finish beta section

Author: DominiqueMakowski

README.md

@@ -5,8 +5,15 @@
 [![](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?
 

content/.jupyter_cache/executed/0f749050907f8a390d3235420d82cc49/base.ipynb

@@ -1 +1 @@
-{"2_predictors.qmd":[],"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"3_scales.qmd":["makowski2023novel"],"references.qmd":[],"index.qmd":[],"5_individual.qmd":[],"3b_choices.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check"],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"],"4b_rt_generative.qmd":[],"3a_scales.qmd":["makowski2023novel"],"1_introduction.qmd":[],"4a_rt_descriptive.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","wiemann2023using","lo2015transform","schramm2019reaction","wagenmakers2005relation","limpert2001log","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"]}
+{"4a_rt_descriptive.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","wiemann2023using","lo2015transform","schramm2019reaction","wagenmakers2005relation","limpert2001log","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"],"2_predictors.qmd":[],"3a_scales.qmd":["makowski2023novel","kubinec2023ordered"],"4_1_Normal.qmd":["wagenmakers2008diffusion","theriault2024check","lo2015transform","schramm2019reaction"],"3_scales.qmd":["makowski2023novel"],"1_introduction.qmd":[],"3b_choices.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check"],"5_individual.qmd":[],"4b_rt_generative.qmd":[],"references.qmd":[],"index.qmd":[],"4_rt.qmd":["wagenmakers2008diffusion","heathcote2012linear","theriault2024check","lo2015transform","schramm2019reaction","balota2011moving","matzke2009psychological","hohle1965inferred","kieffaber2006switch","matzke2009psychological","schwarz2001ex","heathcote2004fitting","anders2016shifted"]}

content/.jupyter_cache/executed/a4a981d0443d63d7224549a44299061c/base.ipynb

@@ -1 +1 @@
-{"entries":[],"options":{"chapters":true},"headings":[]}
+{"options":{"chapters":true},"entries":[],"headings":[]}

content/.jupyter_cache/global.db

@@ -1 +1 @@
-{"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":[]}
+{"entries":[],"options":{"chapters":true},"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"]}

content/.quarto/cites/index.json

@@ -1 +1 @@
-{"entries":[],"headings":["extracting-individual-parameters-from-mixed-models","population-informed-individual-bayesian-models","task-reliability"],"options":{"chapters":true}}
+{"options":{"chapters":true},"entries":[],"headings":["extracting-individual-parameters-from-mixed-models","population-informed-individual-bayesian-models","task-reliability"]}

content/.quarto/idx/2_predictors.qmd.json

@@ -1 +1 @@
-{"options":{"chapters":true},"entries":[],"headings":["categorical-predictors-condition-group","interactions","ordered-predictors-likert-scales","monotonic-effects","non-linear-relationships","polynomials","generalized-additive-models-gams"]}
+{"entries":[],"headings":["categorical-predictors-condition-group","interactions","ordered-predictors-likert-scales","monotonic-effects","non-linear-relationships","polynomials","generalized-additive-models-gams"],"options":{"chapters":true}}

content/.quarto/idx/index.qmd.json

@@ -1 +1 @@
-{"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":[]}
+{"options":{"chapters":true},"entries":[],"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"]}

content/.quarto/xref/04307669

@@ -1 +1 @@
-{"headings":["preface","why-julia","why-bayesian","looking-for-coauthors"],"entries":[],"options":{"chapters":true}}
+{"entries":[],"options":{"chapters":true},"headings":["preface","why-cognitive-models","why-julia","why-bayesian","looking-for-coauthors"]}

content/.quarto/xref/15f266d2

@@ -1 +1 @@
-{"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}}
+{"options":{"chapters":true},"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"]}