Sniffing out meaning: Chemosensory and semantic neural network changes in sommeliers.

Wine tasting is a very complex process that integrates a combination of sensation, language, and memory. Taste and smell provide perceptual information that, together with the semantic narrative that converts flavor into words, seem to be processed differently between sommeliers and naïve wine consumers. We investigate whether sommeliers' wine experience shapes only chemosensory processing, as has been previously demonstrated, or if it also modulates the way in which the taste and olfactory circuits interact with the semantic network. Combining diffusion-weighted images and fMRI (activation and connectivity) we investigated whether brain response to tasting wine differs between sommeliers and nonexperts (1) in the sensory neural circuits representing flavor and/or (2) in the neural circuits for language and memory. We demonstrate that training in wine tasting shapes the microstructure of the left and right superior longitudinal fasciculus. Using mediation analysis, we showed that the experience modulates the relationship between fractional anisotropy and behavior: the higher the fractional anisotropy the higher the capacity to recognize wine complexity. In addition, we found functional differences between sommeliers and naïve consumers affecting the flavor sensory circuit, but also regions involved in semantic operations. The former reflects a capacity for differential sensory processing, while the latter reflects sommeliers' ability to attend to relevant sensory inputs and translate them into complex verbal descriptions. The enhanced synchronization between these apparently independent circuits suggests that sommeliers integrated these descriptions with previous semantic knowledge to optimize their capacity to distinguish between subtle differences in the qualitative character of the wine.

HeLP: The Hebrew Lexicon project.

Lexicon projects (LPs) are large-scale data resources in different languages that present behavioral results from visual word recognition tasks. Analyses using LP data in multiple languages provide evidence regarding cross-linguistic differences as well as similarities in visual word recognition. Here we present the first LP in a Semitic language-the Hebrew Lexicon Project (HeLP). HeLP assembled lexical decision (LD) responses to 10,000 Hebrew words and nonwords, and naming responses to a subset of 5000 Hebrew words. We used the large-scale HeLP data to estimate the impact of general predictors (lexicality, frequency, word length, orthographic neighborhood density), and Hebrew-specific predictors (Semitic structure, presence of clitics, phonological entropy) of visual word recognition performance. Our results revealed the typical effects of lexicality and frequency obtained in many languages, but more complex impact of word length and neighborhood density. Considering Hebrew-specific characteristics, HeLP data revealed better recognition of words with a Semitic structure than words that do not conform to it, and a drop in performance for words comprising clitics. These effects varied, however, across LD and naming tasks. Lastly, a significant inhibitory effect of phonological ambiguity was found in both naming and LD. The implications of these findings for understanding reading in a Semitic language are discussed.

Expanding horizons of cross-linguistic research on reading: The Multilingual Eye-movement Corpus (MECO).

Scientific studies of language behavior need to grapple with a large diversity of languages in the world and, for reading, a further variability in writing systems. Yet, the ability to form meaningful theories of reading is contingent on the availability of cross-linguistic behavioral data. This paper offers new insights into aspects of reading behavior that are shared and those that vary systematically across languages through an investigation of eye-tracking data from 13 languages recorded during text reading. We begin with reporting a bibliometric analysis of eye-tracking studies showing that the current empirical base is insufficient for cross-linguistic comparisons. We respond to this empirical lacuna by presenting the Multilingual Eye-Movement Corpus (MECO), the product of an international multi-lab collaboration. We examine which behavioral indices differentiate between reading in written languages, and which measures are stable across languages. One of the findings is that readers of different languages vary considerably in their skipping rate (i.e., the likelihood of not fixating on a word even once) and that this variability is explained by cross-linguistic differences in word length distributions. In contrast, if readers do not skip a word, they tend to spend a similar average time viewing it. We outline the implications of these findings for theories of reading. We also describe prospective uses of the publicly available MECO data, and its further development plans.

Beta-Band Activity Is a Signature of Statistical Learning.

Through statistical learning (SL), cognitive systems may discover the underlying regularities in the environment. Testing human adults (n = 35, 21 females), we document, in the context of a classical visual SL task, divergent rhythmic EEG activity in the interstimulus delay periods within patterns versus between patterns (i.e., pattern transitions). Our findings reveal increased oscillatory activity in the beta band (∼20 Hz) at triplet transitions that indexes learning: it emerges with increased pattern repetitions; and importantly, it is highly correlated with behavioral learning outcomes. These findings hold the promise of converging on an online measure of learning regularities and provide important theoretical insights regarding the mechanisms of SL and prediction.SIGNIFICANCE STATEMENT Statistical learning has become a major theoretical construct in cognitive science, providing the primary means by which organisms learn about regularities in the environment. As such, it is a critical building block for basic and higher-order cognitive functions. Here we identify, for the first time, a spectral neural index in the time window before stimulus presentation, which evolves with increased pattern exposure, and is predictive of learning performance. The manifestation of learning that is revealed, not in stimulus processing but in the blank interval between stimuli, makes a direct link between the fields of statistical learning on the one hand and either prediction or consolidation on the other hand, suggesting a possible mechanistic account of visual statistical learning.

