Sathvik Nair

I'm a PhD student in Linguistics at the University of Maryland, advised by Profs. Philip Resnik and Colin Phillips. I work on computational psycholinguistics and am affiliated with the Computational Linguistics and Information Processing (CLIP) Lab, and am part of the broader language science community at UMD. More on my collaborators below!

Originally from the Bay Area, I graduated from UC Berkeley with bachelor's degrees in Cognitive Science and Computer Science, where I received the Glushko Prize for Outstanding Undergraduate Research in Cognitive Sciences. There, I closely collaborated with Dr. Stephan Meylan on projects in Profs. Mahesh Srinivasan and Tom Griffiths' groups. Afterwards, I worked as a software engineer at Amazon Web Services in Boston. I generally accept he/him pronouns in English. In languages with gender agreement, masculine and gender neutral forms are fine with me.

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I focus on computational models of prediction in sentence processing. I'm interested in the interaction between language-specific information and domain-general cognitive processes like memory. Recently, I've been looking at ways in which experimental data from humans compares with predictions made by large language models, in the service of developing more cognitively plausible models of language processing. I'm also interested how cognitive modeling can interact with other NLP applications, such as interpetability and computational social sciences. In general, I'm excited about various topics at the intersection of language, computation, and cognition, and am always happy to connect regarding either of these interests.


Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?
Sathvik Nair, Philip Resnik
Findings of EMNLP 2023
Proceedings Paper

An important assumption that comes with using LLMs on psycholinguistic data has gone unverified. LLM-based predictions are based on subword tokenization, not decomposition of words into morphemes. Does that matter? We carefully test this by comparing surprisal estimates using orthographic, morphological, and BPE tokenization against reading time data. Our results replicate previous findings. In the aggregate, predictions using BPE tokenization do not suffer relative to morphological and orthographic segmentation. Finer-grained analyses show differences between BPE and morphological surprisal, suggesting that the effects of subword tokenization should be taken seriously in cognitive research, and provide promising evidence for using morphological surprisal in further research.

How far does probability take us when measuring psycholinguistic fit? Evidence from substitution illusions and speeded cloze data
Sathvik Nair, Shohini Bhattasali, Philip Resnik Colin Phillips
Poster Presentation at the 36th Annual Conference on Human Sentence Processing, 2023
Abstract | Poster

When asked How many animals of each kind did Moses take on the ark?, humans often respond with two instead of detecting issues with the question. This effect is known as a substitution illusion (canonically a Moses illusion, but we refer to them this way to emphasize how they can apply in various contexts). We used measures from NLP techniques on experimental data collected by Muller (2022) to address whether people's selectivity to the illusion is better explained by semantic similarity of the correct word and substituted word (distributional vector-space representations), or the substituted word's relationship to the context(masked language modeling). On a separate speeded cloze task (Staub et al, 2015), we found that language model probability corresponded strongly with cloze probability, but not with reaction time, which is seen as a stronger measure of lexical predictability in context. This work reveals some limitations of applying language models to psycholinguistic data.

Contextualized Word Embeddings Capture Human-Like Relations Between English Word Senses
Sathvik Nair, Mahesh Srinivasan, Stephan Meylan
Oral Presentation for Cognitive Aspects of the Lexicon workshop(CogALex VI) at International Conference on Computational Linguistics (COLING), 2020
Undergraduate Honors Thesis, advised by Dr. Meylan, Prof. Srinivasan, and Prof. Steven Piantadosi, received the Glushko Prize for Outstanding Undergraduate Research in Cognitive Sciences.
Paper | Thesis | Code and Data

We investigated whether recent advances in NLP (specifically the Transformer-based neural network model BERT), are able to capture human-like distinctions between meanings of the same word, such as polysemy and homonymy. We collected human judgements of the relatedness of selected WordNet senses for 32 English words from a two-dimensional spatial arrangement task, and compare them with relatedness according to BERT vectors for these corresponding senses in the SemCor corpus. We demonstrated participants’ judgments of the relatedness between senses are correlated with distances between senses in the BERT embedding space, and that BERT encodes homonymous sense relations closer to human judgements than polysemous ones.

Evaluating Models of Robust Word Recognition with Serial Reproduction.
Stephan Meylan, Sathvik Nair, Tom Griffiths
Published in May 2021 issue of Cognition journal
Journal Article | Preprint (full text)

We compared how several probabilistic generative language models, such as n-grams, probabilistic context free grammars (PCFGs), and neural networks, capture human linguistic expectations in a web-based serial reproduction task, in which in which participants try to repeat sentences said by other participants, similar to a game of "Telephone." We found that models that make use of preceding context, especially those with abstract representations of linguistic structure, best predict changes participants made when trying to reproduce utterances in the experiment. I contributed to designing and implementing parts of the experimental interface, extracting probabilities under PCFGs, modeling which words in utterances were most likely to change under the models, and revising the final paper.

Collaborators, Mentors, Friends, and other Co-Conspirators

Research is never done in a vacuum, and publications don't reflect everyone who's intellectually influenced me. Here are some of those people.



At UC Berkeley:


Other projects (not just academic) and information.

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