How Does the Cerebral Cortex Work? Developement, Learning, Attention, and 3D Vision by Laminar Circuits of Visual Cortex

A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sub-lamina. Here it is proposed how these layered circuits help to realize the processes of developement, learning, perceptual grouping, attention, and 3D vision through a combination of bottom-up, horizontal, and top-down interactions. A key theme is that the mechanisms which enable developement and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical developement, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

Linking Attention to Learning, Expectation, Competition, and Consciousness

The concept of attention has been used in many senses, often without clarifying how or why attention works as it does. Attention, like consciousness, is often described in a disembodied way. The present article summarizes neural models and supportive data and how attention is linked to processes of learning, expectation, competition, and consciousness. A key them is that attention modulates cortical self-organization and stability. Perceptual and cognitive neocortex is organized into six main cell layers, with characteristic sub-lamina. Attention is part of unified design of bottom-up, horizontal, and top-down interactions among indentified cells in laminar cortical circuits. Neural models clarify how attention may be allocated during processes of visual perception, learning and search; auditory streaming and speech perception; movement target selection during sensory-motor control; mental imagery and fantasy; and hallucination during mental disorders, among other processes.

Spiking Dynamics during Perceptual Grouping in the Laminar Circuits of Visual Cortex

Grouping of collinear boundary contours is a fundamental process during visual perception. Illusory contour completion vividly illustrates how stable perceptual boundaries interpolate between pairs of contour inducers, but do not extrapolate from a single inducer. Neural models have simulated how perceptual grouping occurs in laminar visual cortical circuits. These models predicted the existence of grouping cells that obey a bipole property whereby grouping can occur inwardly between pairs or greater numbers of similarly oriented and co-axial inducers, but not outwardly from individual inducers. These models have not, however, incorporated spiking dynamics. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity to inducer configurations occur despite irregularities in spike timing across all the interacting cells. Other models have demonstrated spiking dynamics in laminar neocortical circuits, but not how perceptual grouping occurs. The current model begins to unify these two modeling streams by implementing a laminar cortical network of spiking cells whose intracellular temporal dynamics interact with recurrent intercellular spiking interactions to quantitatively simulate data from neurophysiological experiments about perceptual grouping, the structure of non-classical visual receptive fields, and gamma oscillations.

Do development and learning really decrease memory? On similarity and category-based induction in adults and children

In a recently published study, Sloutsky and Fisher [Sloutsky, V. M., & Fisher, A.V. (2004a). When development and learning decrease memory: Evidence against category-based induction in children. Psychological Science, 15, 553-558; Sloutsky, V. M., & Fisher, A. V. (2004b). Induction and categorization in young children: A similarity-based model. Journal of Experimental Psychology: General, 133, 166-188.] demonstrated that children have better memory for the items that they generalise to than do adults. On the basis of this finding, they claim that children and adults use different mechanisms for inductive generalisations;whereas adults focus on shared category membership, children project properties on the basis of perceptual similarity. Sloutsky & Fisher attribute children's enhanced recognition memory to the more detailed processing required by this similarity-based mechanism. In Experiment I we show that children look at the stimulus items for longer than adults. In Experiment 2 we demonstrate that although when given just 250 ms to inspect the items children remain capable of making accurate inferences, their subsequent memory for those items decreases significantly. These findings suggest that there are no necessary conclusions to be drawn from Sloutsky & Fisher's results about developmental differences in generalisation strategy. (C) 2007 Elsevier B.V. All rights reserved.

Automaticity, spelling, and second language learning

This study investigated the relationship between a chronometric estimate of automaticity for the spelling of French words (Automaticity) and performance on four tests of French language attainment among a sample of Year 11 students of French as a foreign language. Fifty participants each completed a computerized test of French spelling and attainment tests in four aspects of French language learning: reading comprehension, writing fluency, oral fluency, and aural comprehension. Correlations were significant between Automaticity and performance on all four tests of French language attainment as well as on overall attainment.

Did children’s perceptions of an after-school social learning program predict change in their behavior?

This research explored the influence of children’s perceptions of a pro-social behavior after-school program on actual change in the children’s behavioral outcomes over the program’s duration. Children’s perceptions of three program processes were collected as well as self-reported pro-social and anti-social behavior before and after the program. Statistical models showed that: Positive perceptions of the program facilitators’ dispositions significantly predicted reductions in anti-social behavior; and positive perceptions with the program activities significantly predicted gains in pro-social behavior. The children’s perceptions of their peers’ behavior in the sessions were not found to a significant predictor of behavioral change. The two significant perceptual indicators predicted a small percentage of the change in the behavioral outcomes. However, as after-school social learning programs have a research history of problematic implementation children’s perceptions should be considered in future program design, evaluation and monitoring.

