Marco Ciapparelli
@marcociapparelli.bsky.social
Postdoc in psychology and cognitive neuroscience mainly interested in conceptual combination, semantic memory and computational modeling.
https://marcociapparelli.github.io/
https://marcociapparelli.github.io/
Reposted by Marco Ciapparelli
Our paper “The cost of thinking is similar between large reasoning models and humans” is now out in PNAS! 🤖🧠
w/ @fepdelia.bsky.social, @hopekean.bsky.social, @lampinen.bsky.social, and @evfedorenko.bsky.social
Link: www.pnas.org/doi/10.1073/... (1/6)
w/ @fepdelia.bsky.social, @hopekean.bsky.social, @lampinen.bsky.social, and @evfedorenko.bsky.social
Link: www.pnas.org/doi/10.1073/... (1/6)
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
www.pnas.org
November 19, 2025 at 8:14 PM
Our paper “The cost of thinking is similar between large reasoning models and humans” is now out in PNAS! 🤖🧠
w/ @fepdelia.bsky.social, @hopekean.bsky.social, @lampinen.bsky.social, and @evfedorenko.bsky.social
Link: www.pnas.org/doi/10.1073/... (1/6)
w/ @fepdelia.bsky.social, @hopekean.bsky.social, @lampinen.bsky.social, and @evfedorenko.bsky.social
Link: www.pnas.org/doi/10.1073/... (1/6)
Reposted by Marco Ciapparelli
A fascinating read in @theguardian.com on the psycholinguistics of swearing!
Did you know Germans averaged 53 taboo words, while Brits e Spaniards listed only 16?
Great to see the work of our colleague Simone Sulpizio & Jon Andoni Duñabeitia highlighted! 👏
www.theguardian.com/science/2025...
Did you know Germans averaged 53 taboo words, while Brits e Spaniards listed only 16?
Great to see the work of our colleague Simone Sulpizio & Jon Andoni Duñabeitia highlighted! 👏
www.theguardian.com/science/2025...
Italian blasphemy and German ingenuity: how swear words differ around the world
Once swearwords were dismissed as a sign of low intelligence, now researchers argue the ‘power’ of taboo words has been overlooked
www.theguardian.com
October 23, 2025 at 12:27 PM
A fascinating read in @theguardian.com on the psycholinguistics of swearing!
Did you know Germans averaged 53 taboo words, while Brits e Spaniards listed only 16?
Great to see the work of our colleague Simone Sulpizio & Jon Andoni Duñabeitia highlighted! 👏
www.theguardian.com/science/2025...
Did you know Germans averaged 53 taboo words, while Brits e Spaniards listed only 16?
Great to see the work of our colleague Simone Sulpizio & Jon Andoni Duñabeitia highlighted! 👏
www.theguardian.com/science/2025...
Reposted by Marco Ciapparelli
Important fMRI/RSA study by @marcociapparelli.bsky.social et al. Compositional (multiplicative) representations of compounds/phrases in left IFG (BA45), mSTS, ATL; left AG encodes constituents, not their composition, weighing the right element more, vice versa IFG 🧠🧩
academic.oup.com/cercor/artic...
academic.oup.com/cercor/artic...
Compositionality in the semantic network: a model-driven representational similarity analysis
Abstract. Semantic composition allows us to construct complex meanings (e.g., “dog house”, “house dog”) from simpler constituents (“dog”, “house”). Neuroim
academic.oup.com
September 26, 2025 at 9:29 AM
Important fMRI/RSA study by @marcociapparelli.bsky.social et al. Compositional (multiplicative) representations of compounds/phrases in left IFG (BA45), mSTS, ATL; left AG encodes constituents, not their composition, weighing the right element more, vice versa IFG 🧠🧩
academic.oup.com/cercor/artic...
academic.oup.com/cercor/artic...
Happy to share that our work on semantic composition is out now -- open access -- in Cerebral Cortex!
With Marco Marelli (@ercbravenewword.bsky.social), @wwgraves.bsky.social & @carloreve.bsky.social.
doi.org/10.1093/cerc...
With Marco Marelli (@ercbravenewword.bsky.social), @wwgraves.bsky.social & @carloreve.bsky.social.
doi.org/10.1093/cerc...
September 12, 2025 at 9:15 AM
Happy to share that our work on semantic composition is out now -- open access -- in Cerebral Cortex!
