Research

2023

1. Typing time PWI: A scalable paradigm for studying lexical production

   Shiva Upadhye, and Richard Futrell

   In Proceedings of the 45th Annual Meeting of the Cognitive Science Society.2023.

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   Lexical production research has relied extensively on in-person naming experiments such as Picture Word Interference (PWI). While the interference effects observed in the standard paradigm have been well-attested, PWI experiments have traditionally been underpowered, owing to limitations associated with large-scale data collection. In this study, we validate a scalable, typing time-based PWI paradigm that can be deployed on the internet, and enables large-scale replications of the interference effects observed in the spoken modality. We also propose an automated response coding process that incorporates production errors such as incorrect responses and restarts, and can be leveraged to illuminate aspects of response conflict and correction processes in incremental production.

2. Bridging production and comprehension: Toward an integrated computational model of error correction

   Shiva Upadhye*, Jiaxuan Li*, and Richard Futrell

   In Proceedings of the Society for Computation in Linguistics (to appear). Amherst, USA, 2023.

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   Error correction in production and comprehension has traditionally been studied separately. In real-time communication, however, correction may not only depend on speaker or comprehender-internal preferences, but also the interlocutors’ knowledge of each other’s strategies. We present an integrated computational framework for error correction in both production and comprehension systems. Modeling error correction as Bayesian inference, we pro- pose that both speaker and comprehender’s correction strategies are influenced by their prior expectations about the intended message and their knowledge of a noise monitoring model. Our results indicate that speakers and compre- henders tend to weigh phonological and seman- tic cues differently, and these different strate- gies illuminate the asymmetries and potential interactions between the two systems.

2022

1. Information-theoretic analysis of disfluencies in speech

   Shiva Upadhye, and Richard Futrell

   In NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems (InfoCog@NeurIPS). New Orleans, USA, 2022.

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   This study proposes and examines an information-theoretic measure of planning in incremental speech production, and investigates the effects of planning, predictability, and interference-based measures on distractor selection and production in lexical substitution errors. We then present a rate-distortion theoretic model of speech production that explicates how these factors affect the production of lexical substitution errors.

2. The typing modality preserves semantic interference effects in Picture Word Interference

   Shiva Upadhye, and Richard Futrell

   In 28th Annual Conference on Architectures and Mechanisms of Language Processing (AMLaP). York, UK, 2022.

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2020

1. Predicting Reference: What do Language Models Learn about Discourse Models?

   Shiva Upadhye, Leon Bergen, and Andrew Kehler

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Virtual, 2020.

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   Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability. We address this question by drawing on a rich psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. The results reveal that, for the most part, the prediction behavior of neural language models does not resemble that of human language users.

   @inproceedings{upadhye-etal-2020-predicting,
     address = {Virtual},
     author = {Upadhye, Shiva and Bergen, Leon and Kehler, Andrew},
     booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
     doi = {10.18653/v1/2020.emnlp-main.70},
     month = nov,
     pages = {977--982},
     publisher = {Association for Computational Linguistics},
     read = {0},
     title = {Predicting Reference: What do Language Models Learn about Discourse Models?},
     url = {https://aclanthology.org/2020.emnlp-main.70},
     year = {2020},
     publication = {Conference Proceedings}
   }