Publications

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Teasing apart the representational spaces of ANN language models to discover key axes of model-to-brain alignment

Eghbal Hosseini, Noga Zaslavsky, Colton Casto, Evelina Fedorenko. CCN, 2023.

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Evidence for a language-independent conceptual representation of pronominal referents

Mora Maldonado, Noga Zaslavsky, Jennifer Culbertson. CogSci, 2023.

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Alignment of ANN Language Models with Humans After a Developmentally Realistic Amount of Training

Eghbal Hosseini, Martin Schrimpf, Yian Zhang, Samuel Bowman, Noga Zaslavsky, Evelina Fedorenko. COSYNE, 2023.

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Generalization and Translatability in Emergent Communication via Informational Constraints

Mycal Tucker, Roger Levy, Julie Shah, Noga Zaslavsky. InfoCog @ NeurIPS, 2022.

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Trading off Utility, Informativeness, and Complexity in Emergent Communication

Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky. NeurIPS, 2022.

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Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik. NBDT, 2022.

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Towards Human-Agent Communication via the Information Bottleneck Principle

Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky. RSS Workshop on Social Intelligence in Humans and Robots, 2022.

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Teasing apart models of pragmatics using optimal reference game design

Irene Zhou, Jennifer Hu, Roger Levy, Noga Zaslavsky. CogSci, 2022.

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The emergence of discrete and systematic communication in a continuous signal-meaning space

Alicia Chen, Matthias Hofer, Moshe Poliak, Roger Levy, Noga Zaslavsky. CogSci, 2022.

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The evolution of color naming reflects pressure for efficiency: Evidence from the recent past

Noga Zaslavsky*, Karee Garvin*, Charles Kemp, Naftali Tishby, Terry Regier. Journal of Language Evolution, 2022.

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The forms and meanings of grammatical markers support efficient communication

Francis Mollica, Geoffrey Bacon, Noga Zaslavsky, Yang Xu, Terry Regier, Charles Kemp. PNAS, 2021.

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Scalable pragmatic communication via self-supervision

Jennifer Hu, Roger Levy, Noga Zaslavsky. ICML Workshop on Self-Supervised Learning for Reasoning and Perception, 2021.

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Let's talk (efficiently) about us: Person systems achieve near-optimal compression

Noga Zaslavsky*, Mora Maldonado*, Jennifer Culbertson. CogSci, 2021.

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Empirical support for a Rate-Distortion account of pragmatic reasoning

Irene Zhou, Jennifer Hu, Roger P. Levy, Noga Zaslavsky. CogSci, 2021.

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Competition from novel features drives scalar inferences in reference games

Jennifer Hu, Noga Zaslavsky, Roger P. Levy. CogSci, 2021.

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Is it that simple? Linear mapping models in cognitive neuroscience

Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik. bioRxiv, 2021.

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Probing artificial neural networks: insights from neuroscience

Anna A. Ivanova, John Hewitt, Noga Zaslavsky. ICLR Brain2AI Workshop, 2021.

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Crosslinguistic patterns in person systems reflect efficient coding

Mora Maldonado*, Noga Zaslavsky*, Jennifer Culbertson. CUNY, 2021.

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A Rate–Distortion view of human pragmatic reasoning

Noga Zaslavsky, Jennifer Hu, Roger Levy. SCiL, 2021.

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Bayesian Approaches to Color Category Learning

Thomas Griffiths, Noga Zaslavsky. Encyclopedia of Color Science and Technology, 2021.

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Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction

Tiwalayo Eisape, Noga Zaslavsky, Roger Levy. CoNLL, 2020.

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A Rate–Distortion view of human pragmatic reasoning

Noga Zaslavsky, Jennifer Hu, Roger Levy. arXiv preprint, 2020.

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Toward human-like object naming in artificial neural systems

Tiwalayo Eisape, Roger Levy, Joshua Tenenbaum, Noga Zaslavsky. BAICS @ ICLR, 2020.

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Emergence of pragmatic reasoning from least-effort optimization

Noga Zaslavsky, Jennifer Hu, Roger Levy. EvoLang XIII, 2020.

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Information-Theoretic Principles in the Evolution of Semantic Systems

Noga Zaslavsky. PhD Thesis, The Hebrew University, 2020.

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Deterministic annealing and the evolution of Information Bottleneck representations

Noga Zaslavsky, Naftali Tishby. Technical report, 2019.

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Evolution and efficiency in color naming: The case of Nafaanra

Noga Zaslavsky*, Karee Garvin*, Charles Kemp, Naftali Tishby, Terry Regier. CogSci, 2019.

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Semantic categories of artifacts and animals reflect efficient coding

Noga Zaslavsky, Terry Regier, Naftali Tishby, Charles Kemp. CogSci, 2019.

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Communicative need in color naming

Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier. CNP, 2019.

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Color naming reflects both perceptual structure and communicative need

Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier. topiCS, 2019.

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Efficient compression in color naming and its evolution

ELSC Prize for Outstanding Publication

Noga Zaslavsky, Charles Kemp, Terry Regier, Naftali Tishby. PNAS, 2018.

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Color naming reflects both perceptual structure and communicative need (earlier version)

Best paper award for computational modeling of language

Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier. CogSci, 2018.

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Teasing apart the representational spaces of ANN language models to discover key axes of model-to-brain alignment

Evidence for a language-independent conceptual representation of pronominal referents

Alignment of ANN Language Models with Humans After a Developmentally Realistic Amount of Training

Generalization and Translatability in Emergent Communication via Informational Constraints

Trading off Utility, Informativeness, and Complexity in Emergent Communication

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

Towards Human-Agent Communication via the Information Bottleneck Principle

Teasing apart models of pragmatics using optimal reference game design

The emergence of discrete and systematic communication in a continuous signal-meaning space

The evolution of color naming reflects pressure for efficiency: Evidence from the recent past

The forms and meanings of grammatical markers support efficient communication

Scalable pragmatic communication via self-supervision

Let's talk (efficiently) about us: Person systems achieve near-optimal compression

Empirical support for a Rate-Distortion account of pragmatic reasoning

Competition from novel features drives scalar inferences in reference games

Is it that simple? Linear mapping models in cognitive neuroscience

Probing artificial neural networks: insights from neuroscience

Crosslinguistic patterns in person systems reflect efficient coding

A Rate–Distortion view of human pragmatic reasoning

Bayesian Approaches to Color Category Learning

Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction

A Rate–Distortion view of human pragmatic reasoning

Toward human-like object naming in artificial neural systems

Emergence of pragmatic reasoning from least-effort optimization

Information-Theoretic Principles in the Evolution of Semantic Systems

Deterministic annealing and the evolution of Information Bottleneck representations

Evolution and efficiency in color naming: The case of Nafaanra

Semantic categories of artifacts and animals reflect efficient coding

Communicative need in color naming

Color naming reflects both perceptual structure and communicative need

Efficient compression in color naming and its evolution

Color naming reflects both perceptual structure and communicative need (earlier version)

Efficient human-like semantic representations via the Information Bottleneck principle

Efficient encoding of motion is mediated by gap junctions in the fly visual system

Early motion processing circuit uses gap junctions to achieve efficient stimuli encoding

Deep learning and the Information Bottleneck principle