With Marco Marelli (@ercbravenewword.bsky.social), @wwgraves.bsky.social & @carloreve.bsky.social.
doi.org/10.1093/cerc...
With Marco Marelli (@ercbravenewword.bsky.social), @wwgraves.bsky.social & @carloreve.bsky.social.
doi.org/10.1093/cerc...
Reposted by Marco Ciapparelli
I am incredibly proud to share my first, first-author paper as a postdoc with @benhayden.bsky.social . How does the human hippocampus, known for encoding concepts, represent the meanings of words while listening to narrative speech?
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
February 27, 2025 at 8:17 PM
I am incredibly proud to share my first, first-author paper as a postdoc with @benhayden.bsky.social . How does the human hippocampus, known for encoding concepts, represent the meanings of words while listening to narrative speech?
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
Reposted by Marco Ciapparelli
Here's a set of new results from my lab asking how the brain combines different ideas (concepts)! Now in press at J of Cog Neuro, we looked at how semantic composition (combining different concepts together) shapes brain activity. Preprint here: www.biorxiv.org/content/10.1... #neuroskyence
The Neural Consequences of Semantic Composition
Humans can create completely new concepts through semantic composition. These ‘conceptual combinations’ can be created by attributing the features of one concept to another (e.g., a lemon flamingo mig...
www.biorxiv.org
August 26, 2025 at 8:24 PM
Here's a set of new results from my lab asking how the brain combines different ideas (concepts)! Now in press at J of Cog Neuro, we looked at how semantic composition (combining different concepts together) shapes brain activity. Preprint here: www.biorxiv.org/content/10.1... #neuroskyence
I'm sharing a Colab notebook on using large language models for cognitive science! GitHub repo: github.com/MarcoCiappar...
It's geared toward psychologists & linguists and covers extracting embeddings, predictability measures, comparing models across languages & modalities (vision). see examples 🧵
It's geared toward psychologists & linguists and covers extracting embeddings, predictability measures, comparing models across languages & modalities (vision). see examples 🧵
July 18, 2025 at 1:40 PM
I'm sharing a Colab notebook on using large language models for cognitive science! GitHub repo: github.com/MarcoCiappar...
It's geared toward psychologists & linguists and covers extracting embeddings, predictability measures, comparing models across languages & modalities (vision). see examples 🧵
It's geared toward psychologists & linguists and covers extracting embeddings, predictability measures, comparing models across languages & modalities (vision). see examples 🧵
Reposted by Marco Ciapparelli
I’d like to share some slides and code for a “Memory Model 101 workshop” I gave recently, which has some minimal examples to illustrate the Rumelhart network & catastrophic interference :)
slides: shorturl.at/q2iKq
code (with colab support!): github.com/qihongl/demo...
slides: shorturl.at/q2iKq
code (with colab support!): github.com/qihongl/demo...
May 26, 2025 at 11:56 AM
I’d like to share some slides and code for a “Memory Model 101 workshop” I gave recently, which has some minimal examples to illustrate the Rumelhart network & catastrophic interference :)
slides: shorturl.at/q2iKq
code (with colab support!): github.com/qihongl/demo...
slides: shorturl.at/q2iKq
code (with colab support!): github.com/qihongl/demo...
🚨New preprint!🚨
1/7 Happy to share new work with Marco Marelli, @wwgraves.bsky.social & @carloreve.bsky.social! We re-analyze fMRI studies to identify which regions represent semantic information compositionally, and what operations best describe composition.
doi.org/10.1101/2025...
1/7 Happy to share new work with Marco Marelli, @wwgraves.bsky.social & @carloreve.bsky.social! We re-analyze fMRI studies to identify which regions represent semantic information compositionally, and what operations best describe composition.
doi.org/10.1101/2025...
Characterizing semantic compositions in the brain: A model-driven fMRI re-analysis
Semantic composition allows us to construct complex meanings (e.g., ''dog house'', ''house dog'') from simpler constituents (''dog'', ''house''). So far, neuroimaging studies have mostly relied on hig...
doi.org
April 28, 2025 at 12:33 PM
🚨New preprint!🚨
1/7 Happy to share new work with Marco Marelli, @wwgraves.bsky.social & @carloreve.bsky.social! We re-analyze fMRI studies to identify which regions represent semantic information compositionally, and what operations best describe composition.
doi.org/10.1101/2025...
