Search Results for author: Martin N. Hebart

Found 5 papers, 2 papers with code

VICE: Variational Interpretable Concept Embeddings

1 code implementation2 May 2022 Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira

This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.

Experimental Design Object +3

Semantic features of object concepts generated with GPT-3

1 code implementation8 Feb 2022 Hannes Hansen, Martin N. Hebart

Given recent promising developments with transformer-based language models, here we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans.

Revealing interpretable object representations from human behavior

no code implementations ICLR 2019 Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart

To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1, 854 object categories.


The Same Analysis Approach: Practical protection against the pitfalls of novel neuroimaging analysis methods

no code implementations20 Mar 2017 Kai Görgen, Martin N. Hebart, Carsten Allefeld, John-Dylan Haynes

We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided.

Neurons and Cognition Applications

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