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Barsalou (1999) proposes that conceptual knowledge is represented by mental simulations containing perceptual information derived from actual experiences. Although a substantial number of studies have provided evidence consistent with this view in native language comprehension, it remains unclear whether the non-native language comprehension processes also include mental simulations. The current study successfully replicates the shape match effect in sentence-picture verification (Zwaan et al., 2002) for non-native English language comprehenders, indicating native-like visual simulations. In addition, participants displayed better delayed recognition memory when the shape of the depicted objects matched the shape that was implied by the sentence than when it did not, suggesting that visual simulations were generated spontaneously in naturalistic non-native language comprehension. Additional correlational analyses revealed no relationship between English proficiency and the size of the match effect., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty. Specifically, we use 37 contemporary transformer language models to calculate the contextual probability of stimuli from 6 experimental studies of the N400, and test whether N400 amplitude is best predicted by a linear, logarithmic, super-logarithmic, or sub-logarithmic transformation of the probabilities calculated using these language models, as well as combinations of these transformed metrics. We replicate the finding that on some datasets, a combination of linearly and logarithmically-transformed probability can predict N400 amplitude better than either metric alone. In addition, we find that overall, the best single predictor of N400 amplitude is sub-logarithmically-transformed probability, which for almost all language models and datasets explains all the variance in N400 amplitude otherwise explained by the linear and logarithmic transformations. This is a novel finding that is not predicted by any current theoretical accounts, and thus one that we argue is likely to play an important role in increasing our understanding of how the statistical regularities of language impact language comprehension., Competing Interests: Competing Interests: The authors declare no conflict of interests., (© 2024 Massachusetts Institute of Technology.)

Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability. In particular, we used an information-theoretic construct known as surprisal (the negative logarithm of the conditional probability). Contextual semantic similarity was operationalized by using two high-quality co-occurrence-derived vector-based meaning representations for words: GloVe and fastText. The cosine between the vector representation of the sentence frame and final word was used to derive contextual cosine similarity estimates. A series of regression models were constructed, where these variables, along with cloze probability and plausibility ratings, were used to predict single trial N400 amplitudes recorded from healthy adults as they read sentences whose final word varied in its predictability, plausibility, and semantic relationship to the likeliest sentence completion. Statistical model comparison indicated GPT-3 surprisal provided the best account of N400 amplitude and suggested that apparently disparate N400 effects of expectancy, plausibility, and contextual semantic similarity can be reduced to variation in the predictability of words. The results are argued to support predictive coding in the human language network., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2023 Massachusetts Institute of Technology.)

The N400 component of the event-related brain potential is a neural signal of processing difficulty. In the language domain, it is widely believed to be sensitive to the degree to which a given word or its semantic features have been preactivated in the brain based on the preceding context. However, it has also been shown that the brain often preactivates many words in parallel. It is currently unknown whether the N400 is also affected by the preactivations of alternative words other than the stimulus that is actually presented. This leaves a weak link in the derivation chain-how can we use the N400 to understand the mechanisms of preactivation if we do not know what it indexes? This study directly addresses this gap. We estimate the extent to which all words in a lexicon are preactivated in a given context using the predictions of contemporary large language models. We then directly compare two competing possibilities: that the amplitude of the N400 is sensitive only to the extent to which the stimulus is preactivated, and that it is also sensitive to the preactivation states of the alternatives. We find evidence of the former. This result allows for better grounded inferences about the mechanisms underlying the N400, lexical preactivation in the brain, and language processing more generally., Competing Interests: Declaration of competing interest None., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Speakers enjoy considerable flexibility in how they refer to a given referent--referring expressions can vary in their form (e.g., "she" vs. "the cat"), their length (e.g., "the (big) (orange) cat"), and more. What factors drive a speaker's decisions about how to refer, and how do these decisions shape a comprehender's ability to resolve the intended referent? Answering either question presents a methodological challenge; researchers must strike a balance between experimental control and ecological validity. In this paper, we introduce the SCARFS (Spontaneous, Controlled Acts of Reference between Friends and Strangers) Database: a corpus of approximately 20,000 English nominal referring expressions (NREs), produced in the context of a communication game. For each NRE, the corpus lists the concept the speaker was trying to convey (from a set of 471 possible target concepts), formal properties of the NRE (e.g., its length), the relationship between the interlocutors (i.e., friend vs. stranger), and the communicative outcome (i.e., whether the expression was successfully resolved). Researchers from diverse disciplines may use this resource to answer questions about how speakers refer and how comprehenders resolve their intended referent--as well as other fundamental questions about dialogic speech, such as whether and how speakers tailor their utterances to the identity of their interlocutor, how second-degree associations are generated, and the predictors of communicative success., Supplementary Information: The online version contains supplementary material available at 10.1007/s10579-022-09619-y., Competing Interests: Conflicts of interest/Competing interestsThe authors declare no conflict of interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Languages around the world use a recurring strategy to discuss abstract concepts: describe them metaphorically, borrowing language from more concrete domains. We "plan ahead" to the future, "count up" to higher numbers, and "warm" to new friends. Past work has found that these ways of talking have implications for how we think, so that shared systems of linguistic metaphors can produce shared conceptualizations. On the other hand, these systematic linguistic metaphors might not just be the cause but also the effect of shared, non-linguistic ways of thinking. Here, we present a case study of a variety of American English in which a shared, non-linguistic conceptualization of time has become crystallized as a new system of linguistic metaphors. Speakers of various languages, including English, conceptualize time as a lateral timeline, with the past leftward and the future rightward. Until now, this conceptualization has not been documented in the speech of any language. In two studies, we document how members of the U.S. military, but not U.S. civilians, talk about time using conventionalized lateral metaphors (e.g., "move the meeting right" to mean "move the meeting later"). We argue that, under the right cultural circumstances, implicit mental representations become conventionalized metaphors in language., (© 2018 Cognitive Science Society, Inc.)