Previous work has ostensibly shown that readers rapidly adapt to less predictable ambiguity resolutions after repeated exposure to unbalanced statistical input (e.g., a high number of reduced relative-clause garden-path sentences), and that these readers grow to disfavor the a priori more frequent (e.g. main verb) resolution after exposure (Fine, Jaeger, Farmer, & Qian, 2013). However, recent work has failed to replicate effects indicating a penalty for the a priori preferred, more frequent continuation, despite finding a speedup in syntactic repair times after initial exposure to the dispreferred, infrequent structure (Harrington Stack, James, & Watson, 2018). The current study reports three self-paced reading experiments that test whether co-occurring cues (explicit comprehension questions, preceding semantic cues, and font color) help facilitate adaptation to reduced relative/main verb garden-path sentences. Results suggest that readers do not overcome preexisting expectation biases by rapidly adapting to statistically novel linguistic contexts even with convergent probabilistic cues. An emphasis is placed on the difference between syntactic satiation effects and expectation adaptation, the latter of which we argue can only be determined through a penalty for an a priori preferred resolution after repeated exposure to its a priori less-preferred counterpart.
Certain colors are strongly associated with certain adjectives (e.g. red is hot, blue is cold). Some of these associations are grounded in visual experiences like seeing hot embers glow red. Surprisingly, many congenitally blind people show similar color associations, despite lacking all visual experience of color. Presumably, they learn these associations via language. Can we detect these associations in the statistics of language? And if so, what form do they take? We apply a projection method to word embeddings trained on corpora of spoken and written text to identify color-adjective associations as they are represented in language. We show that these projections are predictive of color-adjective ratings collected from blind and sighted people, and that the effect size depends on the training corpus. Finally, we examine how color-adjective associations might be represented in language by training word embeddings on corpora from which various sources of color-semantic information are removed.
We can easily evaluate similarities between concepts within semantic domains, e.g. doctor and nurse, or violin and piano. Here, we show that people are also able to evaluate similarities across domains, e.g. aligning doctors with pianos and nurses with violins. We argue that understanding how people do this is important for understanding conceptual organization and the ubiquity of metaphorical language. We asked people to answer questions of the form ‘If a nurse were an animal, they would be a(n) …’ (Experiments 1 and 2) and asked them to explain the basis for their response (Experiment 1). People converged to a surprising degree (e.g. 20% answered ‘cat’). In Experiment 3, we presented people with cross-domain mappings of the form ‘If a nurse were an animal, they would be a cat’ and asked them to indicate how good each mapping was. The results showed that the targets people chose and their goodness ratings of a given response were predicted by similarity along abstract semantic dimensions such as valence, speed and genderedness. Reliance on such dimensions was also the most common explanation for their responses. Altogether, we show that people can evaluate similarity between very different domains in predictable ways, suggesting either that seemingly concrete concepts are represented along relatively abstract dimensions (e.g. weak–strong) or that they can be readily projected onto these dimensions. This article is part of the theme issue ‘Concepts in interaction: social engagement and inner experiences’.
We can easily evaluate similarities between concepts within semantic domains, e.g., doctor and nurse, or violin and piano. Here, we show that people are also able to evaluate similarities across domains, e.g., aligning doctors with pianos and nurses with violins. We argue that understanding how people do this is important for understanding conceptual organization and the ubiquity of metaphorical language. We asked people to answer questions of the form "If a nurse were an animal, they would be a(n)…" (Experiment 1 and 2), and asked them to explain the basis for their response (Experiment 1). People converged to a surprising degree (e.g., 20% answered "cat"). In Experiment 3, we presented people with cross-domain mappings of the form "If a nurse were an animal, they would be a cat” and asked them to indicate how good each mapping was. The results showed that the targets people chose and their goodness ratings of a given response were predicted by similarity along abstract semantic dimensions such as valence, speed, and genderedness. Reliance on such dimensions was also the most common explanation for their responses. Altogether, we show that people can evaluate similarity between very different domains in predictable ways, suggesting that either seemingly concrete concepts are represented along relatively abstract dimensions (e.g., weak-strong) or that they can be readily projected onto these dimensions.
Across languages, words carve up the world of experience in different ways. For example, English lacks an equivalent to the Chinese superordinate noun tiáowèipǐn, which is loosely translated as “ingredients used to season food while cooking.” Do such differences matter? A conventional label may offer a uniquely effective way of communicating. On the other hand, lexical gaps may be easily bridged by the compositional power of language. After all, most of the ideas we want to express do not map onto simple lexical forms. We conducted a referential Director/Matcher communication task with adult speakers of Chinese and English. Directors provided a clue that Matchers used to select words from a word grid. The three target words corresponded to a superordinate term (e.g., beverages) in either Chinese or English but not both. We found that Matchers were more accurate at choosing the target words when their language lexicalized the target category. This advantage was driven entirely by the Directors’ use/non-use of the intended superordinate term. The presence of a conventional superordinate had no measurable effect on speakers’ within- or between-category similarity ratings. These results show that the ability to rely on a conventional term is surprisingly important despite the flexibility languages offer to communicate about non-lexicalized categories.
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