Towards a unified understanding of lateralized vision:A large-scale study investigating principles governing patterns of lateralization using a heterogeneous sample

While functional lateralization of the human brain has been a widely studied topic in the past decades, few studies to date have gone further than investigating lateralization of single, isolated processes. With the present study, we aimed to arrive at a more unified view by investigating lateralization patterns in face and word processing, and associated lower-level visual processing. We tested a large and heterogeneous participant group, and used a number of tasks that had been shown to produce replicable indices of lateralized processing of visual information of different types and complexity. Following Bayesian statistics, group-level analyses showed the expected right hemisphere (RH) lateralization for face, global form, low spatial frequency processing, and spatial attention, and left hemisphere (LH) lateralization for visual word and local feature processing. Compared to right-handed individuals, lateralization patterns of left-handed and especially those who are RH-dominant for language deviated from this 'typical' pattern. Our results support the notion that face and word processes come to be lateralized to homologue areas of the two hemispheres, under influence of the RHand LH-specializations in global form, local feature, and low and high spatial frequency processing. As such, we present a more unified understanding of lateralized vision, providing evidence for the input asymmetry and causal complementarity principles of lateralized visual information processing. The absence of correlations between spatial attention and lateralization of the other processes supports the notion of their independent lateralization, conform the statistical complementarity principle. (C) 2020 The Author(s). Published by Elsevier Ltd

The Effect of Mixed Font Items on Lexical Decision Performance

The multistream model of word perception (Allen, Smith, Lien, Kaut, & Canfield, 2009) suggests that word identification generally involves whole-word information, but that when the orthographic form of a letter string is not standard, processing occurs analytically and is slower. For example, within-item case transitions slow responses in lexical decision experiments, in which participants are required to decide if a letter string is or is not a word; a within-item font transition may have a similar effect. Letters within a font are distinct yet related, and are constrained on several parameters to facilitate processing (Sanocki & Dyson, 2012). Font tuning allows design commonalties to be utilized by the perceptual system when processing subsequent items, and changes in font slow processing because the translation rules cannot be carried over (Walker, 2008). We conducted two experiments to investigate the effect of font variation on lexical decision performance. Experiment 1 addressed whether between-item font variation interferes with judgments of lexicality. Planned contrasts showed a marginal difference in response times between pure-font and intermixed-font blocks (t(1, 23)= 1.45, p= 0.07). Although the results do not pose a strong challenge to the idea that decisions on lexicality are not interfered with by random trial-to-trial variation in font, response times in intermixed font blocks tended to be slower than responses in pure font blocks. Experiment 2 investigated the effect of within-item font transition on lexical decision performance. The significant main effect of font homogeneity (t(1, 23)= 1.76, p= 0.04) showed that responses to heterogeneous font items were slower than responses to homogeneous font items. The results of Experiment 2 supported the hypothesis that a within-item font transition slows lexical decision performance

The Effect of Mixed Font Items on Lexical Decision Performance

The multistream model of word perception (Allen, Smith, Lien, Kaut, & Canfield, 2009) suggests that word identification generally involves whole-word information, but that when the orthographic form of a letter string is not standard, processing occurs analytically and is slower. For example, within-item case transitions slow responses in lexical decision experiments, in which participants are required to decide if a letter string is or is not a word; a within-item font transition may have a similar effect. Letters within a font are distinct yet related, and are constrained on several parameters to facilitate processing (Sanocki & Dyson, 2012). Font tuning allows design commonalties to be utilized by the perceptual system when processing subsequent items, and changes in font slow processing because the translation rules cannot be carried over (Walker, 2008). We conducted two experiments to investigate the effect of font variation on lexical decision performance. Experiment 1 addressed whether between-item font variation interferes with judgments of lexicality. Planned contrasts showed a marginal difference in response times between pure-font and intermixed-font blocks (t(1, 23)= 1.45, p= 0.07). Although the results do not pose a strong challenge to the idea that decisions on lexicality are not interfered with by random trial-to-trial variation in font, response times in intermixed font blocks tended to be slower than responses in pure font blocks. Experiment 2 investigated the effect of within-item font transition on lexical decision performance. The significant main effect of font homogeneity (t(1, 23)= 1.76, p= 0.04) showed that responses to heterogeneous font items were slower than responses to homogeneous font items. The results of Experiment 2 supported the hypothesis that a within-item font transition slows lexical decision performance

Cognitive Dimensions of Learning in Children With Problems in Attention, Learning, and Memory.

A data-driven, transdiagnostic approach was used to identify the cognitive dimensions linked with learning in a mixed group of 805 children aged 5 to 18 years recognised as having problems in attention, learning and memory by a health or education practitioner. Assessments included phonological processing, information processing speed, short-term and working memory, and executive functions, and attainments in word reading, spelling, and maths. Data reduction methods identified three dimensions of phonological processing, processing speed and executive function for the sample as a whole. This model was comparable for children with and without ADHD. The severity of learning difficulties in literacy was linked with phonological processing skills, and in maths with executive control. Associations between cognition and learning were similar across younger and older children and individuals with and without ADHD, although stronger links between learning-related problems and both executive skills and processing speed were observed in children with ADHD. The results establish clear domain-specific cognitive pathways to learning that distinguish individuals in the heterogeneous population of children struggling to learn

