How hand movements and speech tip the balance in cognitive development:A story about children, complexity, coordination, and affordances

When someone asks us to explain something, such as how a lever or balance scale works, we spontaneously move our hands and gesture. This is also true for children. Furthermore, children use their hands to discover things and to find out how something works. Previous research has shown that children’s hand movements hereby are ahead of speech, and play a leading role in cognitive development. Explanations for this assumed that cognitive understanding takes place in one’s head, and that hand movements and speech (only) reflect this. However, cognitive understanding arises and consists of the constant interplay between (hand) movements and speech, and someone’s physical and social environment. The physical environment includes task properties, for example, and the social environment includes other people. Therefore, I focused on this constant interplay between hand movements, speech, and the environment, to better understand hand movements’ role in cognitive development. Using science and technology tasks, we found that children’s speech affects hand movements more than the other way around. During difficult tasks the coupling between hand movements and speech becomes even stronger than in easy tasks. Interim changes in task properties differently affect hand movements and speech. Collaborating children coordinate their hand movements and speech, and even their head movements together. The coupling between hand movements and speech is related to age and (school) performance. It is important that teachers attend to children’s hand movements and speech, and arrange their lessons and classrooms such that there is room for both

Networks in cognitive science

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.Postprint (author's final draft

The neuro-cognitive representation of word meaning resolved in space and time.

One of the core human abilities is that of interpreting symbols. Prompted with a perceptual stimulus devoid of any intrinsic meaning, such as a written word, our brain can access a complex multidimensional representation, called semantic representation, which corresponds to its meaning. Notwithstanding decades of neuropsychological and neuroimaging work on the cognitive and neural substrate of semantic representations, many questions are left unanswered. The research in this dissertation attempts to unravel one of them: are the neural substrates of different components of concrete word meaning dissociated? In the first part, I review the different theoretical positions and empirical findings on the cognitive and neural correlates of semantic representations. I highlight how recent methodological advances, namely the introduction of multivariate methods for the analysis of distributed patterns of brain activity, broaden the set of hypotheses that can be empirically tested. In particular, they allow the exploration of the representational geometries of different brain areas, which is instrumental to the understanding of where and when the various dimensions of the semantic space are activated in the brain. Crucially, I propose an operational distinction between motor-perceptual dimensions (i.e., those attributes of the objects referred to by the words that are perceived through the senses) and conceptual ones (i.e., the information that is built via a complex integration of multiple perceptual features). In the second part, I present the results of the studies I conducted in order to investigate the automaticity of retrieval, topographical organization, and temporal dynamics of motor-perceptual and conceptual dimensions of word meaning. First, I show how the representational spaces retrieved with different behavioral and corpora-based methods (i.e., Semantic Distance Judgment, Semantic Feature Listing, WordNet) appear to be highly correlated and overall consistent within and across subjects. Second, I present the results of four priming experiments suggesting that perceptual dimensions of word meaning (such as implied real world size and sound) are recovered in an automatic but task-dependent way during reading. Third, thanks to a functional magnetic resonance imaging experiment, I show a representational shift along the ventral visual path: from perceptual features, preferentially encoded in primary visual areas, to conceptual ones, preferentially encoded in mid and anterior temporal areas. This result indicates that complementary dimensions of the semantic space are encoded in a distributed yet partially dissociated way across the cortex. Fourth, by means of a study conducted with magnetoencephalography, I present evidence of an early (around 200 ms after stimulus onset) simultaneous access to both motor-perceptual and conceptual dimensions of the semantic space thanks to different aspects of the signal: inter-trial phase coherence appears to be key for the encoding of perceptual while spectral power changes appear to support encoding of conceptual dimensions. These observations suggest that the neural substrates of different components of symbol meaning can be dissociated in terms of localization and of the feature of the signal encoding them, while sharing a similar temporal evolution

A corpus-driven study of features of Chinese students' undergraduate writing in UK universities

