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20202023
most citedPredicting Human Similarity Judgments Using Large Language Models

6 citations · 12 across the 4 of their papers we have counts for

collaborators

6 papers

q-bio.NC20231 cited

The Universal Law of Generalization Holds for Naturalistic Stimuli

Raja Marjieh, Nori Jacoby, Joshua C. Peterson +1

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that…

cs.CL20221 cited

Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody

Pol van Rijn, Harin Lee, Nori Jacoby

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly…

cs.LG20226 cited

Predicting Human Similarity Judgments Using Large Language Models

Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers +2

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting…

cs.CV20214 cited

Passive Attention in Artificial Neural Networks Predicts Human Visual Selectivity

Thomas A. Langlois, H. Charles Zhao, Erin Grant +3

Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification…

cs.HC2021

Exploring emotional prototypes in a high dimensional TTS latent space

Pol van Rijn, Silvan Mertes, Dominik Schiller +4

Recent TTS systems are able to generate prosodically varied and realistic speech. However, it is unclear how this prosodic variation contributes to the perception of speakers' emot…

q-bio.NC2020

Gibbs Sampling with People

Peter M. C. Harrison, Raja Marjieh, Federico Adolfi +5

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantn…