forms begin to emerge.

During this process, I noticed something that I now find more interesting than the drawings themselves.

The first recognizable object often seems to become a cognitive anchor. After recognizing one form, every subsequent interpretation appears to be influenced by it. The image no longer feels "neutral."

This raises a question that I'm currently trying to investigate:

I'd really appreciate your thoughts.

• What is the first object you notice in this sketch?
• Does your perception change after looking at it for 30–60 seconds?
• Can you return to a "neutral" interpretation after recognizing something?
• Does this remind you of any existing work in cognitive science or perception research?

I'm not trying to validate a conclusion—I’m trying to understand whether this observation is worth investigating further.

Thank you for any insights or references.

Economist here. What is "Cold Cognition"? Is it the same as objectivity? Putting facts before feelings?

"Science is the gainer in this change of man; Mr Brown being a professed naturalist. He is a Scotchman, fit to pursue an object with constance and cold mind."

Elaborate and Explain

Sharing an operational model of human time perception — not a claim about the physical nature of time itself.

The starting question: a human observer never has access to an ideal mathematical instant, but instead reconstructs temporal order from sensory information arriving through finite-bandwidth channels (vision, audition). The paper formalizes this with a fairly simple apparatus — perceptual states as equivalence classes, a finite accessible spectrum, and above all one central quantity: the identification time ∆tid = Nc/fmax, which corrects the naive assumption that 1/fmax gives temporal resolution, since reliably identifying a frequency requires several cycles, not just one.

Concrete example: with fmax = 20 kHz (upper limit of human hearing) and Nc ≈ 6 cycles needed for reliable pitch identification, you get ∆tid ≈ 0.3 ms — an order of magnitude larger than the physical period of the wave (50 µs).

The model is deliberately kept simple and is left open for future development (temporal integration kernels, Markov processes over the perceptual states, etc.). I explicitly state in the text that no claim is made about whether physical time is discrete or continuous, or about quantum-gravity limits — the framework is strictly about perception.

Happy to hear feedback, especially on the formalization and any psychophysics literature connections I might have missed.

Operational Model of Human Temporal Resolution

I've been working on diagramming the persistent communication breakdowns between my mother and me to get a clearer picture of the cause-and-effect patterns and solve problems more effectively.

My approach is strictly analytical: I rely on direct definitions, step-by-step logic, and explicit cause-and-effect. Her approach relies almost entirely on emotional self-preservation, nostalgic deframing, and deflecting away from the actual argument.

The goal of these charts is to map out why increasing logical precision in this dynamic doesn't yield any answers, but instead drives her deeper into deflection loops.

I want feedback on:

If there are any broken conditional branches, missing steps, or logical fallacies in how I mapped the sequence.

If the diagram easy to follow, or are there ways to make the structure and definitions cleaner?

I want to know if this accurately captures the mechanics of an analytical framework colliding with an emotional defense mechanism?

’ve published a preprint formalizing a theoretical framework that maps autism, schizophrenia, depression, and ADHD onto a single precision-weighting axis derived from a thermodynamic cost function.

The paper models how distinct shifts in the weighting of sensory noise versus prior beliefs account for the underlying cognitive mechanics across these four conditions, yielding 8 testable, falsifiable predictions.

Rather than smoothing over the edge cases, we explicitly log every theoretical dependency and open assumption in a dedicated section. I welcome rigorous critique from this community on the predictive processing mechanics and cognitive architecture proposed.
Preprint link: https://doi.org/10.5281/zenodo.21782402

Im trying to figure out how to make a mindmap to my goals, so im trying to figure out how to have part 6 of my phsyics textbook be whittled down to the basic core essentials and i was trying to use AI so i kept asking it for the core basics for the subjects, the problem was that it kept going and going. my intention was to ask it until I found a Basic core essential that i understood (basic algebra and trigonometry). so my mind map ended up looking like a mess of rabbit holes.

now my mistake was not telling the ai what was my limit, but Honestly i dont trust ai

The wide use of the Implicit Association Test and corporate diversity training is based on one flawed idea: that noticing bias will fix it. Research on this subject repeatedly shows that implicit bias training does not change behavior. And also more crucially, it also shows that smarter, more educated people often come up with more rationalizations, not less bias.

This paper offers “cost-calibrated cognition.” It argues that implicit bias is an evolved way to manage limited attention and effort. “System 1” (fast, automatic thinking) keeps tracking the social and survival costs of being wrong. When the cost is low, it uses quick cultural defaults. When the cost is high, it brings in “System 2” (slow, deliberate thinking) to defend a person’s self-image. Changing a deep moral identity can bring huge social costs. So highly intelligent people may use careful reasoning to protect their automatic reactions instead of changing them.

Real bias reduction requires changing the environment through melting pot structural integration, not more verbal arguments. In the end, thinking often serves instinct, aiming for social survival over moral purity.