Chinese people now comprise the ‘largest single overseas student group in the UK’ with more than 85,000 Chinese students registered at UK institutions in 2009 (British Council, 2010a). While there have been many studies carried out on short argumentative essays from this group (e.g. Chen, 2009), and on postgraduate theses (e.g. Hyland, 2008b), there has been comparatively little research conducted on the high-stakes genre of undergraduate assignments. This study examines assessed writing from Chinese and British undergraduates studying in UK universities between 2000 and 2008; these are investigated using corpus linguistic procedures, supported by qualitative reading. A particular focus is the use of lexical chunks, or recurring strings of words. Findings from the literature on Chinese students’ written English indicate high use of informal chunks, connecting chunks, and those containing first person pronouns (e.g. Milton, 1999). This study found that while the Chinese students make greater use of particular connectors and the first person plural, both student groups make (limited) use of informal language. These areas of difference are more apparent in year 1/2 assignments than those from year 3, suggesting that students gradually conform to the academy’s expectations. Unexpected findings which have not been previously identified in the literature include Chinese students’ significantly higher use of tables, figures (or ‘visuals’) and lists, compared to the British students’ writing. Detailed exploration of writing within Biology, Economics and Engineering suggests that using visuals and lists are different, yet equally acceptable, ways of writing assignments. Since the writing of both student groups has been judged by discipline specialists to be of a high standard, it is argued that the difference in use of visuals and lists illustrates the range of acceptability at undergraduate level. The thesis proposes that scholars therefore need to consider expanding the notion of what constitutes ‘good’ student writing

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Online discussions through the lens of interaction patterns

Computer-mediated communication is arguably prevailing over face-to-face. However, many of the subtleties that make in-person communication personal, cues such as an ironic tone of voice or an effortless posture, are inherently impossible to render through a screen. The context vanishes from the conversation - what is left is therefore mostly text, enlivened by occasional multimedia. At least, this seems the dominant opinion of both industry and academia, that recently focused considerable resources on a deeper understanding of natural and visual language. I argue instead that richer cues are missing from online interaction only because current applications do not acknowledge them -- indeed, communication online is already infused with nonverbal codes, and the effort needed to leverage them is well worth the amount of information they carry. This dissertation therefore focuses on what is left out of the traditional definition of content: I refer to these aspects of communication as content-agnostic. Specifically, this dissertation makes three contributions. First, I formalize what constitutes content-agnostic information in computer-mediated communication, and prove content-agnostic information is as personal to each user as its offline counterpart. For this reason, I choose as a venue of research the web forum, a supposedly text-based, impersonal communication environment, and show that it is possible to attribute a message to the corresponding author solely on the basis of its content-agnostic features -- in other words, without looking at the content of the message at all. Next, I display how abundant and how varied is the content-agnostic information that lies untapped in current applications.To this end, I analyze the content-agnostic aspects of one type of interaction, the quote, and draw conclusions on how these may support discussion, signal user status, mark relationships between users, and characterize the discussion forum as a community. One interesting implication is that discussion platforms may not need to introduce new features for supporting social signals, and conversely social networks may better integrate discussion by enhancing its content-agnostic qualities. Finally, I demonstrate how content-agnostic information reveals user behavior. I focus specifically on trolls, malicious users that disrupt communities through deceptive or manipulative actions. In fact, the language of trolls blends in with that of civil users in heated discussions, which makes collecting irrefutable evidence of trolling difficult even for human moderators. Nonetheless, I show that a combination of content-agnostic and linguistic features sets apart discussions that will eventually be trolled, and reactions to trolling posts. This provides evidence of how content-agnostic information can offer a point of view on user behavior that is at the same time different from, and complementary to, that offered by the actual content of the contribution. Popular up and coming platforms, such as Snapchat, Tumblr, or Yik Yak, are increasingly abandoning persistent, threaded, text-based discussion, in favor of ephemeral, loosely structured, mixed-media content. Although the results of this dissertation are mostly drawn from discussion forums, its research frame and methods should apply directly to these other venues, and to a broad range of communication paradigms. Also, this is but a preliminary step towards a fuller understanding of what additional cues can or should complement content to overcome the limitations of computer-mediated communication