A Rule-Based Method for Determining the Degree of Student Satisfaction of a Web-Based Learning Environment

Student essays representing their individual reflections on a collaborative web-based course in International Business are computationally analyzed according to a classification scheme based on a set of a priori fuzzy categories. This classification method enables the identification of themes and trends in the student responses that can be used to illustrate an overall evaluation of the personal learning experiences for this course. By processing the classification results using a computational neural network, we can depict the clustering intensity of thematic elements and illustrate the strength of dependencies between classification attribute values topologically using a self-organizing map (SOM), which provides a pattern recognition visualization. The resulting SOM can then be used to compare successive depictions for future iterations of new thematic data from student self-evaluations

The structure and formation of natural categories

Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving

Object Rivalry: Competition Between incompatible Representations of the Same Object

To understand that an object has changed state during an event, we must represent the `before\u27 and `after\u27 states of that object. Because a physical object cannot be in multiple states at any one moment in time, these `before\u27 and `after\u27 object states are mutually exclusive. In the same way that alternative states of a physical object are mutually exclusive, are cognitive representations of alternative object states also incompatible? If so, comprehension of an object state-change involves interference between the constituent object states. Through a series of functional magnetic resonance imaging experiments, we test the hypothesis that comprehension of object state-change requires the cognitive system to resolve conflict between representationally distinct brain states. We discover that (1) comprehension of an object state-change evokes a neural response in prefrontal cortex that is the same as that found for known forms of conflict, (2) the degree to which an object is described as changing in state predicts the strength of the prefrontal cortex conflict response, (3) the dissimilarity of object states predicts the pattern dissimilarity of visual cortex brain states, and (4) visual cortex pattern dissimilarity predicts the strength of the prefrontal cortex conflict response. Results from these experiments suggest that distinct and incompatible representations of an object compete when representing object state-change. The greater the dissimilarity between described object states, the greater the dissimilarity between rival brain states, and the greater the conflict

How the brain grasps tools: fMRI & motion-capture investigations

Humans’ ability to learn about and use tools is considered a defining feature of our species, with most related neuroimaging investigations involving proxy 2D picture viewing tasks. Using a novel tool grasping paradigm across three experiments, participants grasped 3D-printed tools (e.g., a knife) in ways that were considered to be typical (i.e., by the handle) or atypical (i.e., by the blade) for subsequent use. As a control, participants also performed grasps in corresponding directions on a series of 3D-printed non-tool objects, matched for properties including elongation and object size. Project 1 paired a powerful fMRI block-design with visual localiser Region of Interest (ROI) and searchlight Multivoxel Pattern Analysis (MVPA) approaches. Most remarkably, ROI MVPA revealed that hand-selective, but not anatomically overlapping tool-selective, areas of the left Lateral Occipital Temporal Cortex and Intraparietal Sulcus represented the typicality of tool grasping. Searchlight MVPA found similar evidence within left anterior temporal cortex as well as right parietal and temporal areas. Project 2 measured hand kinematics using motion-capture during a highly similar procedure, finding hallmark grip scaling effects despite the unnatural task demands. Further, slower movements were observed when grasping tools, relative to non-tools, with grip scaling also being poorer for atypical tool, compared to non-tool, grasping. Project 3 used a slow-event related fMRI design to investigate whether representations of typicality were detectable during motor planning, but MVPA was largely unsuccessful, presumably due to a lack of statistical power. Taken together, the representations of typicality identified within areas of the ventral and dorsal, but not ventro-dorsal, pathways have implications for specific predictions made by leading theories about the neural regions supporting human tool-use, including dual visual stream theory and the two-action systems model

Interactive Concept Acquisition for Embodied Artificial Agents

An important capacity that is still lacking in intelligent systems such as robots, is the ability to use concepts in a human-like manner. Indeed, the use of concepts has been recognised as being fundamental to a wide range of cognitive skills, including classification, reasoning and memory. Intricately intertwined with language, concepts are at the core of human cognition; but despite a large body or research, their functioning is as of yet not well understood. Nevertheless it remains clear that if intelligent systems are to achieve a level of cognition comparable to humans, they will have to posses the ability to deal with the fundamental role that concepts play in cognition. A promising manner in which conceptual knowledge can be acquired by an intelligent system is through ongoing, incremental development. In this view, a system is situated in the world and gradually acquires skills and knowledge through interaction with its social and physical environment. Important in this regard is the notion that cognition is embodied. As such, both the physical body and the environment shape the manner in which cognition, including the learning and use of concepts, operates. Through active partaking in the interaction, an intelligent system might influence its learning experience as to be more effective. This work presents experiments which illustrate how these notions of interaction and embodiment can influence the learning process of artificial systems. It shows how an artificial agent can benefit from interactive learning. Rather than passively absorbing knowledge, the system actively partakes in its learning experience, yielding improved learning. Next, the influence of embodiment on perception is further explored in a case study concerning colour perception, which results in an alternative explanation for the question of why human colour experience is very similar amongst individuals despite physiological differences. Finally experiments, in which an artificial agent is embodied in a novel robot that is tailored for human-robot interaction, illustrate how active strategies are also beneficial in an HRI setting in which the robot learns from a human teacher

Robotic hand augmentation drives changes in neural body representation

Humans have long been fascinated by the opportunities afforded through augmentation. This vision not only depends on technological innovations but also critically relies on our brain's ability to learn, adapt, and interface with augmentation devices. Here, we investigated whether successful motor augmentation with an extra robotic thumb can be achieved and what its implications are on the neural representation and function of the biological hand. Able-bodied participants were trained to use an extra robotic thumb (called the Third Thumb) over 5 days, including both lab-based and unstructured daily use. We challenged participants to complete normally bimanual tasks using only the augmented hand and examined their ability to develop hand-robot interactions. Participants were tested on a variety of behavioral and brain imaging tests, designed to interrogate the augmented hand's representation before and after the training. Training improved Third Thumb motor control, dexterity, and hand-robot coordination, even when cognitive load was increased or when vision was occluded. It also resulted in increased sense of embodiment over the Third Thumb. Consequently, augmentation influenced key aspects of hand representation and motor control. Third Thumb usage weakened natural kinematic synergies of the biological hand. Furthermore, brain decoding revealed a mild collapse of the augmented hand's motor representation after training, even while the Third Thumb was not worn. Together, our findings demonstrate that motor augmentation can be readily achieved, with potential for flexible use, reduced cognitive reliance, and increased sense of embodiment. Yet, augmentation may incur changes to the biological hand representation. Such neurocognitive consequences are crucial for successful implementation of future augmentation technologies

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012

Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland

The aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012