Periodic Reporting for period 4 - L2STAT (Statistical learning and L2 literacy acquisition: Towards a neurobiological theory of assimilating novel writing systems)
Berichtszeitraum: 2021-01-01 bis 2022-12-31
The integration of non-native populations into society, and especially into the workforce, is dependent upon the learning of a new language, and mostly, the acquisition of functional literacy. Indeed, a long-term objective of the EU commission is that every citizen achieves literacy in at least two foreign languages. How do proficient readers in one language learn a novel writing system and achieve literacy in a second or third language? What are the regularities that they perceive and learn? What are the main neuro-cognitive mechanisms governing this learning of statistical regularities? Why is this task relatively easy for some but not for others? These intriguing and complex questions were the focus of L2STAT. The aim of the research project was to produce and test a neurobiological theory of assimilating novel writing systems by the brain, a theory that could accommodate in principle any writing system, and has, therefore, wide explanatory power. L2STAT was an interdisciplinary project that employed in parallel advanced methods from computational linguistics and machine learning, the use of biologically-inspired computational models, the development of psychometrically reliable behavioral tests of individuals’ capacities to extract regularities, a search for reliable neurobiological signatures of detecting regularities in the human brain, and extensive behavioral experimentation in four sites (Israel, Spain, Taiwan, USA) to examine the inter-relation of statistical learning ability and reading proficiency in different languages. Overall, the project offered for the first time a theoretical framework for assessing individual differences in statistical learning, and outlined their relations to reading (dis)abilities. It elucidated the componential structure of statistical learning performance, tracing their relation to literacy acquisition in a writing system.
WP1- We assembled a comparable database of printed words in English, Spanish and Hebrew from the OpenSubtitles (OPUS). Our cleaned databases allowed us to examine cross-linguistic differences in the statistical distribution of information in Hebrew vs. English vs. Spanish at the level of printed letters and words per the planned research program.
WP2- In collaboration with Blair Armstrong (now at University of Toronto), Noam Siegelman (now at Hebrew U), and Raquel Alhama (now at Tilburg U.) we produced through a neural network modelling framework, a computational model that learned to identify words, when simulating fixating at different locations in that word (manuscript now in preparation).
WP3- By tracking online performance in a self-paced SL paradigm, we focused on the trajectory of learning. We demonstrated that this paradigm provides a reliable and valid signature of SL performance, and offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality when learning proceeds. We centred on how prior linguistic knowledge regarding speech co-occurrences of a native language, impacts what participants learn from novel auditory verbal input. We showed that auditory-verbal tasks display distinct item-specific effects given entrenchment due to linguistic environment, predicting what regularities will be learned in an auditory speech stream.
WP4- We explored a range of neurobiological signatures of learning during visual statistical learning. This served two main aims. First, methodologically, to offer an independent online measure that regularities have been extracted. Second, to advance towards a deeper understanding of the mechanisms underlying prediction of structured visual inputs. Our research revealed that an increased oscillatory activity in the beta band (20 Hz), is a neurobiological signature of prediction when learning proceeds.
WP5- We examined the convergence of print and speech processing networks of primarily left-hemisphere regions of the brain during second-language (L2) literacy acquisition, targeting English and Hebrew L2 learners. We found 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. 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. Comparing Hebrew to Chinese, we examined how prior linguistic knowledge in reading Chinese enhances sensitivity to spatial contingencies compared with Hebrew readers.
WP2- In collaboration with Blair Armstrong (now at University of Toronto), Noam Siegelman (now at Hebrew U), and Raquel Alhama (now at Tilburg U.) we produced through a neural network modelling framework, a computational model that learned to identify words, when simulating fixating at different locations in that word (manuscript now in preparation).
WP3- By tracking online performance in a self-paced SL paradigm, we focused on the trajectory of learning. We demonstrated that this paradigm provides a reliable and valid signature of SL performance, and offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality when learning proceeds. We centred on how prior linguistic knowledge regarding speech co-occurrences of a native language, impacts what participants learn from novel auditory verbal input. We showed that auditory-verbal tasks display distinct item-specific effects given entrenchment due to linguistic environment, predicting what regularities will be learned in an auditory speech stream.
WP4- We explored a range of neurobiological signatures of learning during visual statistical learning. This served two main aims. First, methodologically, to offer an independent online measure that regularities have been extracted. Second, to advance towards a deeper understanding of the mechanisms underlying prediction of structured visual inputs. Our research revealed that an increased oscillatory activity in the beta band (20 Hz), is a neurobiological signature of prediction when learning proceeds.
WP5- We examined the convergence of print and speech processing networks of primarily left-hemisphere regions of the brain during second-language (L2) literacy acquisition, targeting English and Hebrew L2 learners. We found 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. 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. Comparing Hebrew to Chinese, we examined how prior linguistic knowledge in reading Chinese enhances sensitivity to spatial contingencies compared with Hebrew readers.
L2STAT has developed statistical learning tasks that measure learning while it proceeds. The tasks for tracking learning in the visual modality as developed in WP3 are now acknowledged by the scientific community as a valid tool for tracking statistical learning. These tasks are transferred as free resource to all labs interested in visual or auditory statistical learning for use in laboratory experiments. On the theoretical level, L2STAT has developed a novel Information Theory of Reading, which will be, I believe, a game changer in understanding reading in different writing systems. The expected results until the end of the project is to publish a cross-linguistic computational model of reading that follows information theory.