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We examined the relationship between metaphor comprehension and verbal analogical reasoning in young adults who were either typically developing (TD) or diagnosed with Autism Spectrum Disorder (ASD). The ASD sample was highly educated and high in verbal ability, and closely matched to a subset of TD participants on age, gender, educational background, and verbal ability. Additional TD participants with a broader range of abilities were also tested. Each participant solved sets of verbal analogies and metaphors in verification formats, allowing measurement of both accuracy and reaction times. Measures of individual differences in vocabulary, verbal working memory, and autistic traits were also obtained. Accuracy for both the verbal analogy and the metaphor task was very similar across the ASD and matched TD groups. However, reaction times on both tasks were longer for the ASD group. Additionally, stronger correlations between verbal analogical reasoning and working memory capacity in the ASD group indicated that processing verbal analogies was more effortful for them. In the case of both groups, accuracy on the metaphor and analogy tasks was correlated. A mediation analysis revealed that after controlling for working memory capacity, the inter‐task correlation could be accounted for by the mediating variable of vocabulary knowledge, suggesting that the primary common mechanisms linking the two tasks involve language skills.

 

Analogy is a powerful tool for fostering conceptual understanding and transfer in STEM and other fields. Well‐constructed analogical comparisons focus attention on the causal‐relational structure of STEM concepts, and provide a powerful capability to draw inferences based on a well‐understood source domain that can be applied to a novel target domain. However, analogy must be applied with consideration to students' prior knowledge and cognitive resources. We briefly review theoretical and empirical support for incorporating analogy into education, and recommend five general principles to guide its application so as to maximize the potential benefits. For analogies to be effective, instructors should use well‐understood source analogs and explain correspondences fully; use visuospatial and verbal supports to emphasize shared structure among analogs; discuss the alignment between semantic and formal representations; reduce extraneous cognitive load imposed by analogical comparison; and encourage generation of inferences when students have some proficiency with the material. These principles can be applied flexibly to topics in a wide variety of domains.

 

Despite widespread communication of the health risks associated with the COVID-19 virus, many Americans underestimated its risks and were antagonistic regarding preventative measures. Political partisanship has been linked to diverging attitudes towards the virus, but the cognitive processes underlying this divergence remain unclear. Bayesian models fit to data gathered through two preregistered online surveys, administered before (March 13, 2020, N = 850) and during the first wave (April-May, 2020, N = 1610) of cases in the United States, reveal two preexisting forms of distrust––distrust in Democratic politicians and in medical scientists––that drove initial skepticism about the virus. During the first wave of cases, additional factors came into play, suggesting that skeptical attitudes became more deeply embedded within a complex network of auxiliary beliefs. These findings highlight how mechanisms that enhance cognitive coherence can drive anti-science attitudes. 

Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development. 

When a group member commits wrongdoing, people sometimes assign responsibility and blame not only to the wrongdoer but also to other members of the same group. We examined such assignment of collective responsibility in the context of exploitation of one family by another. Participants were recruited from the United States and South Korea, which are known to vary in cultural norms and endorsement of collectivistic values. Participants in both countries rated the degree to which an agent (grandson) should be held responsible for his grandfather’s exploitation of a victimized family, while varying the closeness of familial connection. Participants’ responsibility judgments showed sensitivity to whether the grandson received financial benefit from the wrongdoer and to the perceived closeness between the grandson and the wrongdoer. Korean participants imposed greater responsibility on the agent than did American participants. Implications for understanding the influence of social norms on moral judgments are discussed. 

Understanding abstract relations, and reasoning about various instantiations of the same relation, is an important marker in human cognition. Here we focus on development of understanding for the concept of antonymy. We examined whether four- and five-year-olds (N= 67) are able to complete an analogy task involving antonyms, whether language cues facilitate children’s ability to reason about the antonym relation, and how their performance compares with that of two vector-based computational models. We found that explicit relation labels in the form of a relation phrase (“opposites”) improved performance on the task for five-year-olds but not four-year-olds. Five-year-old (but not four-year-old) children were more accurate for adjective and verb antonyms than for noun antonyms. Two computational models showed substantial variability in performance across different lexical classes, and in some cases fell short of children’s accuracy levels. These results suggest that young children acquire a solid understanding of the abstract relation of opposites, and can generalize it to various instantiations across different lexical classes. These developmental results challenge relation models based on vector semantics, and highlight the importance of examining performance across different parts of speech. 

We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems. 

Using poetic metaphors in the Serbian language, we identified systematic variations in the impact of fluid and crystalized intelligence on comprehen-sion of metaphors that varied in rated aptness and familiarity. Overall, comprehension scores were higher for metaphors that were high rather than low in aptness, and high rather than low in familiarity. A measure of crystalized intelligence was a robust predictor of comprehension across the full range of metaphors, but especially for those that were either relatively unfamiliar or more apt. In contrast, individual differences associated with fluid intelligence were clearly found only for metaphors that were low in aptness. Superior verbal knowledge appears to be particularly important when trying to find meaning in novel metaphorical expressions, and also when exploring the rich interpretive potential of apt metaphors. The broad role of crystalized intelligence in metaphor comprehension is consistent with the view that metaphors are largely understood using semantic integration processes continuous with those that operate in understanding literal language.