Sonification as Concurrent Augmented Feedback for Motor Skill Learning and the Importance of Mapping Design

In recent years, sonification of movement has emerged as a viable method for the provision of feedback in motor learning. Despite some experimental validation of its utility, controlled trials to test the usefulness of sonification in a motor learning context are still rare. As such, there are no accepted conventions for dealing with its implementation. This article addresses the question of how continuous movement information should be best presented as sound to be fed back to the learner. It is proposed that to establish effective approaches to using sonification in this context, consideration must be given to the processes that underlie motor learning, in particular the nature of the perceptual information available to the learner for performing the task at hand. Although sonification has much potential in movement performance enhancement, this potential is largely unrealised as of yet, in part due to the lack of a clear framework for sonification mapping: the relationship between movement and sound. By grounding mapping decisions in a firmer understanding of how perceptual information guides learning, and an embodied cognition stance in general, it is hoped that greater advances in use of sonification to enhance motor learning can be achieved.

Transposing Musical Skill: Sonification of movement as concurrent augmented feedback enhances learning in a bimanual task

Concurrent feedback provided during acquisition can enhance performance of novel tasks. The ‘guidance hypothesis’ predicts that feedback provision leads to dependence and poor performance in its absence. However, appropriately-structured feedback information provided through sound (‘sonification’) may not be subject to this effect. We test this directly using a rhythmic bimanual shape-tracing task in which participants learned to move at a 4:3 timing ratio. Sonification of movement and demonstration was compared to two other learning conditions: (1) sonification of task demonstration alone and (2) completely silent practice (control). Sonification of movement emerged as the most effective form of practice, reaching significantly lower error scores than control. Sonification of solely the demonstration, which was expected to benefit participants by perceptually unifying task requirements, did not lead to better performance than control. Good performance was maintained by participants in the sonification condition in an immediate retention test without feedback, indicating that the use of this feedback can overcome the guidance effect. On a 24-hour retention test, performance had declined and was equal between groups. We argue that this and similar findings in the feedback literature are best explained by an ecological approach to motor skill learning which places available perceptual information at the highest level of importance.

Perceptual user interface framework for immersive information retrieval environments (An experimental framework for testing and rapid iteration)

The use of perceptual inputs is an emerging area within HCI that suggests a developing Perceptual User Interface (PUI) that may prove advantageous for those involved in mobile serious games and immersive social network environments. Since there are a large variety of input devices, software platforms, possible interactions, and myriad ways to combine all of the above elements in pursuit of a PUI, we propose in this paper a basic experimental framework that will be able to standardize study of the wide range of interactive applications for testing efficacy in learning or information retrieval and also suggest improvements to emerging PUIs by enabling quick iteration. This rapid iteration will start to define a targeted range of interactions that will be intuitive and comfortable as perceptual inputs, and enhance learning and information retention in comparison to traditional GUI systems. The work focuses on the planning of the technical development of two scenarios, and the first steps in developing a framework to evaluate these and other PUIs for efficacy and pedagogy.

A study of self-directed learning at an Ontario college of applied arts and technology

Ontario Colleges of Applied Arts and Technology (CAATs) are currently in the process of restructuring to ensure quality, accountability, and accessibility of college education. References to learner involvement and self-directed learning are prevalent. "Alternative delivery" and "paradigm shift" are current buzzwords within the Ontario CAAT system as an environment is created supportive of change. Instability of funding has also dictated a need for change. Therefore, a focus has become quality of learning with less demand on public resources. This qualitative case study was conducted at an Ontario CAAT to gather descriptive, perceptual data from post-secondary community college educators who were identified as supportive of self-directed learning and from post-secondary, traditional-aged college students who were perceived by their educators to be selfdirected learners. This college was selected because of initiatives to modify its academic paradigm to encourage what was reputed in the Ontario CAAT system to be self-directed learning. The purpose of this study was to investigate how postsecondary, traditional-aged college students and their educators perceive self-directed learning as part of the teaching-learning experience within a community college setting. Educator participants of the study were selected based on the results of a teaching and learning survey intended to identify educators supportive of self-directed learning. A total of 317 surveys were distributed to every full-time educator at the sample college; 192 completed surveys were returned for a return rate of 61 %. Of these, 8% indicated instructional beliefs and values supportive of self-directed learning. A purposive sample of six educators was selected using a maximulp variation sampling strategy. A network selection sampling strategy was used to select a purposive sample of seven post-secondary students who were identified by the sample educators as selfdirected learners. The results of the study show that students and educators have similar perspectives and operating definitions of self-directed learning and all participants believe they either practice or facilitate self-directed learning. However, their perspectives and practices are not consistent with the literature which emphasizes learner autonomy or control in course structure and content. A central characteristic of the participants represented in this study is the service-oriented professions with which each is associated. Experientiallearning opportunities were highly valued for the options provided in increasing learner independence and competencies in reflective practice. Although there were discrepancies between espoused theory and theory in practice in terms of course structure, the process of self-directed learning was being practiced and supported outside the classroom structure in clinical settings, labs and related experiences.

