A computational model of language comprehension unites diverse perspectives

When we comprehend language, we convert the acoustic or visual signal that we perceive into an internal representation of meaning. This conversion is complex, and language scientists have worked for decades to understand how it functions. In studying language comprehension, the field has been marked by several sharp theoretical divides. One of these controversies concerns modularity of the component processes of comprehension, such as whether interpretation of a sentence’s syntactic structure proceeds independently from interpretation of meaning and other aspects of language (1), or whether comprehension involves interaction among syntax, meaning, and so on (2). A second debate concerns the relationship between comprehension processes and the temporary memory that is necessary to execute them: does this working memory stem from a dedicated temporary storage system (3), or do language processes themselves create the temporary memories they need (4)? These longstanding debates get at core questions about the nature of human language capacities, but alternative positions have been difficult to distinguish from one another in precise ways. In PNAS, Hahn et al. (5) describe a theory and implemented computational model of central pieces of language comprehension, linking disparate theoretical perspectives in both language comprehension and working memory. Because linguistic signals arrive over time, converting signal to meaning requires integrating earlier and later portions of the signal. For perceivers to understand who did what to whom in the sentence in Fig. 1A, for example, the word dog must be linked to three verbs—the nearby word jumped and the more distant chased and caught. The perceiver must also determine that the squirrel is the object of the chasing and is not doing any jumping, chasing, or catching itself. Although the example in Fig. 1A may not seem much like everyday language, these complex sentences have been a central testing ground of theories of language comprehension, for two reasons. First, sentences with embedded clauses, such as the one in Fig. 1A, have been a cornerstone of generative linguists’ claims for the competence–performance distinction (6, 7), which holds that humans have expansive linguistic knowledge (competence) but more restricted comprehension performance because of constraints on their working memory capacity. Studying these difficult embedded sentences may therefore inform longstanding claims about human language knowledge and use. Second, as Hahn et al. note, computational models provide quantitative predictions of varying levels of comprehension difficulty across these types of sentences (8, 9), yielding insight into the nature of the signal-to-meaning computations that undergird language comprehension more generally. Three historically disparate approaches, shown in Figs. 1 B–D, have all aimed to characterize patterns of comprehension difficulty at different points of complex sentences. Fig. 1B shows the DLT (8), which is foundational to treatment of memory in the Hahn et al. (5) account. In DLT, difficulty of comprehension is tied to limitations in verbal working memory: when words that must be integrated with one another are far apart, working memory demands are higher, and comprehension is more difficult, evidenced by slowed reading. A second key component of the Hahn et al. model is Surprisal Theory (9, 10), shown in Fig. 1C. It ties comprehension difficulty at a particular point in a sentence to uncertainty about what input is coming next. The model predicts upcoming input as a function of what has been encountered in the sentence to that point, guided by knowledge of the likelihood of completions, as estimated via large language corpora. Surprisal Theory and the DLT differ in the role of prior experience in explanations for comprehension difficulty, with Surprisal linking difficulty to prior comprehension experience—difficult sentence regions are ones that violate expectations based on past experience—while the DLT pointing to a direct relationship between distance between integrated words and comprehension difficulty, independent of experience. However, they share a commitment to modularity of syntactic computations. As illustrated in Fig. 1 B and C, both models operate over abstract grammatical categories (noun, verb, etc.) without regard to word meaning. The constraint satisfaction approach (Fig. 1D) rejects isolation of syntax-level processes and argues that interpretation of sentence structure, word meaning, and other aspects of language must be jointly computed (2). Words are massively ambiguous in both meaning and their grammatical categories; for example, all the nouns in the sentence in Fig. 1A (dog, fence, and squirrel) are also used as verbs in English. DLT and Surprisal computations over grammatical category input may be more complicated if words’ grammatical categories must also be disambiguated. Words also exhibit lexico-syntactic regularities, where certain words tend to occur in certain sentence types, so that knowledge of words informs syntax interpretation and vice versa (11).