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finish beta section
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DominiqueMakowski committed Jul 31, 2024
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11 changes: 9 additions & 2 deletions README.md
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[![](https://img.shields.io/badge/status-looking_for_collaborators-orange)](https://github.com/DominiqueMakowski/CognitiveModels/issues)
[![](https://img.shields.io/badge/access-open-brightgreen)](https://dominiquemakowski.github.io/CognitiveModels/)

The project is to write an open-access book on **cognitive models**, i.e., statistical models that best fit **psychological data** (e.g., reaction times, scales from surveys, ...).
This framework aims at moving away from a mere description of the data, to make inferences about the underlying cognitive processes that led to its generation.
This project aims at writing an open-access book on cognitive statistical models in Julia.

## Why Cognitive Models?

Psychological and behavioural data that typically result from cognitive processes are often exhibiting characteristics that are not well captured by traditional statistical models.
This issue has been simply ignored for a long time, with researchers using simple linear models without even thinking about whether they are appropriate, contributing to the **replication crisis**.
Recent advances have underlined the need for statistical models that better reflect the data at hand.

**Cognitive models** are statistical models that best fit **psychological data** (e.g., reaction times, scales from surveys, ...) and can offer new insights by enabling inferences about the underlying cognitive processes that led to its generation.

## Why Julia?

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{"headings":["brief-intro-to-julia-and-turing","installing-julia-and-packages","julia-basics","generate-data-from-normal-distribution","recover-distribution-parameters-with-turing","bayesian-linear-models","hierarchical-models"],"options":{"chapters":true},"entries":[]}
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{"headings":["the-data","gaussian-aka-linear-model","model-specification","posterior-predictive-check","scaled-gaussian-model","solution-1-directional-effect-of-condition","solution-2-avoid-exploring-negative-variance-values","solution-3-use-a-softplus-function","exponential-transformation","softplus-function","the-model","conclusion","the-problem-with-linear-models","shifted-lognormal-model","prior-on-minimum-rt","model-specification-1","interpretation","exgaussian-model","conditional-tau-tau-parameter","interpretation-1","shifted-wald-model","model-specification-2","model-comparison","shifted-lognormal-mixed-model"],"options":{"chapters":true},"entries":[]}
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{"headings":["preface","why-julia","why-bayesian","looking-for-coauthors"],"entries":[],"options":{"chapters":true}}
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{"entries":[],"headings":["evidence-accumulation","wald-distribution-revisited","drift-diffusion-model-ddm","linear-ballistic-accumulator-lba","other-models-lnr-rdm","including-random-effects","random-intercept","random-slopes","performance-tips","additional-resources"],"options":{"chapters":true}}
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