Verbal ability, previous practice and load on short-term memory as determiners of differences in a complex learning task: an experimental study

Traditional psychometric theory and practice classify people according to broad ability dimensions but do not examine how these mental processes occur. Hunt and Lansman (1975) proposed a 'distributed memory' model of cognitive processes with emphasis on how to describe individual differences based on the assumption that each individual possesses the same components. It is in the quality of these components ~hat individual differences arise. Carroll (1974) expands Hunt's model to include a production system (after Newell and Simon, 1973) and a response system. He developed a framework of factor analytic (FA) factors for : the purpose of describing how individual differences may arise from them. This scheme is to be used in the analysis of psychometric tes ts . Recent advances in the field of information processing are examined and include. 1) Hunt's development of differences between subjects designated as high or low verbal , 2) Miller's pursuit of the magic number seven, plus or minus two, 3) Ferguson's examination of transfer and abilities and, 4) Brown's discoveries concerning strategy teaching and retardates . In order to examine possible sources of individual differences arising from cognitive tasks, traditional psychometric tests were searched for a suitable perceptual task which could be varied slightly and administered to gauge learning effects produced by controlling independent variables. It also had to be suitable for analysis using Carroll's f ramework . The Coding Task (a symbol substitution test) found i n the Performance Scale of the WISe was chosen. Two experiments were devised to test the following hypotheses. 1) High verbals should be able to complete significantly more items on the Symbol Substitution Task than low verbals (Hunt, Lansman, 1975). 2) Having previous practice on a task, where strategies involved in the task may be identified, increases the amount of output on a similar task (Carroll, 1974). J) There should be a sUbstantial decrease in the amount of output as the load on STM is increased (Miller, 1956) . 4) Repeated measures should produce an increase in output over trials and where individual differences in previously acquired abilities are involved, these should differentiate individuals over trials (Ferguson, 1956). S) Teaching slow learners a rehearsal strategy would improve their learning such that their learning would resemble that of normals on the ,:same task. (Brown, 1974). In the first experiment 60 subjects were d.ivided·into high and low verbal, further divided randomly into a practice group and nonpractice group. Five subjects in each group were assigned randomly to work on a five, seven and nine digit code throughout the experiment. The practice group was given three trials of two minutes each on the practice code (designed to eliminate transfer effects due to symbol similarity) and then three trials of two minutes each on the actual SST task . The nonpractice group was given three trials of two minutes each on the same actual SST task . Results were analyzed using a four-way analysis of variance . In the second experiment 18 slow learners were divided randomly into two groups. one group receiving a planned strategy practioe, the other receiving random practice. Both groups worked on the actual code to be used later in the actual task. Within each group subjects were randomly assigned to work on a five, seven or nine digit code throughout. Both practice and actual tests consisted on three trials of two minutes each. Results were analyzed using a three-way analysis of variance . It was found in t he first experiment that 1) high or low verbal ability by itself did not produce significantly different results. However, when in interaction with the other independent variables, a difference in performance was noted . 2) The previous practice variable was significant over all segments of the experiment. Those who received previo.us practice were able to score significantly higher than those without it. J) Increasing the size of the load on STM severely restricts performance. 4) The effect of repeated trials proved to be beneficial. Generally, gains were made on each successive trial within each group. S) In the second experiment, slow learners who were allowed to practice randomly performed better on the actual task than subjeots who were taught the code by means of a planned strategy. Upon analysis using the Carroll scheme, individual differences were noted in the ability to develop strategies of storing, searching and retrieving items from STM, and in adopting necessary rehearsals for retention in STM. While these strategies may benef it some it was found that for others they may be harmful . Temporal aspects and perceptual speed were also found to be sources of variance within individuals . Generally it was found that the largest single factor i nfluencing learning on this task was the repeated measures . What e~ables gains to be made, varies with individuals . There are environmental factors, specific abilities, strategy development, previous learning, amount of load on STM , perceptual and temporal parameters which influence learning and these have serious implications for educational programs .

