Noga Zaslavsky

Noga Zaslavsky
Massachusetts Institute of Technology | MIT · Department of Brain and Cognitive Sciences

PhD

About

21
Publications
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669
Citations

Publications

Publications (21)
Article
It has been proposed that semantic systems evolve under pressure for efficiency. This hypothesis has so far been supported largely indirectly, by synchronic cross-language comparison, rather than directly by diachronic data. Here, we directly test this hypothesis in the domain of color naming, by analyzing recent diachronic data from Nafaanra, a la...
Article
Significance Grammatical marking of features such as number, tense, and evidentiality varies widely across languages. Despite this variation, we show that grammatical markers support efficient information transfer from speakers to listeners. We apply a formal model of communication to data from dozens of languages and find that grammatical marking...
Preprint
Full-text available
It has been proposed that semantic systems evolve under pressure for efficiency. This hypothesis has so far been supported largely indirectly, by synchronic cross-language comparison, rather than directly by diachronic data. Here, we directly test this hypothesis in the domain of color naming, by analyzing recent diachronic data from Nafaanra, a la...
Preprint
Full-text available
Models of context-sensitive communication often use the Rational Speech Act framework (RSA; Frank & Goodman, 2012), which formulates listeners and speakers in a cooperative reasoning process. However, the standard RSA formulation can only be applied to small domains, and large-scale applications have relied on imitating human behavior. Here, we pro...
Preprint
Systems of personal pronouns (e.g., 'you' and 'I') vary widely across languages, but at the same time not all possible systems are attested. Linguistic theories have generally accounted for this in terms of strong grammatical constraints, but recent experimental work challenges this view. Here, we take a novel approach to understanding personal pro...
Preprint
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A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems. One such tool is probes, i.e., supervised models that relate features of interest to activation patterns arising in biological or artificial neural networks. Neuroscience has paved the way in using...
Preprint
Full-text available
Advances in cognitive neuroscience are often accompanied by an increased complexity in the methods we use to uncover new aspects of brain function. Recently, many studies have started to use large feature sets to predict and interpret brain activity patterns. Of crucial importance in this paradigm is the mapping model, which defines the space of po...
Preprint
Functionalist accounts of language suggest that forms are paired with meanings in ways that support efficient communication. Previous work on grammatical marking suggests that word forms have lengths that enable efficient production, and work on the semantic typology of the lexicon suggests that word meanings represent efficient partitions of seman...
Article
Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing. However, past work has also suggested dissociations between corpus probabilities and human next-word predictions. Here we evaluate several state-of-theart language models for their match to hu...
Preprint
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What computational principles underlie human pragmatic reasoning? A prominent approach to pragmatics is the Rational Speech Act (RSA) framework, which formulates pragmatic reasoning as probabilistic speakers and listeners recursively reasoning about each other. While RSA enjoys broad empirical support, it is not yet clear whether the dynamics of su...
Thesis
Full-text available
Across the world, languages enable their speakers to communicate effectively using relatively small lexicons compared to the complexity of the environment. How do word meanings facilitate this ability across languages? The forces that govern how languages assign meanings to words, i.e., human semantic systems, have been debated for decades. Recentl...
Preprint
Full-text available
It has been argued that semantic categories across languages reflect pressure for efficient communication. Recently, this idea has been cast in terms of a general information-theoretic principle of efficiency, the Information Bottleneck (IB) principle, and it has been shown that this principle accounts for the emergence and evolution of named color...
Article
Full-text available
Colour naming across languages has traditionally been held to reflect the structure of colour perception. At the same time, it has often, and increasingly, been suggested that colour naming may be shaped by patterns of communicative need. However, much remains unknown about the factors involved in communicative need, how need interacts with percept...
Article
Gibson et al. (2017) argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support more precise communication about warm colors than cool colors, and that the objects we talk about tend to be warm‐colored rather than cool‐colored. Here, we present new a...
Article
Full-text available
We derive a principled information-theoretic account of cross-language semantic variation. Specifically, we argue that languages efficiently compress ideas into words by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of the lexicon. We test this proposal in the domain of color naming and show that (i) color...
Preprint
Full-text available
Gibson et al. (2017) argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support more precise communication about warm colors than cool colors, and that the objects we talk about tend to be warm-colored rather than cool-colored. Here, we present new a...
Preprint
Maintaining efficient semantic representations of the environment is a major challenge both for humans and for machines. While human languages represent useful solutions to this problem, it is not yet clear what computational principle could give rise to similar solutions in machines. In this work we propose an answer to this open question. We sugg...
Article
Full-text available
Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual proces...
Article
Full-text available
Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain f...

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Projects (3)
Project
We need better understanding of Deep Neural Networks: Why they work so well? What are the problems for which they work well? What determines the optimal architecture (number of layers and their width, connections, etc.)? We believe that by studying the information flows through the network and compare them to the theoretically optimal IB bound we can gain much insight about these questions.