Statistical learning in patients in the minimally conscious state

When listening to an unknown language, listeners could learn the transitional probability between syllables and group frequently co-occurred syllables into a whole unit. Such statistical learning ability has been demonstrated for both pre-verbal infants and adults, even during passive listening. Here, we investigated whether statistical learning occurred in patients in minimally conscious state (MCS) and patients emerged from the minimally conscious state (EMCS) using electroencephalography (EEG). We presented to participants an isochronous sequence of syllables, which were composed of either 2-word real phrases or 2-word artificial phrases that were defined by the transitional probability between words. An inter-trial phase coherence (ITPC) analysis revealed that the phrase-rate EEG response was weakened in EMCS patients compared with healthy individuals, and was even more severely weakened in MCS patients. Although weak, the phrase-rate response or its harmonics remained statistically significant in MCS patients, suggesting that the statistical learning ability was preserved in MCS patients. The word-rate response was also weakened with a decreased level of consciousness. The harmonics of the word-rate response, however,were more salient in MCS than EMCS patients in the alpha and beta bands. Together with previous studies, the current results suggest that MCS patients retain residual learning ability, which can potentially be harnessed to induce neural plasticity, and that different frequency bands are differentially related to the consciousness level.

Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to 4 syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult' behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. But we also revealed that : 1) Successfully tracking transition probabilities in a sequence is not sufficient to segment it 2) Prosodic cues, as subtle as subliminal pauses, enable to recover segmenting capacities 3) Adults' and neonates' capacities are remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the similarity of neural responses across infants, providing a new neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing to extract small coherent word-like units within auditory streams, based on the combination of statistical analyses and prosodic cues.

Comparing predictors of sentence self-paced reading times: Syntactic complexity versus transitional probability metrics

When estimating the influence of sentence complexity on reading, researchers typically opt for one of two main approaches: Measuring syntactic complexity (SC) or transitional probability (TP). Comparisons of the predictive power of both approaches have yielded mixed results. To address this inconsistency, we conducted a self-paced reading experiment. Participants read sentences of varying syntactic complexity. From two alternatives, we selected the set of SC and TP measures, respectively, that provided the best fit to the self-paced reading data. We then compared the contributions of the SC and TP measures to self-paced reading times when entered into the same model. Our results showed that while both measures explained significant portions of variance in reading times (over and above control variables: word/sentence length, word frequency and word position) when included in independent models, their contributions changed drastically when SC and TP were entered into the same model. Specifically, we only observed significant effects of TP. We conclude that in our experiment the control variables explained the bulk of variance. When comparing the small effects of SC and TP, the effects of TP appear to be more robust.

Comparing predictors of sentence self-paced reading times: Syntactic complexity versus transitional probability metrics

When estimating the influence of sentence complexity on reading, researchers typically opt for one of two main approaches: Measuring syntactic complexity (SC) or transitional probability (TP). Comparisons of the predictive power of both approaches have yielded mixed results. To address this inconsistency, we conducted a self-paced reading experiment. Participants read sentences of varying syntactic complexity. From two alternatives, we selected the set of SC and TP measures, respectively, that provided the best fit to the self-paced reading data. We then compared the contributions of the SC and TP measures to reading times when entered into the same model. Our results showed that both measures explained significant portions of variance in self-paced reading times. Thus, researchers aiming to measure sentence complexity should take both SC and TP into account. All of the analyses were conducted with and without control variables known to influence reading times (word/sentence length, word frequency and word position) to showcase how the effects of SC and TP change in the presence of the control variables.

Statistical learning of unbalanced exclusive-or temporal sequences in humans

A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to the position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X, Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.

Statistical Properties in Jazz Improvisation Underline Individuality of Musical Representation

Statistical learning is an innate function in the brain and considered to be essential for producing and comprehending structured information such as music. Within the framework of statistical learning the brain has an ability to calculate the transitional probabilities of sequences such as speech and music, and to predict a future state using learned statistics. This paper computationally examines whether and how statistical learning and knowledge partially contributes to musical representation in jazz improvisation. The results represent the time-course variations in a musician’s statistical knowledge. Furthermore, the findings show that improvisational musical representation might be susceptible to higher- but not lower-order statistical knowledge (i.e., knowledge of higher-order transitional probability). The evidence also demonstrates the individuality of improvisation for each improviser, which in part depends on statistical knowledge. Thus, this study suggests that statistical properties in jazz improvisation underline individuality of musical representation.

An analysis of direction of trade in select Indian agricultural commodities

An attempt is made in this paper to assess the dynamics of changes in exports of agricultural commodities namely, maize, chilli and Bengal gram, from India to different export markets by employing Markov chain model. The study is based on a set of countries importing agricultural commodities namely Nepal, others (pooled countries except selected countries) and Bangladesh were the most stable importers of the Indian maize with a probability of retention of 88.52 per cent, 68.90 percent and 61.09 per cent, respectively. The changing pattern of chilli exports through transitional probability matrices indicated that Thailand, other countries (pooled countries) and Vietnam were stable in importing Indian chilli with a probability of retention of 80.52 per cent, 69.02 per cent and 67.09 per cent, respectively. In case of Bengal gram, Pakistan was one of the stable countries as revealed by a probability of retention of its share i.e., 61.35 per cent. Algeria was also another stable importer as it retained its original share of 45.54 per cent followed by Turkey 41.13 per cent. The overall conclusion that emerges from present study is that Nepal, Thailand and Pakistan turned out to be the most stable countries in respect of importing Indian maize, chilli and Bengal gram.

A note on co-occurrence, transitional probability, and causal inference

Much has been written about the role of prediction in cognition in general, and language processing in particular, with some authors even claiming that prediction is the central goal of cognition. Attributing such a specific goal to cognition seems speculative, but prediction is generally held to play an important role in both perception and action. In empirical studies of language processing, however, measures of predictability such as forward transitional probability (or surprisal) are often no more effective in describing behavioral and neural phenomena than measures of post- or retrodictability such as backward transitional probability. We address this paradox by looking at the relationship between these different information theoretic measures and proposing a mechanistic account of how they are used in cognition. We posit that backward transitional probabilities support causal inferences about the occurrence of word sequences. Using Bayes' Theorem, we demonstrate that predictions (formalized as forward transitional probabilities) can be used in conjunction with the marginal probabilities of the current state/word and the upcoming state/word to compute these causal inferences. This conceptualization of causal inference in language processing both accounts for the role of prediction, and the surprising effectiveness of backwards transitional probability as a predictor of human behavior and its neural correlates.

Japanese Perceptual Epenthesis is Modulated by Transitional Probability

Perceptual epenthesis is the perception of illusory vowels in consonantal sequences that violate native phonotactics. The consensus has been that each language has a single, predictable candidate for perceptual epenthesis, that vowel which is most minimal (i.e., shortest and/or quietest). However, recent studies have shown that alternate epenthetic vowels can be perceived when the perceptual epenthesis of the minimal vowel would violate native co-occurrence restrictions. We propose a potential explanation for these observed patterns: speech perception, and thus also vowel perceptual epenthesis, is modulated by transitional probability whereby epenthetic vowels must conform to the language specific expectations of the listener. To test this explanation, we present two experiments examining perceptual epenthesis of two Japanese vowels—/u/ and /i/—against their transitional probability in CV sequences. In Experiment 1, Japanese listeners assigned VCCV tokens to VCuCV and VCiCV categories. In Experiment 2, participants discriminated VCCV tokens from VCuCV and VCiCV tokens. The results show that sequences where /i/ is transitionally probable are more likely to elicit /i/ perceptual epenthesis.