1/7 Happy to share new work with Marco Marelli, @wwgraves.bsky.social & @carloreve.bsky.social! We re-analyze fMRI studies to identify which regions represent semantic information compositionally, and what operations best describe composition.
doi.org/10.1101/2025...
Reposted by Marco Ciapparelli
another day another minicons update (potentially a significant one for psycholinguists?)
"Word" scoring is now a thing! You just have to supply your own splitting function!
pip install -U minicons for merriment
"Word" scoring is now a thing! You just have to supply your own splitting function!
pip install -U minicons for merriment
![from minicons import scorer
from nltk.tokenize import TweetTokenizer
lm = scorer.IncrementalLMScorer("gpt2")
# your own tokenizer function that returns a list of words
# given some sentence input
word_tokenizer = TweetTokenizer().tokenize
# word scoring
lm.word_score_tokenized(
["I was a matron in France", "I was a mat in France"],
bos_token=True, # needed for GPT-2/Pythia and NOT needed for others
tokenize_function=word_tokenizer,
bow_correction=True, # Oh and Schuler correction
surprisal=True,
base_two=True
)
'''
First word = -log_2 P(word | <beginning of text>)
[[('I', 6.1522440910339355),
('was', 4.033324718475342),
('a', 4.879510402679443),
('matron', 17.611848831176758),
('in', 2.5804288387298584),
('France', 9.036953926086426)],
[('I', 6.1522440910339355),
('was', 4.033324718475342),
('a', 4.879510402679443),
('mat', 19.385351181030273),
('in', 6.76780366897583),
('France', 10.574726104736328)]]
'''](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:njnapclhkbrhe3hsq44q2e4w/bafkreibw7fjs4zeocjmfvpr4fo7wikiqenbdz7b3clwq2rolckpoqwkssu@jpeg)
April 2, 2025 at 3:35 AM
another day another minicons update (potentially a significant one for psycholinguists?)
"Word" scoring is now a thing! You just have to supply your own splitting function!
pip install -U minicons for merriment
"Word" scoring is now a thing! You just have to supply your own splitting function!
pip install -U minicons for merriment
Reposted by Marco Ciapparelli
🚀 My first PhD paper is out! 🚀
"How do multiple meanings affect word learning and remapping?" was published in Memory & Cognition!
Big thanks to my supervisors and co-authors (Iring Koch & @troembke.bsky.social).
Curious? Read it here: rdcu.be/eeY9o
#CognitivePsychology #WordLearning #Bilingualism
"How do multiple meanings affect word learning and remapping?" was published in Memory & Cognition!
Big thanks to my supervisors and co-authors (Iring Koch & @troembke.bsky.social).
Curious? Read it here: rdcu.be/eeY9o
#CognitivePsychology #WordLearning #Bilingualism
How do multiple meanings affect word learning and remapping?
rdcu.be
March 25, 2025 at 10:20 AM
🚀 My first PhD paper is out! 🚀
"How do multiple meanings affect word learning and remapping?" was published in Memory & Cognition!
Big thanks to my supervisors and co-authors (Iring Koch & @troembke.bsky.social).
Curious? Read it here: rdcu.be/eeY9o
#CognitivePsychology #WordLearning #Bilingualism
"How do multiple meanings affect word learning and remapping?" was published in Memory & Cognition!
Big thanks to my supervisors and co-authors (Iring Koch & @troembke.bsky.social).
Curious? Read it here: rdcu.be/eeY9o
#CognitivePsychology #WordLearning #Bilingualism
1/n Happy to share a new paper with Calogero Zarbo & Marco Marelli! How well do LLMs represent the implicit meaning of familiar and novel compounds? How do they compare with simpler distributional semantics models (DSMs; i.e., word embeddings)?
doi.org/10.1111/cogs...
doi.org/10.1111/cogs...
Conceptual Combination in Large Language Models: Uncovering Implicit Relational Interpretations in Compound Words With Contextualized Word Embeddings
Large language models (LLMs) have been proposed as candidate models of human semantics, and as such, they must be able to account for conceptual combination. This work explores the ability of two LLM...
doi.org
March 19, 2025 at 2:07 PM
1/n Happy to share a new paper with Calogero Zarbo & Marco Marelli! How well do LLMs represent the implicit meaning of familiar and novel compounds? How do they compare with simpler distributional semantics models (DSMs; i.e., word embeddings)?
doi.org/10.1111/cogs...
doi.org/10.1111/cogs...