Neurobiological signatures of L2 proficiency: Evidence from a bi-directional cross-linguistic study.

Recent evidence has shown that convergence of print and speech processing across a network of primarily left-hemisphere regions of the brain is a predictor of future reading skills in children, and a marker of fluent reading ability in adults. The present study extends these findings into the domain of second-language (L2) literacy, through brain imaging data of English and Hebrew L2 learners. Participants received an fMRI brain scan, while performing a semantic judgement task on spoken and written words and pseudowords in both their L1 and L2, alongside a battery of L1 and L2 behavioural measures. Imaging results show, overall, show a similar network of activation for reading across the two languages, alongside significant convergence of print and speech processing across a network of left-hemisphere regions in both L1 and L2 and in both cohorts. Importantly, convergence is greater for L1 in occipito-temporal regions tied to automatic skilled reading processes including the visual word-form area, but greater for L2 in frontal regions of the reading network, tied to more effortful, active processing. The main groupwise brain effects tell a similar story, with greater L2 than L1 activation across frontal, temporal and parietal regions, but greater L1 than L2 activation in parieto-occipital regions tied to automatic mapping processes in skilled reading. These results provide evidence for the shifting of the reading networks towards more automatic processing as reading proficiency rises and the mappings and statistics of the new orthography are learned and incorporated into the reading system.

Universal brain signature of proficient reading: Evidence from four contrasting languages.

We propose and test a theoretical perspective in which a universal hallmark of successful literacy acquisition is the convergence of the speech and orthographic processing systems onto a common network of neural structures, regardless of how spoken words are represented orthographically in a writing system. During functional MRI, skilled adult readers of four distinct and highly contrasting languages, Spanish, English, Hebrew, and Chinese, performed an identical semantic categorization task to spoken and written words. Results from three complementary analytic approaches demonstrate limited language variation, with speech-print convergence emerging as a common brain signature of reading proficiency across the wide spectrum of selected languages, whether their writing system is alphabetic or logographic, whether it is opaque or transparent, and regardless of the phonological and morphological structure it represents.

Measuring individual differences in statistical learning: Current pitfalls and possible solutions.

Most research in statistical learning (SL) has focused on the mean success rates of participants in detecting statistical contingencies at a group level. In recent years, however, researchers have shown increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most if not all of this research enterprise has employed SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak, and sometimes even flawed. In particular, the existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetition of the same targets throughout the test); (2) a large proportion of the sample performs at chance level, so that most of the data points reflect noise; and (3) the test items following familiarization are all of the same type and an identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability, and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task and discuss the importance of the implementation of such tasks in future research.

What predicts successful literacy acquisition in a second language?

In the study reported here, we examined whether success (or failure) in assimilating the structure of a second language can be predicted by general statistical-learning abilities that are nonlinguistic in nature. We employed a visual-statistical-learning (VSL) task, monitoring our participants' implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task was not correlated with abilities related to a general g factor or working memory. We found that, on average, native speakers of English who more accurately picked up the implicit statistical structure embedded in the continuous stream of shapes better assimilated the Semitic structure of Hebrew words. Languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and our findings suggest that these correlations are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment.

Towards a universal model of reading.

In the last decade, reading research has seen a paradigmatic shift. A new wave of computational models of orthographic processing that offer various forms of noisy position or context-sensitive coding have revolutionized the field of visual word recognition. The influx of such models stems mainly from consistent findings, coming mostly from European languages, regarding an apparent insensitivity of skilled readers to letter order. Underlying the current revolution is the theoretical assumption that the insensitivity of readers to letter order reflects the special way in which the human brain encodes the position of letters in printed words. The present article discusses the theoretical shortcomings and misconceptions of this approach to visual word recognition. A systematic review of data obtained from a variety of languages demonstrates that letter-order insensitivity is neither a general property of the cognitive system nor a property of the brain in encoding letters. Rather, it is a variant and idiosyncratic characteristic of some languages, mostly European, reflecting a strategy of optimizing encoding resources, given the specific structure of words. Since the main goal of reading research is to develop theories that describe the fundamental and invariant phenomena of reading across orthographies, an alternative approach to model visual word recognition is offered. The dimensions of a possible universal model of reading, which outlines the common cognitive operations involved in orthographic processing in all writing systems, are discussed.

Author's response: A universal approach to modeling visual word recognition and reading: not only possible, but also inevitable.

