High-level language brain regions process sublexical regularities.

A network of left frontal and temporal brain regions supports language processing. This "core" language network stores our knowledge of words and constructions as well as constraints on how those combine to form sentences. However, our linguistic knowledge additionally includes information about phonemes and how they combine to form phonemic clusters, syllables, and words. Are phoneme combinatorics also represented in these language regions? Across five functional magnetic resonance imaging experiments, we investigated the sensitivity of high-level language processing brain regions to sublexical linguistic regularities by examining responses to diverse nonwords-sequences of phonemes that do not constitute real words (e.g. punes, silory, flope). We establish robust responses in the language network to visually (experiment 1a, n = 605) and auditorily (experiments 1b, n = 12, and 1c, n = 13) presented nonwords. In experiment 2 (n = 16), we find stronger responses to nonwords that are more well-formed, i.e. obey the phoneme-combinatorial constraints of English. Finally, in experiment 3 (n = 14), we provide suggestive evidence that the responses in experiments 1 and 2 are not due to the activation of real words that share some phonology with the nonwords. The results suggest that sublexical regularities are stored and processed within the same fronto-temporal network that supports lexical and syntactic processes.

Scalar Implicature is Sensitive to Contextual Alternatives.

The quantifier "some" often elicits a scalar implicature during comprehension: "Some of today's letters have checks inside" is often interpreted to mean that not all of today's letters have checks inside. In previous work, Goodman and Stuhlmüller (G&S) proposed a model that predicts that this implicature should depend on the speaker's knowledgeability: If the speaker has only examined some of the available letters (e.g., two of three letters), people are less likely to infer that "some" implies "not all" than if the speaker has examined all of the available letters. G&S also provided behavioral evidence in support of their model. In this paper, we first show that a simple extension of G&S's model (1) predicts G&S's knowledgeability effects, and in addition, (2) predicts that the knowledgeability effect will be reduced when the speaker's usage indicates numeral alternatives are available. We tested the new model's predictions in four preregistered experiments. All experiments supported the first model prediction, replicating G&S's finding of a main effect of the speaker's knowledge. Further, Experiments 2 and 4 supported the second model prediction showing that the words that a speaker tends to use affect the strength of scalar implicature that comprehenders make. In particular, when the speaker has partial knowledge (e.g., has only examined two of three letters), comprehenders think that "some" is more likely to mean "not all" when the speaker also tends to produce number words in similar sentences (e.g., "2 of today's rooms have working smoke detectors."). These results have important ramifications for theories of meaning: the context beyond the sentence (e.g., the speaker's tendency to use particular words) affects the set of alternatives that comprehenders consider when inferring meaning.

An ERP index of real-time error correction within a noisy-channel framework of human communication.

Recent evidence suggests that language processing is well-adapted to noise in the input (e.g., spelling or speech errors, misreading or mishearing) and that comprehenders readily correct the input via rational inference over possible intended sentences given probable noise corruptions. In the current study, we probed the processing of noisy linguistic input, asking whether well-studied ERP components may serve as useful indices of this inferential process. In particular, we examined sentences where semantic violations could be attributed to noise-for example, in "The storyteller could turn any incident into an amusing antidote", where the implausible word "antidote" is orthographically and phonologically close to the intended "anecdote". We found that the processing of such sentences-where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately-was associated with a reduced (less negative) N400 effect and an increased P600 effect, compared to semantic violations which are unlikely to be attributed to noise ("The storyteller could turn any incident into an amusing hearse"). Further, the magnitudes of these ERP effects were correlated with the probability that the comprehender retrieved a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are optimized for dealing with noise in the input, and opens the door to electrophysiologic investigations of the computations that support the processing of imperfect input.

How Efficiency Shapes Human Language.

Cognitive science applies diverse tools and perspectives to study human language. Recently, an exciting body of work has examined linguistic phenomena through the lens of efficiency in usage: what otherwise puzzling features of language find explanation in formal accounts of how language might be optimized for communication and learning? Here, we review studies that deploy formal tools from probability and information theory to understand how and why language works the way that it does, focusing on phenomena ranging from the lexicon through syntax. These studies show how a pervasive pressure for efficiency guides the forms of natural language and indicate that a rich future for language research lies in connecting linguistics to cognitive psychology and mathematical theories of communication and inference.

