Do you remember this question:

How Can Slow Components Think So Fast?

This was the title of the 1988 Spring Symposium on Parallel Models of Intelligence, Stanford, California.
(Note that the pulse rates of biological neurons are several _orders of magnitude_ lower than clock rates of computer processors, even though one cannot be directly compared to the other.)

I think the answer has been known, or strongly suspected, for a long time.
Maybe it was proposed at that symposium itself.

As this BBC article puts it:
«... a leading theory about how our brains deal with visual information called predictive coding. It suggests that our visual system doesn't just passively process features in our surroundings when we look around. Instead, it first predicts what it expects to see by drawing on past experience before it processes discrepancies in the input from our eyes. This allows us to see more quickly.»

Furthermore, using computer models to help understand human cognition has long been one of the goals of cognitive science.

From the BBC:
AI can now 'see' optical illusions. What does it tell us about our own brains?
<https://www.bbc.com/future/article/20251218-how-ai-is-shedding-new-light-on-optical-illusions>

#AI
#ArtificialIntelligence
#CognitiveModeling
#CognitiveModelling
#CognitiveScience

AI can now 'see' optical illusions. What does it tell us about our own brains?

Our eyes can frequently play tricks on us, but scientists have discovered that some artificial intelligence can fall for the same illusions.

BBC

Awesome new Bayesian cognitive modeling package by Ven Popov and Gidon Frischkorn just dropped with an amazing Stan Discourse thread:

https://discourse.mc-stan.org/t/bmm-r-package-for-easy-and-flexible-bayesian-measurement-modeling-v1-0-0/35483/1

#stats #cognitivemodeling #bayes #cognition #modeling

Bmm: R package for easy and flexible Bayesian Measurement Modeling (v1.0.0)

I’m really happy to share the first official CRAN release of the Bayesian Measurement Modeling (bmm) R package, developed together with @g.frischkorn 🥳 🥳 🥳 tldr; 🥇 Hierarhical Bayesian estimation of complex measurment models in nearly any design 🥈 Uses brms as a translation engine for Stan and integrates into the existing R infrastructure 🥉 Provides a general Bayesian development framework for domain-specific models via highly modular code base, well-documented developer tools and useful ...

The Stan Forums
New #introduction at #fediscience! I'm Manuel, a postdoc in #Psychology and #CognitiveNeuroscience at #unieichstaett. I will be tooting about research on #PerceptualDecisionMaking, #VisualAwareness and #Metacognition using #CognitiveModeling, #Psychophysics and sometimes #EEG. I am also interested in #rstats, #python, and #openscience.
#introduction Hi all 👋 I am a researcher (psychologist by training) at the University of Zurich Switzerland interested in #Cognition (#Memory #DecisionMaking) and #Motivation from a #Developmental #Lifespan view, #OpenScience, #OpenData, also some #CognitiveModeling
@kaitclark @perspektivbrocken
Thanks for starting the list!
Can you please add me with the keywords #Psychometrics #CognitiveModeling #MathematicalPsychology
Does direction matter? Linguistic asymmetries reflected in visual attention
Finally: Our paper in #Cognition is online: Grab it while it's hot (and not yet paywalled)! (https://authors.elsevier.com/sd/article/S0010027718302427)
#psycholinguistics #visualAttention #cognitiveScience #cognitiveModeling
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