Does Vocabulary Help Structure the Mind?

Lupyan, Gary; Zettersten, Martin

doi:10.1002/9781119684527.ch6

Cited by 18 publications

(10 citation statements)

References 54 publications

(36 reference statements)

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“…This is in line with recent work (e.g. Lupyan, 2012;Lupyan & Zettersten, 2020) suggesting that problem difficulty increases as description length increases and as nameability (the ease with which a concept can be named) decreases; and that language biases perceptual processing towards certain concepts. Furthermore, we find a negative correlation (r=-0.50) between average description length and average accuracy per task.…”

Section: Resultssupporting

confidence: 91%

“…If so, this poses a challenge for existing LOT-based theories, as it is not clear how the semantics of existing conceptual knowledge could be easily integrated into a minimal LOT. One alternative account that is more parsimonious with our data-especially our findings regarding the relationship between description length and difficulty-is that hypothesis generation uses natural language as a scaffold for generating hypotheses, either instead of or in addition to a symbolic language of thought (Carruthers, 2002;Lupyan & Zettersten, 2020;Andreas, Klein, & Levine, 2017).…”

Section: Discussionsupporting

confidence: 50%

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Fast and flexible: Human program induction in abstract reasoning tasks

Johnson¹,

Vong²,

Lake³

et al. 2021

Preprint

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The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet ( 2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerable variation, our results showed that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 80% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability within the action sequences during the generation process, the natural language descriptions to describe the transformations for each task, and the errors people made. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution. Future modeling work could incorporate these findings, potentially by connecting the natural language descriptions we collected here to the underlying semantics of ARC.

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Section: Resultssupporting

confidence: 91%

Section: Discussionsupporting

confidence: 50%

Fast and flexible: Human program induction in abstract reasoning tasks

Johnson¹,

Vong²,

Lake³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…It is possible that the larger magnitude of the difference observed here is due to the fact that we tested cultures with large differences in color-term use, and therefore the present results might generalize only to language pairs from vastly different cultures. Both the Whorfian (e.g., Lupyan & Zettersten, 2021) and efficiency-based (e.g., Gibson et al, 2019;Mahowald et al, 2020) approaches predict changes in a native language upon learning a second language. However, a key difference between these approaches is the mechanism behind 4 (green-blue, yellow-brown, red-purple), as well as the reverse of each (i.e., blue-green, brown-yellow, purple-red) across all language groups.…”

Section: Discussionmentioning

confidence: 99%

Concepts Are Restructured During Language Contact: The Birth of Blue and Other Color Concepts in Tsimane’-Spanish Bilinguals

Malik-Moraleda,

Mahowald,

Conway

et al. 2023

Psychol Sci

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Words and the concepts they represent vary across languages. Here we ask if mother-tongue concepts are altered by learning a second language. What happens when speakers of Tsimane’, a language with few consensus color terms, learn Bolivian Spanish, a language with more terms? Three possibilities arise: Concepts in Tsimane’ may remain unaffected, or they may be remapped, either by Tsimane’ terms taking on new meanings or by borrowing Bolivian-Spanish terms. We found that adult bilingual speakers ( n = 30) remapped Tsimane’ concepts without importing Bolivian-Spanish terms into Tsimane’. All Tsimane’ terms become more precise; for example, concepts of monolingual shandyes and yụshñus (~green or blue, used synonymously by Tsimane’ monolinguals; n = 71) come to reflect the Bolivian-Spanish distinction of verde (~green) and azul (~blue). These results show that learning a second language can change the concepts in the first language.

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“…One explanation for the current findings is that labels may be particularly powerful, compact representations of hypotheses about to-be-learned structures (Clark, 1998;Gentner, 2016;Lupyan & Zettersten, 2021). These compact representations (e.g., "alien A likes to eat red things") make it easier for learners to formulate hypotheses about category membership ("it is about the red segment" vs. "it is about that pinkish-purplish segment").…”

Section: Why Are Categories With More Nameable Features Easier To Learn?mentioning

confidence: 85%

Nameability Supports Rule-based Category Learning in Children and Adults

Zettersten¹,

Bredemann²,

Kaul³

et al. 2022

Preprint

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Learning abstract, rule-based categories is crucial to children’s development, from learning how to maneuver through the world (e.g., knowing to stop at a red light) to abstract reasoning (e.g., learning that a three-sided shape is a triangle). How do children learn rule-based categories? Past work with adults suggests that verbal labels may support novel category induction by providing compact hypotheses about category-relevant features. We asked whether children also make use of accessible labels to guide category learning. We tested children’s (N = 97; 4 - 6 years of age) and adults’ (N = 90) ability to learn a novel category in which the colors that perfectly predicted category membership were either easy to name (e.g., “red” and “brown”) or more difficult to name (e.g., “mauve” and “chartreuse”). Both children and adults showed better learning when the categories were composed of more nameable color features compared to less nameable color features, though the effect of nameability on category learning was stronger for adults than children. Children’s success in learning categories with difficult-to-name features was related to knowledge of difficult-to-name colors. Together, these results provide evidence that rule-based category learning may be supported by the emerging ability to form verbal hypotheses about category membership (e.g., “the red ones belong to category A”).

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Does Vocabulary Help Structure the Mind?

Cited by 18 publications

References 54 publications

Fast and flexible: Human program induction in abstract reasoning tasks

Fast and flexible: Human program induction in abstract reasoning tasks

Concepts Are Restructured During Language Contact: The Birth of Blue and Other Color Concepts in Tsimane’-Spanish Bilinguals

Nameability Supports Rule-based Category Learning in Children and Adults

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