The functional locus of emotion effects in visual word processing

Die emotionale Valenz von Wörtern beeinflusst deren kognitive Verarbeitung. Ungeklärt ist, obwohl von zentraler Bedeutung für die Disziplinen der Psycholinguistik und der Neurowissenschaften, die Frage nach dem funktionellen Lokus von Emotionseffekten in der visuellen Wortverarbeitung. In der vorliegenden Dissertation wurde mit Hilfe von Ereignis-korrelierten Potentialen (EKPs) untersucht, ob emotionale Valenz auf lexikalischen oder auf semantischen Wortverarbeitungsstufen wirksam wird. Vorausgegangene Studien weisen auf einen post-lexikalischen Lokus von Emotionseffekten hin, wobei einige wenige heterogene Befunde von sehr frühen Emotionseffekten auch einen lexikalischen Lokus vermuten lassen. In der vorliegenden Arbeit wurden drei emotions-sensitive EKP Komponenten beobachtet, die distinkte zeitliche und räumliche Verteilungen aufwiesen, und daher verschiedene Wortverarbeitungsstufen zu reflektieren scheinen. Die Ergebnisse wurden im Rahmen von allgemeinen Annahmen aktueller Wortverarbeitungs- und semantischer Repräsentationsmodelle diskutiert. Als zentrales Ergebnis kann benannt werden, dass Emotion am stärksten semantische Wortverarbeitungsstufen beeinflusste. Hieraus wurde geschlussfolgert, dass emotionale Valenz einen Teil der Wortbedeutung darstellt. Eine Interaktion mit einem lexikalischen Faktor sowie sehr frühe Emotionseffekte deuten auf einen zusätzlichen Lokus auf lexikalischen oder sogar perzeptuellen Wortverarbeitungsstufen hin. Dies bedeutet, Emotion veränderte die visuelle Wortverarbeitung auf multiplen Stufen, dabei konnten separate emotions-sensitive EKP Komponenten, die unterschiedlichen Randbedingungen unterliegen, mit jeweils einem frühen (pre-)lexikalischen und einem späten semantischen Lokus in der Wortverarbeitung in Verbindung gesetzt werden. Die Befunde stützen Wortverarbeitungsmodelle, die zeitlich flexible und interaktive Wortverarbeitungsstufen annehmen.Emotional valence of words influences their cognitive processing. The functional locus of emotion effects in the stream of visual word processing is still elusive, although it is an issue of great importance for the disciplines of psycholinguistics and neuroscience. In the present dissertation event-related potentials (ERPs) were applied to examine whether emotional valence influences visual word processing on either lexical or semantic processing stages. Previous studies argued for a post-lexical locus of emotion effects, whereas a lexical locus has been indicated by a few heterogeneous findings of very early emotion effects. Three emotion-related ERP components were observed that showed distinct temporal and topographic distributions, and thus seem to reflect different processing stages in word recognition. Results are discussed within a framework of common assumptions from word recognition and semantic representation models. As a main finding, emotion impacted most strongly semantic processing stages. Thus, emotional valence can be considered to be a part of the meaning of words. However, an interaction of emotion with a lexical factor and very early emotion effects argued for an additional functional locus on lexical, or even on perceptual processing stages in word recognition. In conclusion, emotion impacted visual word processing on multiple stages, whereas distinct emotion-related ERP components, that are subject to different boundary conditions, were associated each with an early (pre-)lexical locus or a late semantic locus. The findings are in line with models of visual word processing that assume time-flexible and interactive processing stages, and point out the need for integration of word recognition models with models of semantic representation

PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks

Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular machine learning tasks. One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. Although the low dimensional representations learned are applicable to many different tasks, they are not particularly tuned for any task. In this paper, we fill this gap by proposing a semi-supervised representation learning method for text data, which we call the \textit{predictive text embedding} (PTE). Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. The labeled information and different levels of word co-occurrence information are first represented as a large-scale heterogeneous text network, which is then embedded into a low dimensional space through a principled and efficient algorithm. This low dimensional embedding not only preserves the semantic closeness of words and documents, but also has a strong predictive power for the particular task. Compared to recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much more efficient, and has fewer parameters to tune.Comment: KDD 201

Phonological processing in children with specific language impairment with and without reading difficulties

Background: SLI is heterogeneous and identifying subgroups within it may help explain the aetiology of the condition. Phonological processing abilities distinguish between children with SLI who do and do not have reading decoding impairments (RDI). Aims: This study aims to probe different levels of phonological processing in children with SLI with and without RDI to investigate the cognitive basis of these differences. Methods & Procedures: 64 children aged 5-17 years were classified using the results of standardised language and single-word reading tests into those with no SLI and no RDI (No-SLI/No-RDI) (N = 18), no SLI but with RDI (No-SLI/RDI) (N = 4, not included in analyses because of the small number), SLI/No-RDI (N = 20) and SLI/RDI (N = 22). The groups were compared on a range of tasks engaging different levels of phonological processing (input and output processing and phonological awareness). Outcomes & Results: The SLI/RDI group was distinguished from the SLI/No-RDI and No- SLI/No-RDI groups by more errors in the longer items in nonword repetition and by poorer phonological awareness. Nonword discrimination scores indicated a gradient of performance across groups, which was not associated with a qualitatively different pattern of performance. Conclusions & Implications: This is the first study contrasting input and output processes associated with phonological processing. The results suggest that deficits in SLI plus RDI may be associated with impairment in actively maintaining phonological representations for phonological processing, which is not present in those without RDI and which leads to reading decoding difficulties

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201