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Takeaways:
- Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.
- Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.
- Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.
- From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.
- Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.
- DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.
- Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.
- Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.
- Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.
- Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility.
Chapters:
10:35 The Struggles of Bayesian Statistics in Psychology
22:30 Exploring Appetite and Cognitive Performance
29:45 Research Methodology and Causal Inference
36:36 Understanding Cravings and Definitions
39:02 Intermittent Fasting and Cognitive Performance
42:57 Practical Recommendations for Intermittent Fasting
49:40 Balancing Experimental Psychology and Statistical Modeling
55:00 Pressing Questions in Health Psychology
01:04:50 Future Directions in Research
Thank you to my Patrons for...
