Robert Thibadeau

A Model of the Time Course and Content of Reading

This paper describes a computer simulation of reading that is strongly driven by eye fixation data from human readers. The simulation, READER, is a natural language understanding system that reads a text word by word and whose processing cycles on each word have some correspondence with the human gaze duration on that word. READER operates within a newly developed information processing architecture, a Collaborative, Activation-based, Production System (CAPS) that permits the modeling of the temporal properties of human comprehension. CAPS allows for concurrent, collaborative execution of processes operating at different levels of analysis. As READER encounters each successive word, the word is operated on by processes at the levels of word encoding, lexical access, syntactic and semantic analysis, and referential and schema-level processes. Like human readers, READER uses a strategy of immediacy of comprehension, attempting to interpret each word as soon as it is encountered, rather than unnecessarily buffering information. A major contribution of this simulation is its use of human performance characteristics in constraining and determining the models mechanisms.

Artificial Perception of Actions

This paper has to do with the visual perception of actions that are discretely conceptualized. The intent is to develop a vision system that produces causal or intentional descriptions of actions, thus providing the conceptual underpinnings of natural language descriptions. The computational theory is developed in linking a “point of action definition” analysis to an analysis of how the physical events will elicit appropriate verbal descriptions. Out of this theory of direct computational linkages between physical events, points of action definition, and verbal descriptions, comes a theory of perception that provides some insight into how to go about constructing systems that can watch the world and report on what they are watching.

Optical Chinese character recognition using probabilistic neural networks

Building on previous work in Chinese character recognition, we describe an advanced system of classification using probabilistic neural networks. Training of the classifier starts with the use of distortion modeled characters from four fonts. Statistical measures are taken on a set of features computed from the distorted character. Based on these measures, the space of feature vectors is transformed to the optimal discriminant space for a nearest neighbor classifier. In the discriminant space, a probabilistic neural network classifier is trained. For classification, we present some modifications to the standard approach implied by the probabilistic neural network structure which yields significant speed improvements. We then compare this approach to using discriminant analysis and Geva and Sittes Decision Surface Mapping classifiers. All methods are tested using 39,644 characters in three different fonts.

Spacetime visualization of relativistic effects

We have developed an innovative ray-tracing algorithm to describe <italic>R</italic>elativistic <italic>E</italic>ffects in <italic>S</italic>pace<italic>T</italic>ime (“REST”). Our algorithm, called <italic>REST-frame</italic>, models light rays that have assumed infinite speed in conventional ray-tracing to have a <italic>finite</italic> speed c in spacetime, and uses general Lorentz Transformation, which connects the spacetime description of a single event in two inertial coordinate systems (<italic>frames</italic>) that differ by a constant velocity, to perform the relativistic translation and aberration of light rays. In this paper, we report the extension of our previous work for visualizing relativistic motion in spacetime to include relativistic Doppler color shift and the simulation of complex kinematic systems in which objects of different relativistic velocities coexist. Our simulations have produced non-intuitive images showing anisotropic deformation (<italic>warping</italic>) of space and intensity concentration/spreading of light sources in spacetime. Images of objects undergoing relativistic Doppler shift are also generated. By applying state-of-the-art computation technology and simulation techniques to the earlier quests in Physics that were conducted mainly by <italic>thought experiment</italic>, we demonstrate, through our new revelations, that <italic>REST-frame</italic> offers a powerful <italic>experimentation tool</italic> to study and explore some of the most exciting aspects of the natural world; particularly, the rich physical properties associated with the finite speed of light.

Time dilation visualization in relativity

The authors extend their previous effort in visualizing the spatial aspect of relativistic effects, and treat the phenomenon of time dilation, an inherent temporal effect of special relativity. Here, the authors demonstrate through still-frame images and live animations that, in observing the viewing-independent time dilation, the finite light transit time involved in performing the observation makes the observed time dilation also depend on the viewing condition. As the authors introduce the physics of special relativity into ray-tracing and make time to pass as a ray travels through space, they are able to link the spatial and temporal dimensions in a fundamental and consistent way in their simulations, and generate images that reveal the spatial and temporal properties of the 4D geometry of spacetime. The present exploration highlights the interplay of the imaging process and the imaged physical events in producing the final images.<<ETX>>

CAPS: A language for modeling highly skilled knowledge-intensive behavior

CAPS is a computer language specialized for modeling highly skilled and knowledge-intensive human activities such as reading (Thibadeau, Just, & Carpenter, 1982). The present paper provides a critical analysis of the particular style of psychological modeling of which CAPS is an example. Suggestions are presented for modeling environments for psychologists.

Intelligent Visual Inspection Machines

Visual inspection is a common feature of manufacturing for which machines are currently illsuited. A major problem has been in bringing enough knowledge to bear on the camera input, fast enough, to be of practical value. This paper describes a machine architecture for such expert vision research in the domain of pattern inspection: in particular, the inspection of printed wiring patterns. The machine includes a powerful model of image-to-symbol transduction and a Lisp multiprocessor environment.

The Television As Robot Servant

Abstract : Television and computing have an interesting intersection in the topic of how to put computer software into the television infrastructure. It is widely recognized that the result is a home information appliance. However, despite previous commercial experimentation, such an appliance has not emerged as successful. Perhaps there is only a deficiency in properly conceptualizing the appliance. In keeping with this theme, this paper presents a theory of why people value information. Using this theory as a design aid in the creation of future home information appliances should improve their chances of commercial success.