Claire Bergey

All Publications

• Relevant answers to polar questions. Philosophical transactions of the Royal Society of London. Series B, Biological sciences Hawkins, R. D., Tsvilodub, P., Bergey, C. A., Goodman, N. D., Franke, M. 2025; 380 (1932): 20230505

  Abstract

  People often provide answers that go beyond what a question literally asks, but it has been difficult to pin down what makes some answers more relevant than others. Here, we introduce Pragmatic Reasoning In Overinformative Responses to Polar Questions (PRIOR-PQ), a probabilistic cognitive model formalizing how people use theory of mind (ToM) to produce and interpret relevantly overinformative answers to yes-no questions. Specifically, PRIOR-PQ grounds the pragmatics of question answering in inferences about the underlying goal that motivated the questioner to ask the given question as opposed to a different question. We evaluate our probabilistic model against human answering behaviour elicited in three case studies of increasing complexity, demonstrating its ability to predict nuanced patterns of relevance better than existing models, including state-of-the-art large language models. We also show how the goal-sensitive reasoning instantiated in our probabilistic model motivates a novel chain-of-thought prompting method allowing language models to approach more human-like performance. This work illuminates the mechanistic role of ToM in the pragmatics of question-answer exchanges, bridging formal semantics, cognitive science and artificial intelligence. Our findings have implications for developing more socially grounded dialogue systems and highlight the importance of integrating explanatory cognitive models with machine learning approaches.This article is part of the theme issue 'At the heart of human communication: new views on the complex relationship between pragmatics and Theory of Mind'.

  View details for DOI 10.1098/rstb.2023.0505

  View details for PubMedID 40808460

• Peekbank: An open, large-scale repository for developmental eye-tracking data of children's word recognition. Behavior research methods Zettersten, M., Yurovsky, D., Xu, T. L., Uner, S., Tsui, A. S., Schneider, R. M., Saleh, A. N., Meylan, S. C., Marchman, V. A., Mankewitz, J., MacDonald, K., Long, B., Lewis, M., Kachergis, G., Handa, K., deMayo, B., Carstensen, A., Braginsky, M., Boyce, V., Bhatt, N. S., Bergey, C. A., Frank, M. C. 2022

  Abstract

  The ability to rapidly recognize words and link them to referents is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We first present how we created the database, its features and structure, and associated tools for processing and accessing infant eye-tracking datasets. Using these tools, we then work through two illustrative examples to show how researchers can use Peekbank to interrogate theoretical and methodological questions about children's developing word recognition ability.

  View details for DOI 10.3758/s13428-022-01906-4

  View details for PubMedID 36002623