The time course of speaker-specific language processing

Introducing the Sisu Voice Matching Test (SVMT): A novel tool for assessing voice discrimination in Chinese

Existing standardized tests for voice discrimination are based mainly on Indo-European languages, particularly English. However, voice identity perception is influenced by language familiarity, with listeners generally performing better in their native language than in a foreign one. To provide a more accurate and comprehensive assessment of voice discrimination, it is crucial to develop tests tailored to the native language of the test takers. In response, we developed the Sisu Voice Matching Test (SVMT), a pioneering tool designed specifically for Mandarin Chinese speakers. The SVMT was designed to model real-world communication since it includes both pseudo-word and pseudo-sentence stimuli and covers both the ability to categorize identical voices as the same and the ability to categorize distinct voices as different. Built on a neurally validated voice-space model and item response theory, the SVMT ensures high reliability, validity, appropriate difficulty, and strong discriminative power, while maintaining a concise test duration of approximately 10 min. Therefore, by taking into account the effects of language nativeness, the SVMT complements existing voice tests based on other languages' phonologies to provide a more accurate assessment of voice discrimination ability for Mandarin Chinese speakers. Future research can use the SVMT to deepen our understanding of the mechanisms underlying human voice identity perception, especially in special populations, and to examining the relationship between voice identity recognition and other cognitive processes.

An Agent-First Preference in a Patient-First Language During Sentence Comprehension

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400-like negativity when the disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents.

Online neurostimulation of Broca's area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed-e.g., the determiner "a" triggers the prediction for a noun-and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca's area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

I was being sarcastic!: The effect of foreign accent and political ideology on irony (mis)understanding

Misunderstood ironic intents may injure the conversation and impede connecting with others. Prior research suggests that ironic compliments, a rarer type of irony, are considered less ironic when spoken with a foreign accent. Using more ecologically-valid stimuli with natural prosodic cues, we found that this effect also applied to ironic criticisms, not just to ironic compliments. English native speakers (N = 96) listened to dialogs between Canadian English speakers and their foreign-accented peers, rating targets on multiple scales (irony, certainty in the speaker's intent, appropriateness, and offensiveness). Generalized additive mixed modelling showed that 1) ironic comments were rated lower for irony when foreign-accented, whereas literal comments were unaffected by accent; 2) the listener's political orientation, but not empathy or need for cognitive closure, modulated irony detection accuracy. The results are discussed in terms of linguistic expectations, social distance, cultural stereotypes, and personality differences.