70 research outputs found
On the incrementality of pragmatic processing: An ERP investigation of informativeness and pragmatic abilities
In two event-related potential (ERP) experiments, we determined to what extent Grice’s maxim of informativeness as well as pragmatic ability contributes to the incremental build-up of sentence meaning, by examining the impact of underinformative versus informative scalar statements (e.g. “Some people have lungs/pets, and…”) on the N400 event-related potential (ERP), an electrophysiological index of semantic processing. In Experiment 1, only pragmatically skilled participants (as indexed by the Autism Quotient Communication subscale) showed a larger N400 to underinformative statements. In Experiment 2, this effect disappeared when the critical words were unfocused so that the local underinformativeness went unnoticed (e.g., “Some people have lungs that…”). Our results suggest that, while pragmatic scalar meaning can incrementally contribute to sentence comprehension, this contribution is dependent on contextual factors, whether these are derived from individual pragmatic abilities or the overall experimental context
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Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming.
When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) - a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related "prime" word. This so-called "N400 semantic priming effect" is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model's trial-by-trial estimates of target word probability accounted for significant variance in trial-by-trial N400 amplitude. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing
A predictive coding model of the N400
The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding – a biologically plausible algorithm for approximating Bayesian inference – offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.</p
Asymmetric projections of the arcuate fasciculus to the temporal cortex underlie lateralized language function in the human brain
The arcuate fasciculus (AF) in the human brain has asymmetric structural properties. However, the topographic organization of the asymmetric AF projections to the cortex and its relevance to cortical function remain unclear. Here we mapped the posterior projections of the human AF in the inferior parietal and lateral temporal cortices using surface-based structural connectivity analysis based on diffusion MRI and investigated their hemispheric differences. We then performed the cross-modal comparison with functional connectivity based on resting-state functional MRI (fMRI) and task-related cortical activation based on fMRI using a semantic classification task of single words. Structural connectivity analysis showed that the left AF connecting to Broca's area predominantly projected in the lateral temporal cortex extending from the posterior superior temporal gyrus to the mid part of the superior temporal sulcus and the middle temporal gyrus, whereas the right AF connecting to the right homolog of Broca's area predominantly projected to the inferior parietal cortex extending from the mid part of the supramarginal gyrus to the anterior part of the angular gyrus. The left-lateralized projection regions of the AF in the left temporal cortex had asymmetric functional connectivity with Broca's area, indicating structure-function concordance through the AF. During the language task, left-lateralized cortical activation was observed. Among them, the brain responses in the temporal cortex and Broca's area that were connected through the left-lateralized AF pathway were specifically correlated across subjects. These results suggest that the human left AF, which structurally and functionally connects the mid temporal cortex and Broca's area in asymmetrical fashion, coordinates the cortical activity in these remote cortices during a semantic decision task. The unique feature of the left AF is discussed in the context of the human capacity for language.National Institutes of Health (U.S.) (Grant R01NS069696)National Institutes of Health (U.S.) (Grant P41EB015896)National Institutes of Health (U.S.) (Grant S10ODRR031599)National Institutes of Health (U.S.) (Grant S10RR021110)National Science Foundation (U.S.) (Grant NFS-DMS-1042134)Uehara Memorial Foundation (Fellowship)Society of Nuclear Medicine and Molecular Imaging (Wagner-Torizuka Fellowship)United States. Dept. of Energy (Grant DE-SC0008430
When the Truth Is Not Too Hard to Handle: An Event-Related Potential Study on the Pragmatics of Negation
Our brains rapidly map incoming language onto what we hold to be true. Yet there are claims that such integration and verification processes are delayed in sentences containing negation words like not. However, studies have often confounded whether a statement is true and whether it is a natural thing to say during normal communication. In an event-related potential (ERP) experiment, we aimed to disentangle effects of truth value and pragmatic licensing on the comprehension of affirmative and negated real-world statements. As in affirmative sentences, false words elicited a larger N400 ERP than did true words in pragmatically licensed negated sentences (e.g., “In moderation, drinking red wine isn't bad/good…”), whereas true and false words elicited similar responses in unlicensed negated sentences (e.g., “A baby bunny's fur isn't very hard/soft…”). These results suggest that negation poses no principled obstacle for readers to immediately relate incoming words to what they hold to be true
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Altered language network activity in young people at familial high-risk for schizophrenia
Background—Abnormalities in language and language neural circuitry are observed in