Color naming across languages reflects color use.

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.

The strategic use of noise in pragmatic reasoning.

We combine two recent probabilistic approaches to natural language understanding, exploring the formal pragmatics of communication on a noisy channel. We first extend a model of rational communication between a speaker and listener, to allow for the possibility that messages are corrupted by noise. In this model, common knowledge of a noisy channel leads to the use and correct understanding of sentence fragments. A further extension of the model, which allows the speaker to intentionally reduce the noise rate on a word, is used to model prosodic emphasis. We show that the model derives several well-known changes in meaning associated with prosodic emphasis. Our results show that nominal amounts of actual noise can be leveraged for communicative purposes.

Nonliteral understanding of number words.

One of the most puzzling and important facts about communication is that people do not always mean what they say; speakers often use imprecise, exaggerated, or otherwise literally false descriptions to communicate experiences and attitudes. Here, we focus on the nonliteral interpretation of number words, in particular hyperbole (interpreting unlikely numbers as exaggerated and conveying affect) and pragmatic halo (interpreting round numbers imprecisely). We provide a computational model of number interpretation as social inference regarding the communicative goal, meaning, and affective subtext of an utterance. We show that our model predicts humans' interpretation of number words with high accuracy. Our model is the first to our knowledge to incorporate principles of communication and empirically measured background knowledge to quantitatively predict hyperbolic and pragmatic halo effects in number interpretation. This modeling framework provides a unified approach to nonliteral language understanding more generally.

Rational integration of noisy evidence and prior semantic expectations in sentence interpretation.

Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Indeed, like many cognitive systems, language processing mechanisms may even be "well designed"--in this case for the task of recovering intended meaning from noisy utterances. In particular, comprehension mechanisms may be sensitive to the types of information that an idealized statistical comprehender would be sensitive to. Here, we evaluate four predictions about such a rational (Bayesian) noisy-channel language comprehender in a sentence comprehension task: (i) semantic cues should pull sentence interpretation towards plausible meanings, especially if the wording of the more plausible meaning is close to the observed utterance in terms of the number of edits; (ii) this process should asymmetrically treat insertions and deletions due to the Bayesian "size principle"; such nonliteral interpretation of sentences should (iii) increase with the perceived noise rate of the communicative situation and (iv) decrease if semantically anomalous meanings are more likely to be communicated. These predictions are borne out, strongly suggesting that human language relies on rational statistical inference over a noisy channel.

A noisy-channel account of crosslinguistic word-order variation.

The distribution of word orders across languages is highly nonuniform, with subject-verb-object (SVO) and subject-object-verb (SOV) orders being prevalent. Recent work suggests that the SOV order may be the default in human language. Why, then, is SVO order so common? We hypothesize that SOV/SVO variation can be explained by language users' sensitivity to the possibility of noise corrupting the linguistic signal. In particular, the noisy-channel hypothesis predicts a shift from the default SOV order to SVO order for semantically reversible events, for which potential ambiguity arises in SOV order because two plausible agents appear on the same side of the verb. We found support for this prediction in three languages (English, Japanese, and Korean) by using a gesture-production task, which reflects word-order preferences largely independent of native language. Other patterns of crosslinguistic variation (e.g., the prevalence of case marking in SOV languages and its relative absence in SVO languages) also straightforwardly follow from the noisy-channel hypothesis.

Speaker knowledge influences the comprehension of pragmatic inferences.

Inferring what speakers mean from what they say requires consideration of what they know. For instance, depending on the speaker's level of expertise, uttering Some squirrels hibernate can imply that not all squirrels hibernate, or it might imply the weaker proposition that the speaker does not know whether all squirrels hibernate. The present study examines the extent to which speaker knowledge influences implied meanings as well as the timing of any such influence. Using a self-paced presentation, participants read sentences containing some in contexts where a speaker should know whether all was true, or where the speaker merely might know whether all was true. This knowledge manipulation was found to have immediate and reliable effects on the type of inference that was drawn. In contrast, knowledge played no role when the same meanings were conveyed literally. This work thus demonstrates that perceivers consider the speaker's knowledge state incrementally to establish the speaker's communicative goals.