Machine learning of visual object categorization: an application of the SUSTAIN model

Formal models of categorization are psychological theories that try to describe the process of categorization in a lawful way, using the language of mathematics. Their mathematical formulation makes it possible for the models to generate precise, quantitative predictions. SUSTAIN (Love, Medin & Gureckis, 2004) is a powerful formal model of categorization that has been used to model a range of human experimental data, describing the process of categorization in terms of an adaptive clustering principle. Love et al. (2004) suggested a possible application of the model in the field of object recognition and categorization. The present study explores this possibility, investigating at the same time the utility of using a formal model of categorization in a typical machine learning task. The image categorization performance of SUSTAIN on a well-known image set is compared with that of a linear Support Vector Machine, confirming the capability of SUSTAIN to perform image categorization with a reasonable accuracy, even if at a rather high computational cost

Solutions and Open Challenges for the Symbol Grounding Problem

This article discusses the current progress and solutions to the symbol grounding problem and specifically identifies which aspects of the problem have been addressed and issues and scientific challenges that still require investigation. In particular, the paper suggests that of the various aspects of the symbol grounding problem, the transition from indexical representations to symbol-symbol relationships requires the most research. This analysis initiated a debate and solicited commentaries from experts in the field to gather consensus on progress and achievements and identify the challenges still open in the symbol grounding problem

A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.Comment: Accepted at The 5th International Conference on Data-Driven Control and Learning Systems. 201

Towards the Grounding of Abstract Words: A Neural Network Model for Cognitive Robots

In this paper, a model based on Artificial Neural Networks (ANNs) extends the symbol grounding mechanism toabstract words for cognitive robots. The aim of this work is to obtain a semantic representation of abstract concepts through the grounding in sensorimotor experiences for a humanoid robotic platform. Simulation experiments have been developed on a software environment for the iCub robot. Words that express general actions with a sensorimotor component are first taught to the simulated robot. During the training stage the robot first learns to perform a set of basic action primitives through the mechanism of direct grounding. Subsequently, the grounding of action primitives, acquired via direct sensorimotor experience, is transferred to higher-order words via linguistic descriptions. The idea is that by combining words grounded in sensorimotor experience the simulated robot can acquire more abstract concepts. The experiments aim to teach the robot the meaning of abstract words by making it experience sensorimotor actions. The iCub humanoid robot will be used for testing experiments on a real robotic architecture

Co-exploring Actuator Antagonism and Bio-inspired Control in a Printable Robot Arm

The human arm is capable of performing fast targeted movements with high precision, say in pointing with a mouse cursor, but is inherently ‘soft’ due to the muscles, tendons and other tissues of which it is composed. Robot arms are also becoming softer, to enable robustness when operating in real-world environments, and to make them safer to use around people. But softness comes at a price, typically an increase in the complexity of the control required for a given task speed/accuracy requirement. Here we explore how fast and precise joint movements can be simply and effectively performed in a soft robot arm, by taking inspiration from the human arm. First, viscoelastic actuator-tendon systems in an agonist-antagonist setup provide joints with inherent damping, and stiffness that can be varied in real-time through co-contraction. Second, a light-weight and learnable inverse model for each joint enables a fast ballistic phase that drives the arm close to a desired equilibrium point and co-contraction tuple, while the final adjustment is done by a feedback controller. The approach is embodied in the GummiArm, a robot which can almost entirely be printed on hobby-grade 3D printers. This enables rapid and iterative co-exploration of ‘brain’ and ‘body’, and provides a great platform for developing adaptive and bio-inspired behaviours

The oculomotor resonance effect in spatial-numerical mapping.

We investigated automatic Spatial-Numerical Association of Response Codes (SNARC) effect in auditory number processing. Two experiments continually measured spatial characteristics of ocular drift at central fixation during and after auditory number presentation. Consistent with the notion of a spatially oriented mental number line, we found spontaneous magnitude-dependent gaze adjustments, both with and without a concurrent saccadic task. This fixation adjustment (1) had a small-number/left-lateralized bias and (2) it was biphasic as it emerged for a short time around the point of lexical access and it received later robust representation around following number onset. This pattern suggests a two-step mechanism of sensorimotor mapping between numbers and space - a first-pass bottom-up activation followed by a top-down and more robust horizontal SNARC. Our results inform theories of number processing as well as simulation-based approaches to cognition by identifying the characteristics of an oculomotor resonance phenomenon

Ocular drift along the mental number line.

We examined the spontaneous association between numbers and space by documenting attention deployment and the time course of associated spatial-numerical mapping with and without overt oculomotor responses. In Experiment 1, participants maintained central fixation while listening to number names. In Experiment 2, they made horizontal target-direct saccades following auditory number presentation. In both experiments, we continuously measured spontaneous ocular drift in horizontal space during and after number presentation. Experiment 2 also measured visual-probe-directed saccades following number presentation. Reliable ocular drift congruent with a horizontal mental number line emerged during and after number presentation in both experiments. Our results provide new evidence for the implicit and automatic nature of the oculomotor resonance effect associated with the horizontal spatial-numerical mapping mechanism