schizophrenia (SZ). Similar, but less pronounced language deficits are also seen in young first degree relatives of people with SZ, who are at higher familial risk (FHR) for the disorder than the general population. The neural underpinnings of these deficits in people with FHR are unclear. Methods—Participants were 43 people with FHR and 32 comparable controls. fMRI scans were collected while participants viewed associated and unrelated word pairs, and performed a lexical decision task. fMRI analyses conducted in SPM8 examined group differences in the modulation of hemodynamic activity by semantic association. Results—There were no group differences in demographics, IQ or behavioral semantic priming, but FHR participants had more schizotypal traits than controls. Controls exhibited the expected suppression of hemodynamic activity to associated versus unrelated word pairs. Compared to controls, FHR participants showed an opposite pattern of hemodynamic modulation to associated versus unrelated word pairs, in the left inferior frontal gyrus (IFG), right superior and middle temporal gyrus (STG) and the left cerebellum. Group differences in activation were significant, FWE-corrected for multiple comparisons (p<0.05). Activity within the IFG during the unrelated condition predicted schizotypal symptoms in FHR participants. Conclusions—FHR for SZ is associated with abnormally increased neural activity to semantic associates within an inferior frontal/temporal network. This might increase the risk of developing unusual ideas, perceptions and disorganized language that characterize schizotypal traits, potentially predicting which individuals are at greater risk to develop a psychotic disorder
Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop
This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount
A hierarchical generative framework of language processing: Linking language perception, interpretation, and production abnormalities in schizophrenia
Language and thought dysfunction are central to the schizophrenia syndrome. They are evident in the major symptoms of psychosis itself, particularly as disorganized language output (positive thought disorder) and auditory verbal hallucinations, and they also manifest as abnormalities in both high-level semantic and contextual processing and low-level perception. However, the literatures characterizing these abnormalities have largely been separate and have sometimes provided mutually exclusive accounts of aberrant language in schizophrenia. In this review, we propose that recent generative probabilistic frameworks of language processing can provide crucial insights that link these four lines of research. We first outline neural and cognitive evidence that real-time language comprehension and production normally involve internal generative circuits that propagate probabilistic predictions to perceptual cortices — predictions that are incrementally updated based on prediction error signals as new inputs are encountered. We then explain how disruptions to these circuits may compromise communicative abilities in schizophrenia by reducing the efficiency and robustness of both high-level language processing and low-level speech perception. We also argue that such disruptions may contribute to the phenomenology of thought-disordered speech and false perceptual inferences in the language system (i.e., auditory verbal hallucinations). This perspective suggests a number of productive avenues for future research that may elucidate not only the mechanisms of language abnormalities in schizophrenia, but also promising directions for cognitive rehabilitation
Studying musical and linguistic prediction in comparable ways: the melodic cloze probability method
Prediction or expectancy is thought to play an important role in both music and language processing. However, prediction is currently studied independently in the two domains, limiting research on relations between predictive mechanisms in music and language. One limitation is a difference in how expectancy is quantified. In language, expectancy is typically measured using the cloze probability task, in which listeners are asked to complete a sentence fragment with the first word that comes to mind. In contrast, previous production-based studies of melodic expectancy have asked participants to sing continuations following only one to two notes. We have developed a melodic cloze probability task in which listeners are presented with the beginning of a novel tonal melody (5-9 notes) and are asked to sing the note they expect to come next. Half of the melodies had an underlying harmonic structure designed to constrain expectations for the next note, based on an implied authentic cadence within the melody. Each such ‘authentic cadence’ (AC) melody was matched to a ‘non-cadential’ (NC) melody matched in terms of length, rhythm and melodic contour, but differing in implied harmonic structure. Participants showed much greater consistency in the notes sung following AC vs. NC melodies on average. However, significant variation in degree of consistency was observed within both AC and NC melodies. Analysis of individual melodies suggests that pitch prediction in tonal melodies depends on the interplay of local factors just prior to the target note (e.g., local pitch interval patterns) and larger-scale structural relationships (e.g., implied harmonic structure). We illustrate how the melodic cloze method can be used to test a computational model of melodic expectation. Future uses for the method include exploring the interplay of different factors shaping melodic expectation, and designing experiments that compare the cognitive mechanisms of prediction in music and language
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