Deep learning of representations and its application to computer vision

L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de l’intelligence artificielle. Cette thèse contient quatre articles qui présentent chacun une différente nouvelle méthode d’inférence perceptive en utilisant l’apprentissage machine et, plus particulièrement, les réseaux neuronaux profonds. Chacun de ces documents met en évidence l’utilité de sa méthode proposée dans le cadre d’une tâche de vision par ordinateur. Ces méthodes sont applicables dans un contexte plus général, et dans certains cas elles on tété appliquées ailleurs, mais ceci ne sera pas abordé dans le contexte de cette de thèse. Dans le premier article, nous présentons deux nouveaux algorithmes d’inférence variationelle pour le modèle génératif d’images appelé codage parcimonieux “spike- and-slab” (CPSS). Ces méthodes d’inférence plus rapides nous permettent d’utiliser des modèles CPSS de tailles beaucoup plus grandes qu’auparavant. Nous démontrons qu’elles sont meilleures pour extraire des détecteur de caractéristiques quand très peu d’exemples étiquetés sont disponibles pour l’entraînement. Partant d’un modèle CPSS, nous construisons ensuite une architecture profonde, la machine de Boltzmann profonde partiellement dirigée (MBP-PD). Ce modèle a été conçu de manière à simplifier d’entraînement des machines de Boltzmann profondes qui nécessitent normalement une phase de pré-entraînement glouton pour chaque couche. Ce problème est réglé dans une certaine mesure, mais le coût d’inférence dans le nouveau modèle est relativement trop élevé pour permettre de l’utiliser de manière pratique. Dans le deuxième article, nous revenons au problème d’entraînement joint de machines de Boltzmann profondes. Cette fois, au lieu de changer de famille de modèles, nous introduisons un nouveau critère d’entraînement qui donne naissance aux machines de Boltzmann profondes à multiples prédictions (MBP-MP). Les MBP-MP sont entraînables en une seule étape et ont un meilleur taux de succès en classification que les MBP classiques. Elles s’entraînent aussi avec des méthodes variationelles standard au lieu de nécessiter un classificateur discriminant pour obtenir un bon taux de succès en classification. Par contre, un des inconvénients de tels modèles est leur incapacité de générer deséchantillons, mais ceci n’est pas trop grave puisque la performance de classification des machines de Boltzmann profondes n’est plus une priorité étant donné les dernières avancées en apprentissage supervisé. Malgré cela, les MBP-MP demeurent intéressantes parce qu’elles sont capable d’accomplir certaines tâches que des modèles purement supervisés ne peuvent pas faire, telles que celle de classifier des données incomplètes ou encore celle de combler intelligemment l’information manquante dans ces données incomplètes. Le travail présenté dans cette thèse s’est déroulé au milieu d’une période de transformations importantes du domaine de l’apprentissage à réseaux neuronaux profonds qui a été déclenchée par la découverte de l’algorithme de “dropout” par Geoffrey Hinton. Dropout rend possible un entraînement purement supervisé d’architectures de propagation unidirectionnel sans être exposé au danger de sur- entraînement. Le troisième article présenté dans cette thèse introduit une nouvelle fonction d’activation spécialement con ̧cue pour aller avec l’algorithme de Dropout. Cette fonction d’activation, appelée maxout, permet l’utilisation de aggrégation multi-canal dans un contexte d’apprentissage purement supervisé. Nous démontrons comment plusieurs tâches de reconnaissance d’objets sont mieux accomplies par l’utilisation de maxout. Pour terminer, sont présentons un vrai cas d’utilisation dans l’industrie pour la transcription d’adresses de maisons à plusieurs chiffres. En combinant maxout avec une nouvelle sorte de couche de sortie pour des réseaux neuronaux de convolution, nous démontrons qu’il est possible d’atteindre un taux de succès comparable à celui des humains sur un ensemble de données coriace constitué de photos prises par les voitures de Google. Ce système a été déployé avec succès chez Google pour lire environ cent million d’adresses de maisons.

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Perceptual Evaluation of Video-Realistic Speech

abstract With many visual speech animation techniques now available, there is a clear need for systematic perceptual evaluation schemes. We describe here our scheme and its application to a new video-realistic (potentially indistinguishable from real recorded video) visual-speech animation system, called Mary 101. Two types of experiments were performed: a) distinguishing visually between real and synthetic image- sequences of the same utterances, ("Turing tests") and b) gauging visual speech recognition by comparing lip-reading performance of the real and synthetic image-sequences of the same utterances ("Intelligibility tests"). Subjects that were presented randomly with either real or synthetic image-sequences could not tell the synthetic from the real sequences above chance level. The same subjects when asked to lip-read the utterances from the same image-sequences recognized speech from real image-sequences significantly better than from synthetic ones. However, performance for both, real and synthetic, were at levels suggested in the literature on lip-reading. We conclude from the two experiments that the animation of Mary 101 is adequate for providing a percept of a talking head. However, additional effort is required to improve the animation for lip-reading purposes like rehabilitation and language learning. In addition, these two tasks could be considered as explicit and implicit perceptual discrimination tasks. In the explicit task (a), each stimulus is classified directly as a synthetic or real image-sequence by detecting a possible difference between the synthetic and the real image-sequences. The implicit perceptual discrimination task (b) consists of a comparison between visual recognition of speech of real and synthetic image-sequences. Our results suggest that implicit perceptual discrimination is a more sensitive method for discrimination between synthetic and real image-sequences than explicit perceptual discrimination.