Chunking or not chunking? How do we find words in artificial language learning?

What is the nature of the representations acquired in implicit statistical learning? Recent results in the field of language learning have shown that adults and infants are able to find the words of an artificial language when exposed to a continuous auditory sequence consisting in a random ordering of these words. Such performance can only be based on processing the transitional probabilities between sequence elements. Two different kinds of mechanisms may account for these data: Participants may either parse the sequence into smaller chunks corresponding to the words of the artificial language, or they may become progressively sensitive to the actual values of the transitional probabilities between syllables. The two accounts are difficult to differentiate because they make similar predictions in comparable experimental settings. In this study, we present two experiments that aimed at contrasting these two theories. In these experiments, participants had to learn 2 sets of pseudo-linguistic regularities: Language 1 (L1) and Language 2 (L2) presented in the context of a serial reaction time task. L1 and L2 were either unrelated (none of the syllabic transitions of L1 were present in L2), or partly related (some of the intra-words transitions of L1 were used as inter-words transitions of L2). The two accounts make opposite predictions in these two settings. Our results indicate that the nature of the representations depends on the learning condition. When cues were presented to facilitate parsing of the sequence, participants learned the words of the artificial language. However, when no cues were provided, performance was strongly influenced by the employed transitional probabilities

Computational models of cognition

Existing connectionist computational models of neural networks idealise the biological process in the neuron to a discrete summation, and fail to provide an efficient substrate for computation involving the spectral data that is the input to the biological perceptual process. This work presents a computational model of neural function that introduces a continuous analogue process and explores the computational uses of sub-threshold oscillations of the membrane potential. The goal of tins work is to present an in itial examination of the advantages to the practitioner that are afforded by a new computational model of the neuron that includes sub-threshold oscillations as a component on an equal footing with axonal impulses themselves. The relevant. evidence that these effects are important in a biological neural network is presented. The new resonate-and-fire model is presented and mathematically defined, and shown to be a superset of the ubiquitous integrate-and-fire model. The behaviour patterns of the model are explored initially in single neurons and then networks are examined and shown to be capable of exhibiting useful excitation patterns such as tonic oscillation, selective innervation and resonance. An unsupervised learning algorithm is defined and shown to generate networks that naturally organise to perform Fourier-style transforms central to spectral manipulations. Finally, the model is examined with respect to the current theories of computational neuroscience and cognitive science, and its p otential uses in these domains described

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

A sensorimotor account of visual attention in natural behaviour

The real-world sensorimotor paradigm is based on the premise that sufficient ecological complexity is a prerequisite for inducing naturally relevant sensorimotor relations in the experimental context. The aim of this thesis is to embed visual attention research within the real-world sensorimotor paradigm using an innovative mobile gaze-tracking system (EyeSeeCam, Schneider et al., 2009). Common laboratory set-ups in the field of attention research fail to create natural two-way interaction between observer and situation because they deliver pre-selected stimuli and human observer is essentially neutral or passive. EyeSeeCam, by contrast, permits an experimental design whereby the observer freely and spontaneously engages in real-world situations. By aligning a video camera in real time to the movements of the eyes, the system directly measures the observer’s perspective in a video recording and thus allows us to study vision in the context of authentic human behaviour, namely as resulting from past actions and as originating future actions. The results of this thesis demonstrate that (1) humans, when freely exploring natural environments, prefer directing their attention to local structural features of the world, (2) eyes, head and body perform distinct functions throughout this process, and (3) coordinated eye and head movements do not fully stabilize but rather continuously adjust the retinal image also during periods of quasi-stable “fixation”. These findings validate and extend the common laboratory concept of feature salience within whole-body sensorimotor actions outside the laboratory. Head and body movements roughly orient gaze, potentially driven by early stages of processing. The eyes then fine-tune the direction of gaze, potentially during higher-level stages of visual-spatial behaviour (Studies 1 and 2). Additional head-centred recordings reveal distinctive spatial biases both in the visual stimulation and the spatial allocation of gaze generated in a particular real-world situation. These spatial structures may result both from the environment and form the idiosyncrasies of the natural behaviour afforded by the situation. By contrast, when the head-centred videos are re-played as stimuli in the laboratory, gaze directions reveal a bias towards the centre of the screen. This “central bias” is likely a consequence of the laboratory set-up with its limitation to eye-in-head movements and its restricted screen (Study 3). Temporal analysis of natural visual behaviour reveals frequent synergistic interactions of eye and head that direct rather than stabilize gaze in the quasi-stable eye movement periods following saccades, leading to rich temporal dynamics of real-world retinal input (Study 4) typically not addressed in laboratory studies. Direct comparison to earlier data with respect to the visual system of cats (CatCam), frequently taken as proxy for human vision, shows that stabilizing eye movements play an even less dominant role in the natural behaviour of cats. This highlights the importance of realistic temporal dynamics of vision for models and experiments (Study 5). The approach and findings presented in this thesis demonstrate the need for and feasibility of real- world research on visual attention. Real-world paradigms permit the identification of relevant features triggered in the natural interplay between internal-physiological and external-situational sensorimotor factors. Realistic spatial and temporal characteristics of eye, head and body interactions are essential qualitative properties of reliable sensorimotor models of attention but difficult to obtain under laboratory conditions. Taken together, the data and theory presented in this thesis suggest that visual attention does not represent a pre-processing stage of object recognition but rather is an integral component of embodied action in the real world

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces

The word-space model is a computational model of word meaning that utilizes the distributional patterns of words collected over large text data to represent semantic similarity between words in terms of spatial proximity. The model has been used for over a decade, and has demonstrated its mettle in numerous experiments and applications. It is now on the verge of moving from research environments to practical deployment in commercial systems. Although extensively used and intensively investigated, our theoretical understanding of the word-space model remains unclear. The question this dissertation attempts to answer is: what kind of semantic information does the word-space model acquire and represent? The answer is derived through an identification and discussion of the three main theoretical cornerstones of the word-space model: the geometric metaphor of meaning, the distributional methodology, and the structuralist meaning theory. It is argued that the word-space model acquires and represents two different types of relations between words – syntagmatic and paradigmatic relations – depending on how the distributional patterns of words are used to accumulate word spaces. The difference between syntagmatic and paradigmatic word spaces is empirically demonstrated in a number of experiments, including comparisons with thesaurus entries, association norms, a synonym test, a list of antonym pairs, and a record of part-of-speech assignments.För att köpa boken skicka en beställning till [email protected]/ To order the book send an e-mail to [email protected]

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Human-Computer Interaction

In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools