To study the mechanisms underlying covert orienting of attention in visual space, subjects were given advance cues indicating the probable locations of targets that they had to discriminate and localize. Direct peripheral cues (brightening of one of four boxes in peripheral vision) and symbolic central cues (an arrow at the fixation point indicating a probable peripheral box) were compared. Peripheral and central cues are believed to activate different reflexive and voluntary modes of orienting (Jonides, 1981; Posner, 1980). Experiment 1 showed that the time courses of facilitation and inhibition from peripheral and central cues were characteristic and different. Experiment 2 showed that voluntary orienting in response to symbolic central cues is interrupted by reflexive orienting to random peripheral flashes. Experiment 3 showed that irrelevant peripheral flashes also compete with relevant peripheral cues. The amount of interference varied systematically with the interval between the onset of the relevant cue and of the distracting flash (cue-flash onset asynchrony) and with the cuing condition. Taken together, these effects support a model for spatial attention with distinct but interacting reflexive and voluntary orienting mechanisms.

This article describes and evaluates a class of methods for performing automated analysis of eye-movement protocols. Although eye movements have become increasingly popular as a tool for investigating user behavior, they can be extremely difficult and tedious to analyze. In this article we propose an approach to automating eye-movement protocol analysis by means of tracing—relating observed eye movements to the sequential predictions of a process model. We present three tracing methods that provide fast and robust analysis and alleviate the equipment noise and individual variability prevalent in typical eye-movement protocols. We also describe three applications of the tracing methods that demonstrate how the methods facilitate the use of eye movements in the study of user behavior and the inference of user intentions. 1.

Abstract-In the present experiments three different motion discrimination tasks were studied using a random dot pattern as stimulus: velocity discrimination, direction discrimination and discrimination of opposite directions. The analysis of the motion of random dot patterns is based on motion sensitive mechanisms without the confounding interference of position sensitive mechanisms (Nakayama and Tyler, I98 1). Furthermore, since isotropic random dot patterns contain no dominant orientation, a change in the direction of motion does not parallel a change in orientation. Hence the use of a random dot pattern as stimulus allows velocity and direction discrimination to be compared. Human velocity discrimination displays a U-shaped dependence on the stimulus velocity: the JNDs, expressed as Weber-fractions. are minimal for velocities ranging from 4 to 64 deg set-‘. The Weber-fractions in velocity, determined with a staircase procedure tracking a 84 % correct response level, were about 7 % at the optimal speeds. The velocity discrimination curve obtained with the random dot pattern is similar to that obtained with light bars. Human direction discrimination, defined as the smallest difference in direction which can be resolved, also displays a U-shaped dependence on the stimulus velocity. Direction discrimination thresholds decrease up to a velocity of 4 deg’secc’, they then stay at a constant level up to 128 deg,sec-‘. Beyond this velocity the thresholds increase again. The mean direction discrimination threshold was 1.8 deg at

In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. We evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental protocols in terms of the given strategies, the automated tracing methods performed as well as human expert coders in a fraction of the time.

During reaching movements, the brain’s internal models map desired limb motion into predicted forces. When the forces in the task change, these models adapt. Adaptation is guided by generalization: errors in one movement influence prediction in other types of movement. If the mapping is accomplished with population coding, combining basis elements that encode different regions of movement space, then generalization can reveal the encoding of the basis elements. We present a theory that relates encoding to generalization using trial-by-trial changes in behavior during adaptation. We consider adaptation during reaching movements in various velocity-dependent force fields and quantify how errors generalize across direction. We find that the measurement of error is critical to the theory. A typical assumption in motor control is that error is the difference between a current trajectory and a desired trajectory (DJ) that does not change during adaptation. Under this assumption, in all force fields that we examined, including one in which force randomly changes from trial to trial, we found a bimodal generalization pattern, perhaps reflecting basis elements that encode direction bimodally. If the DJ was allowed to vary, bimodality was reduced or eliminated, but the generalization function accounted for nearly twice as much variance. We suggest, therefore, that basis elements representing the internal model of dynamics are sensitive to limb velocity with bimodal tuning; however, it is also possible that during adaptation the error metric itself adapts, which affects the implied shape of the basis elements.

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The perceived direction of a grating moving behind an elongated aperture is biased towards the aperture’s long axis. This “barber pole ” illusion is a consequence of integrating one-dimensional (1D) or grating and two-dimensional (2D) or terminator motion signals. In humans, we recorded the ocular following responses to this stimulus. Tracking was always initiated at ultra-short latencies ( � 85 ms) in the direction of grating motion. With elongated apertures, a later component was initiated 15–20 ms later in the direction of the terminator motion signals along the aperture’s long axis. Amplitude of the later component was dependent upon the aperture’s aspect ratio. Mean tracking direction at the end of the trial (135–175 ms after stimulus onset) was between the directions of the vector sum computed by integrating either terminator motion signals only or both grating and terminator motion signals. Introducing an elongated mask at the center of the “barber pole ” did not affect the latency difference between early and later components, indicating that this latency shift was not due to foveal versus peripheral locations of 1D and 2D motion signals. Increasing the size of the foveal mask up to 90 % of the stimulus area selectively reduced the strength of the grating motion signals and, consequently, the amplitude of the early component. Conversely, reducing the contrast of, or indenting the aperture’s edges, selectively reduced the strength of terminator motion signals and, consequently, the amplitude of the later component. Latencies were never affected by these manipulations. These results tease

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Saccadic localization of spatially extended objects requires the computation of a single saccadic landing position. What representation of the target guides saccades? Saccades were examined for various targets composed of dots to determine whether landing position corresponded to the center-of-gravity (average location) of the dots, the center-of-area of the shape, or the symmetric axis. Targets were composed of dots configured as outline drawings of circles, ellipses, cardioids, wiggly lines, or amorphous blobs. In some cases, dot spacing was varied, extraneous dot clusters were superimposed, or different distributions of dots inside the boundary were added. Quasi-random dot clusters without a well-defined contour were also studied. Instructions were to look at the target as a whole, and keep latency long enough to avoid compromising accuracy. Saccades landed with a high level of precision (S.D.s 7–10 % of target eccentricity) near the center-of-area of the target shape, rather than at the center-of-gravity of the dots or on the symmetric axis. Landing position was unaffected by the spacing of dots along the boundary, the addition of dots within the boundary, or the addition of the extraneous dot clusters. When the target was a cluster of quasi-random dots, saccades landed closer to the center-of-area of the implied surface than to the average location of the dots. Overall, the positions of individual dots were important only insofar as the dots affected overall target shape. The results show that a representation of target shape guides saccades, rather than a more primitive representation of individual elements within

This study investigated recognition systems that combine loosely coupled modalities, integrating eye movements in an Automatic Speech Recognition (ASR) system as an exemplar. A probabilistic framework for combining modalities was formalised and applied to the specific case of integrating eye movement and speech. A corpus of a matched eye movement and related spontaneous conversational British English speech for a visual-based, goal-driven task was collected. This corpus enabled the relationship between the modalities to be verified. Robust extraction of visual attention from eye movement data was investigated using Hidden Markov Models and Hidden Semi-Markov Models. Gaze-contingent ASR systems were developed from a research-grade baseline ASR system by redistributing language model probability mass according to the visual attention. The best performing systems maintained the Word Error Rates but showed an increase in the Figure of Merit- a measure of the keyword spotting accuracy and integration success. The core values of this work may be useful for developing robust multimodal decoding system functions.