I have argued that orthographic processing cannot be understood and modeled without considering the manner in which orthographic structure represents phonological, semantic, and morphological information in a given writing system. A reading theory, therefore, must be a theory of the interaction of the reader with his/her linguistic environment. This outlines a novel approach to studying and modeling visual word recognition, an approach that focuses on the common cognitive principles involved in processing printed words across different writing systems. These claims were challenged by several commentaries that contested the merits of my general theoretical agenda, the relevance of the evolution of writing systems, and the plausibility of finding commonalities in reading across orthographies. Other commentaries extended the scope of the debate by bringing into the discussion additional perspectives. My response addresses all these issues. By considering the constraints of neurobiology on modeling reading, developmental data, and a large scope of cross-linguistic evidence, I argue that front-end implementations of orthographic processing that do not stem from a comprehensive theory of the complex information conveyed by writing systems do not present a viable approach for understanding reading. The common principles by which writing systems have evolved to represent orthographic, phonological, and semantic information in a language reveal the critical distributional characteristics of orthographic structure that govern reading behavior. Models of reading should thus be learning models, primarily constrained by cross-linguistic developmental evidence that describes how the statistical properties of writing systems shape the characteristics of orthographic processing. When this approach is adopted, a universal model of reading is possible.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

When the "Tabula" is Anything but "Rasa:" What Determines Performance in the Auditory Statistical Learning Task?

How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners' native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish ("Spanish-like" condition), the other by embedding pseudowords with low co-occurrence frequency ("Spanish-unlike" condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more "Spanish-like," participants were better able to identify them as part of the stream. These findings demonstrate that learners' task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.

Hebrew brain vs. English brain: language modulates the way it is processed.

Is language processing universal? How do the specific properties of each language influence the way it is processed? In this study, we compare the neural correlates of morphological processing in Hebrew--a Semitic language with a rich and systematic morphology, to those revealed in English--an Indo-European language with a linear morphology. Using fMRI, we show that while in the bilingual brain both languages involve a common neural circuitry in processing morphological structure, this activation is significantly modulated by the different aspects of language. Whereas in Hebrew, morphological processing is independent of semantics, in English, morphological activation is clearly modulated by semantic overlap. These findings suggest that the processes involved in reading words are not universal, and therefore impose important constraints on current models of visual word recognition.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts.

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., "statistical learning," "implicit learning," and "procedural learning") as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Tracking second language immersion across time: Evidence from a bi-directional longitudinal cross-linguistic fMRI study.

Parallel cohorts of Hebrew speakers learning English in the U.S., and American-English speakers learning Hebrew in Israel were tracked over the course of two years of immersion in their L2. We utilised a functional MRI semantic judgement task with print and speech tokens, as well as a battery of linguistic and cognitive behavioural measures prior to and after immersion, to track changes in both L1 and L2 processing. fMRI activation for print tokens produced a similar network of activation in both English and Hebrew, irrespective of L1 or L2 status. Significant convergence of print and speech processing was also observed in both languages across a network of left-hemisphere regions joint for both L1 and L2. Despite significant increases in behavioural measures of L2 proficiency, only a few signs of longitudinal change in L2 brain activation were found. In contrast, L1 showed widespread differences in processing across time, suggesting that the neurobiological footprint of reading is dynamic and plastic even in adults, with L2 immersion impacting L1 processing. Print/speech convergence showed little longitudinal change, suggesting that it is a stable marker of the differences in L1 and L2 processing across L2 proficiency.

Imaging implicit morphological processing: evidence from Hebrew.

Is morphology a discrete and independent element of lexical structure or does it simply reflect a fine-tuning of the system to the statistical correlation that exists among orthographic and semantic properties of words? Hebrew provides a unique opportunity to examine morphological processing in the brain because of its rich morphological system. In an fMRI masked priming experiment, we investigated the neural networks involved in implicit morphological processing in Hebrew. In the lMFG and lIFG, activation was found to be significantly reduced when the primes were morphologically related to the targets. This effect was not influenced by the semantic transparency of the morphological prime, and was not found in the semantic or orthographic condition. Additional morphologically related decrease in activation was found in the lIPL, where activation was significantly modulated by semantic transparency. Our findings regarding implicit morphological processing suggest that morphology is an automatic and distinct aspect of visually processing words. These results also coincide with the behavioral data previously obtained demonstrating the central role of morphological processing in reading Hebrew.

What Determines Visual Statistical Learning Performance? Insights From Information Theory.

In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co-occurrences between them are computed and registered. Here, we entertain a novel hypothesis that one unifying construct-the rate of information in the sensory input-explains learning performance. This theoretical approach merges processes related to encoding of events and those related to learning their regularities into a single computational principle. We present data from two large-scale experiments with over 800 participants tested in support for this hypothesis, showing that rate of information in a visual stream clearly predicts SL performance, and that similar rate of information values leads to similar SL performance. We discuss the implications for SL theory and its relation to regularity learning.

What exactly is learned in visual statistical learning? Insights from Bayesian modeling.

It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.

Statistical learning research: A critical review and possible new directions.

Statistical learning (SL) is involved in a wide range of basic and higher-order cognitive functions and is taken to be an important building block of virtually all current theories of information processing. In the last 2 decades, a large and continuously growing research community has therefore focused on the ability to extract embedded patterns of regularity in time and space. This work has mostly focused on transitional probabilities, in vision, audition, by newborns, children, adults, in normal developing and clinical populations. Here we appraise this research approach and we critically assess what it has achieved, what it has not, and why it is so. We then center on present SL research to examine whether it has adopted novel perspectives. These discussions lead us to outline possible blueprints for a novel research agenda. (PsycINFO Database Record (c) 2019 APA, all rights reserved).