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We use language to communicate our thoughts. But is language merely the expression of thoughts, which are themselves produced by other, nonlinguistic parts of our minds? Or does language play a more transformative role in human cognition, allowing us to have thoughts that we otherwise could (or would) not have? Recent developments in artificial intelligence (AI) and cognitive science have reinvigorated this old question. We argue that language may hold the key to the emergence of both more general AI systems and central aspects of human intelligence. We highlight two related properties of language that make it such a powerful tool for developing domain-general abilities. First, language offers compact representations that make it easier to represent and reason about many abstract concepts (e.g., exact numerosity). Second, these compressed representations are the iterated output of collective minds. In learning a language, we learn a treasure trove of culturally evolved abstractions. Taken together, these properties mean that a sufficiently powerful learning system exposed to language—whether biological or artificial—learns a compressed model of the world, reverse engineering many of the conceptual and causal structures that support human (and human-like) thought. 

Language is often regarded as a de ning trait of our species, but what are its core properties? In 1960, Hockett published ‘The origin of speech’ enumerating 13 de- sign features presumed to be common to all languages, and which, taken to- gether, separate language from other communication systems. Here. we review which features still hold true in light of new evidence from cognitive science, lin- guistics, animal cognition, and anthropology, and demonstrate how a revised un- derstanding of language highlights three core aspects: that language is inherently multimodal and semiotically diverse; that it functions as a tool for se- mantic, pragmatic, and social inference, as well as facilitating categorization; and that the processes of interaction and transmission give rise to central design features of language. 

It is often assumed that how we talk about the world matters a great deal. This is one reason why conceptual engineers seek to improve our linguistic practices by advocating novel uses of our words, or by inventing new ones altogether. A core idea shared by conceptual engineers is that by changing our language in this way, we can reap all sorts of cognitive and practical benefits, such as improving our theorizing, combating hermeneutical injustice, or promoting social emancipation. But how do changes at the linguistic level translate into any of these worthwhile benefits? In this paper, we propose the nameability account as a novel answer to this question. More specifically, we argue that what linguistic resources are readily available to us directly affects our cognitive performance on various categorization‐related tasks. Consequently, our performance on such tasks can be improved by making controlled changes to our linguistic resources. We argue that this account supports and extends recent motivations for conceptual engineering, as categorization plays an important role in both theoretical and practical contexts. 

It is commonly assumed that inner speech—the experience of thought as occurring in a natural language—is a human universal. Recent evidence, however, suggests that the experience of inner speech in adults varies from near constant to nonexistent. We propose a name for a lack of the experience of inner speech—anendophasia—and report four studies examining some of its behavioral consequences. We found that adults who reported low levels of inner speech ( N = 46) had lower performance on a verbal working memory task and more difficulty performing rhyme judgments compared with adults who reported high levels of inner speech ( N = 47). Task-switching performance—previously linked to endogenous verbal cueing—and categorical effects on perceptual judgments were unrelated to differences in inner speech. 

In a recent paper, Aceves and Evans computed information and semantic density measures for hun- dreds of languages, and showed that these measures predict the pace and breadth of ideas in com- munication. Here, we summarize their key findings and situate them in a broader debate about the adap- tive nature of language. 

What determines whether two people represent something in a similar way? We examined the role of verbal labels in promoting representational alignment. Across two experiments, three groups of participants sorted novel shapes from two visually dissimilar categories. Prior to sorting, participants in two of the groups were pre-exposed to the shapes using a simple visual matching task designed to reinforce the visual category structure. In one of these groups, participants additionally heard one of two nonsense category labels accompanying the shapes. Exposure to these redundant labels led people to represent the shapes in a more categorical way, which led to greater alignment between sorters. We found this effect of label-induced alignment despite the two categories being highly visually distinct and despite participants in both pre-exposure conditions receiving identical visual experience with the shapes. Experiment 2 replicated this basic result using more even more stringent testing conditions. The results hint at the possibly extensive role that labels may play in aligning people’s mental representations. 

Abstract What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a better predictor than predictability derived from n‐gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross‐linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable.