Predictive processing of music and language in autism: Evidence from Mandarin and English speakers

Atypical predictive processing has been associated with autism across multiple domains, based mainly on artificial antecedents and consequents. As structured sequences where expectations derive from implicit learning of combinatorial principles, language and music provide naturalistic stimuli for investigating predictive processing. In this study, we matched melodic and sentence stimuli in cloze probabilities and examined musical and linguistic prediction in Mandarin- (Experiment 1) and English-speaking (Experiment 2) autistic and non-autistic individuals using both production and perception tasks. In the production tasks, participants listened to unfinished melodies/sentences and then produced the final notes/words to complete these items. In the perception tasks, participants provided expectedness ratings of the completed melodies/sentences based on the most frequent notes/words in the norms. While Experiment 1 showed intact musical prediction but atypical linguistic prediction in autism in the Mandarin sample that demonstrated imbalanced musical training experience and receptive vocabulary skills between groups, the group difference disappeared in a more closely matched sample of English speakers in Experiment 2. These findings suggest the importance of taking an individual differences approach when investigating predictive processing in music and language in autism, as the difficulty in prediction in autism may not be due to generalized problems with prediction in any type of complex sequence processing.

Study of differences between patients with schizophrenia and healthy people in semantic processing

Semantic processing is important in language comprehension and production, and context can facilitate understanding and accelerate processing speed by pre-activating semantically related words. There are many studies suggesting that patients with schizophrenia have inferior language ability. This study was aimed to examine the differences between patients with schizophrenia and healthy people in semantic processing with Chinese classifier-noun pairs rating tasks. Participants were required to finish rating tasks to judge acceptability of classifier-noun pairs. Also, the Positive and Negative Syndrome Scale (PANSS) was conducted in the schizophrenia group. According to results of variance analysis, schizophrenic patients' accuracy of judgment on the acceptability of classifier-noun pairs differed from the control group (F = 4.13, p < .05), and the contextual effect of classifier constraint could be observed in healthy people (F(1, 31) = 5.38, p < .05) but not in patients with schizophrenia (F(1, 25) = 3.55, p = .07), indicating that they failed to use the contextual information to facilitate language comprehension as healthy people. Stepwise linear regression analysis found that hostility, poor impulse control and suspiciousness/persecution and preoccupation in the PANSS may have contributed to the reduced sensitivity in the rating in patients (t = -2.38-3.80, p < .05).

Completion norms for 3085 English sentence contexts

In everyday language processing, sentence context affects how readers and listeners process upcoming words. In experimental situations, it can be useful to identify words that are predicted to greater or lesser degrees by the preceding context. Here we report completion norms for 3085 English sentences, collected online using a written cloze procedure in which participants were asked to provide their best guess for the word completing a sentence. Sentences varied between eight and ten words in length. At least 100 unique participants contributed to each sentence. All responses were reviewed by human raters to mitigate the influence of mis-spellings and typographical errors. The responses provide a range of predictability values for 13,438 unique target words, 6790 of which appear in more than one sentence context. We also provide entropy values based on the relative predictability of multiple responses. A searchable set of norms is available at http://sentencenorms.net. Finally, we provide the code used to collate and organize the responses to facilitate additional analyses and future research projects.

Are Linguistic Prediction Deficits Characteristic of Adults with Dyslexia?

Individuals with dyslexia show deficits in phonological abilities, rapid automatized naming, short-term/working memory, processing speed, and some aspects of sensory and visual processing. There is currently one report in the literature that individuals with dyslexia also show impairments in linguistic prediction. The current study sought to investigate prediction in language processing in dyslexia. Forty-one adults with dyslexia and 43 typically-developing controls participated. In the experiment, participants made speeded-acceptability judgements in sentences with word final cloze manipulations. The final word was a high-cloze probability word, a low-cloze probability word, or a semantically anomalous word. Reaction time from the onset of the final word to participants' response was recorded. Results indicated that individuals with dyslexia showed longer reaction times, and crucially, they showed clear differences from controls in low predictability sentences, which is consistent with deficits in linguistic prediction. Conclusions focus on the mechanism supporting prediction in language comprehension and possible reasons why individuals with dyslexia show less prediction.

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 (AC) within the melody. Each such 'authentic cadence' 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., melodic patterns and 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.