Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed. Many studies using eye movements to investigate cognitive processes have appeared over the past 20 years. In an earlier review, I (Rayner, 1978b) argued that since the mid-1970s we have been in a third era of eye movement research and that the success of research in the current era would depend on the ingenuity of researchers in designing interesting and informative

In two experiments, eye movements were monitored as participants followed spoken instructions to click on and move pictures with a computer mouse. In Experiment 1, a referent picture (e.g., the picture of a bench) was presented along with three pictures, two of which had names that shared the same initial phonemes as the name of the referent (e.g., bed and bell). Participants were more likely to fixate the picture with the higher frequency name (bed) than the picture with the lower frequency name (bell). In Experiment 2, referent pictures were presented with three unrelated distractors. Fixation latencies to referents with high-frequency names were shorter than those to referents with low-frequency names. The proportion of fixations to the referents and distractors were analyzed in 33-ms time slices to provide fine-grained information about the time course of frequency effects. These analyses established that frequency affects the earliest moments of lexical access and rule out a late-acting, decision-bias locus for frequency. Simulations using models in which frequency operates on resting-activation levels, on connection strengths, and as a postactivation decision bias provided further constraints on the locus of frequency effects. © 2001 Academic Press Key Words: lexical frequency; spoken-word recognition; eye tracking. As the sound pattern of a spoken word unfolds over time, recognition takes place against a backdrop of partially activated alternatives that compete for recognition. The most activated alternatives are those that most closely match the input. For instance, as a listener hears the word cap, lexical representations of words with similar sounds, such as cat, will be briefly activated. This work was supported by NSF Grant SBR-9729095 to Michael K. Tanenhaus and Richard N. Aslin, and an NSF GRF to James S. Magnuson. We thank Ellen Hogan for her help

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Motivated by and grounded in observations of eye-gaze patterns in human-human dialogue, this study explores using eye-gaze patterns in managing human-computer dialogue. We developed an interactive system, iTourist, for city trip planning, which encapsulated knowledge of eye-gaze patterns gained from studies of human-human collaboration systems. User study results show that it was possible to sense users ’ interest based on eye-gaze patterns and manage computer information output accordingly. Study participants could successfully plan their trip with iTourist and positively rated their experience of using it. We demonstrate that eyegaze could play an important role in managing future multimodal human-computer dialogues.

Two striking contrasts currently exist in the sentence processing literature. First, whereas adult readers rely heavily on lexical information in the generation of syntactic alternatives, adult listeners in world-situated eye-gaze studies appear to allow referential evidence to override strong countervailing lexical biases (Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995). Second, in contrast to adults, children in similar listening studies fail to use this referential information and appear to rely exclusively on verb biases or perhaps syntactically based parsing principles (Trueswell, Sekerina, Hill, & Logrip, 1999). We explore these contrasts by fully crossing verb bias and referential manipulations in a study using the eye-gaze listening technique with adults (Experiment 1) and Wve-year-olds (Experiment 2). Results indicate that adults combine lexical and referential information to determine syntactic choice. Children rely A portion of this work was presented in proceedings to the 23rd Annual Conference of the Cognitive Science Society. The ideas in this paper owe much to our conversations with Lila Gleitman and to the comments of the many audiences who heard preliminary reports of this research. We thank Kirsten Thorpe for her assistance with testing, coding, and participant recruitment and Sylvia Yuan for her assistance in data analysis. We also gratefully acknowledge Tracy Dardick who carried out the norming studies and Jared Novick and David January who assisted in comparisons between head-mounted eye-tracking and our procedure. This work was supported by NIH Grant 1-R01-HD37507 to the second author and a National Science Foundation Science and Technology grant to the Institute for Research in Cognitive Science at the University of Pennsylvania (NSF-STC Coo...

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Abstract—An important goal in studying both human intelligence and artificial intelligence is to understand how a natural or an artificial learning system deals with the uncertainty and ambiguity of the real world. For a natural intelligence system such as a human toddler, the relevant aspects in a learning environment are only those that make contact with the learner’s sensory system. In real-world interactions, what the child perceives critically depends on his own actions as these actions bring information into and out of the learner’s sensory field. The present analyses indicate how, in the case of a toddler playing with toys, these perception-action loops may simplify the learning environment by selecting relevant information and filtering irrelevant information. This paper reports new findings using a novel method that seeks to describe the visual learning environment from a young child’s point of view and measures the visual information that a child perceives in real-time toy play with a parent. The main results are 1) what the child perceives primarily depends on his own actions but also his social partner’s actions; 2) manual actions, in particular, play a critical role in creating visual experiences in which one object dominates; 3) this selecting and filtering of visual objects through the actions of the child provides more constrained and clean input that seems likely to facilitate cognitive learning processes. These findings have broad implications for how one studies and thinks about human and artificial learning systems.

in human-robot interaction

The time course of spoken word recognition depends largely on the frequencies of a word and its competitors, or neighbors (similar-sounding words). However, variability in natural lexicons makes systematic analysis of frequency and neighbor similarity difficult. Artificial lexicons were used to achieve precise control over word frequency and phonological similarity. Eye tracking provided time course measures of lexical activation and competition (during spoken instructions to perform visually guided tasks) both during and after word learning, as a function of word frequency, neighbor type, and neighbor frequency. Apparent shifts from holistic to incremental competitor effects were observed in adults and neural network simulations, suggesting such shifts reflect general properties of learning rather than changes in the nature of lexical representations. Current models of spoken word recognition share a set of core assumptions that correspond to what Marslen-Wilson (1993) called the macrostructure of spoken word recognition: As speech is heard, multiple lexical candidates are activated and compete for recognition with strengths proportional to their similarity with the input and their prior probabilities (frequencies of occurrence).