I just finished reading an all-time classic of experiment psychology “Principles of Behavior” by Clark Hull (1943). In the book, the author tries to delineate the first principles of a scientific theory of behaviour, based on a capillary survey of (mainly) animal experiments 1/n The first chapter presents his epistemological manifesto in favour of a mathematical (logico-deductive) formalization. It reminded me of the preface of “Theory of games" (1944). Back then use of formal approaches to social sciences was presented as a necessary paradigm shift

🚨🥳Very glad to share the publication by @TrendsCognSci of this review paper entitled “The computational roots of positivity and confirmation bias in human reinforcement learning” with my usual partner in scientific crime @mael_lebreton (long) thread 🧵
doi.org/10.1016/j.tics… In this paper we summarise the evidence allowing answering this fundamental question: do we (reinforcement) learn equally well from positive or negative prediction errors? From outcomes that confirm our current choices?

Will huge sample sizes (alone) solve psychology problems (as for example it is claimed they solved behavioural genetics problems)? This classic article clearly illustrate that "not quite". It start from recognising a key difference between psychology and physics 1/n Physical theories allow point estimate predictions (what the exact value of an empirical measure should be), therefore increasing the number of measures will increase the precision and represent a more stringent test for the theory 2/n

Thrilled to share our last paper! In this review article, we discuss why the description-experience gap may constitute a challenge to #neuroeconomics (specifically when comparing human and monkey findings)
with @PagerFM & @FCerrotti
preprint 👉 psyarxiv.com/9s23f/ In description paradigms, payoffs and probabilities (i.e. the decision variables) are given explicitly and symbolically. In experience paradigms, decision variables are never stated and information is acquired a via trial-by-trial feedbacks.