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Found 14 papers, 3 papers with code
A Rational Analysis of the Speech-to-Song Illusion
Here we provide a formal account of this phenomenon, by recasting it as a statistical inference whereby a rational agent attempts to decide whether a sequence of utterances is more likely to have been produced in a song or speech.
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa.
MacGyver: Are Large Language Models Creative Problem Solvers?
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting.
Improving Interpersonal Communication by Simulating Audiences with Language Models
How do we communicate with others to achieve our goals?
Getting aligned on representational alignment
Finally, we lay out open problems in representational alignment where progress can benefit all three of these fields.
The Universal Law of Generalization Holds for Naturalistic Stimuli
Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity.
Around the world in 60 words: A generative vocabulary test for online research
Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0. 82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports.
Large language models predict human sensory judgments across six modalities
Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science.
On the Informativeness of Supervision Signals
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans.
Analyzing Diffusion as Serial Reproduction
Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise.
Words are all you need? Language as an approximation for human similarity judgments
Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data.
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
Predicting Human Similarity Judgments Using Large Language Models
Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning.
Gibbs Sampling with People
We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP).
