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Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
@StePalminteri
Mar 11, 2022 • 7 tweets • 2 min read • Read on X
Just finished reading this old-ish gem by Herbert Simon (1992) that I strongly suggest reading for all interested in the epistemological foundations of behavioural sciences. Many key ideas are extremely clearly and convincingly laid down 1/n Image
The first great thing to mention about the paper is that even one of the "fathers" of cognitive revolution did not believe that a cognitive revolution ever happened 2/n Image
I wrote about this topic in another thread about Leahey's great paper (link below). A cognitive "revolution" never happened: let's get over it. 3/n
The second remarkable thing about the paper is one of the clearer defence of the anti-reductionist approach to understanding human cognition I have ever read (key passages below) 4/n Image
The third thing is a very clear illustration of the idea that normative principles (statistical or evolutionary) bear little heuristic values for discovering how the mind actually works, which is in essence an empirical endeavour 5/n Image
There are many other great things on the paper. It can be found here. journals.sagepub.com/doi/10.1111/j.…
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More from @StePalminteri

Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
Feb 27, 2023
Just posted in @PsyArXiv a short where I collected some suggestions concerning "how to prepare a rebuttal letter". Journals do not provide explicit guidance on this and, in my experience as a reviewer and editor, a great diversity exists in their format..

psyarxiv.com/kyfus/Image
.. however, not all formats are, in my modest and personal opinion, as effective. And by effective here I mean the capacity of delivering all the relevant information (how are you responding to the Reviewer's point) in a clear and efficient manner. I mostly focused on formatting
Tip 0: Before the rebuttal, the cover letter.
I find it extremely helpful when the cover letter mentions, in a narrative manner, the main modifications undertaken during the revision and how they are related to the Reviewers’ main points and the final story of the manuscript Image
Read 12 tweets
Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
Feb 25, 2023
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
In chapter II, we dive into the specifics of what an objective theory of behaviour should be. Of prime importance here is the introduction of the notions of intervening variables and observables, which illustrate how the standard narrative about behaviourists is false
Read 13 tweets
Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
Sep 19, 2022
I just finished reading this paper by Meehl on appraisal theory selection in Psychology (and beyond). The paper essentially proposes to amend rigid falsificationism (Popper0) in favor of a more flexible approach including a step of “strategic retreat and defense” (Lakatos) Image
The point here is that when one tests a theory, they never test the Theory "alone", but also a series of ancillary elements (how does the measurement work?) and assumptions (e.g., the ceteribus paribus clause: which is always false in psychology)...
Therefore, an apparent falsification could, in principle derive, from the theory being false OR by any other of the ancillary elements and assumption being false. The question: how do we decide whether or not a “strike is fatal” for a theory? Not an easy question, which…
Read 16 tweets
Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
May 31, 2022
🚨🥳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… Image
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? Image
We started by recognizing that similar biases have already been identified and documented by psychologists and economists in the past in belief updates. However, they are traditionally understood as specific to high-level processing ImageImage
Read 14 tweets
Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
Nov 17, 2021
Just finished reading this that I strongly recommend to anyone interested in history of cognitive science (and more broadly in questions related to the advancement of science) and is curious about what happened during the “cognitive revolutions” 1/n
citeseerx.ist.psu.edu/viewdoc/downlo…Image
The paper starts by (extremely clearly and sharply) reminding the elements of Kuhn’s hypothetical structure of scientific revolutions and illustrate criteria proposed by later scholars to “empirically” (or at least more objectively) assess whether a revolution occurred. Image
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The paper then analyses the historical contexts and records of the behaviouristic and cognitive revolution and provides compelling arguments to the claim that neither period actually witnessed a scientific revolution (according to Kuhnian -or common sense - definition). Image
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Read 8 tweets
Stefano Palminteri (@stepalminteri.bsky.social) Profile picture
Oct 8, 2021
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
In psychology the situation was (it still is) quite different: theories often make only directional prediction (i.e., RT should higher in condition 1 compared condition 2, but how much higher is not specified). 3/
Read 9 tweets

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