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When do people use knowledge about the world in order to comprehend language? We asked whether pronoun resolution decisions are influenced by knowledge about physical plausibility. Results showed that referents which are more physically plausible in described events were more likely to be selected as antecedents of ambiguous pronouns, implying that resolution decisions were driven by physical inference. An alternative explanation is that these decisions were driven instead by distributional word knowledge. We tested this by including predictions of a statistical language model (BERT) and found that physical plausibility explained variance on top of the statistical language model predictions. This indicates that at least part of people's pronoun resolution judgments comes from knowledge about the world and not the word. This result constrains psycholinguistic models of comprehension—world knowledge must influence propositional interpretation—and raises the broader question of how non-linguistic physical inference processes are incorporated during comprehension.

When do people use knowledge about the world in order to comprehend language? We asked whether pronoun resolution decisions are influenced by knowledge about physical plausibility. Results showed that referents which are more physically plausible in described events were more likely to be selected as antecedents of ambiguous pronouns, implying that resolution decisions were driven by physical inference. An alternative explanation is that these decisions were driven instead by distributional word knowledge. We tested this by including predictions of a statistical language model (BERT) and found that physical plausibility explained variance on top of the statistical language model predictions. This indicates that at least part of people's pronoun resolution judgments comes from knowledge about the world and not the word. This result constrains psycholinguistic models of comprehension—world knowledge must influence propositional interpretation—and raises the broader question of how non-linguistic physical inference processes are incorporated during comprehension.

How do people ask questions to zero in on a correct answer? Although we can formally define an optimal query to maximize information gain, algorithms for finding this optimal guess may impose large resource costs in space (memory) and time (computation). To understand how people trade off the information gain and the computational difficulty of choosing the ideal query, we turned to a large dataset of 380,000 guesses made during a number-guessing game with Amazon Alexa. We analyzed whether the arithmetic difficulty of following the optimal strategy predicts how far a guess deviates from theoretically optimal query. We find that when memory load is higher, and when more arithmetic operations need to be performed, human guesses deviate more from the most informative query. These results suggest human computational resource constraints limit how people seek out informative questions.

How do people ask questions to zero in on a correct answer? Although we can formally define an optimal query to maximize information gain, algorithms for finding this optimal guess may impose large resource costs in space (memory) and time (computation). To understand how people trade off the information gain and the computational difficulty of choosing the ideal query, we turned to a large dataset of 380,000 guesses made during a number-guessing game with Amazon Alexa. We analyzed whether the arithmetic difficulty of following the optimal strategy predicts how far a guess deviates from theoretically optimal query. We find that when memory load is higher, and when more arithmetic operations need to be performed, human guesses deviate more from the most informative query. These results suggest human computational resource constraints limit how people seek out informative questions.