PubMed · 5861125
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
J J McGrath. 1965. Performance sharing in an audio-visual vigilance task.. https://doi.org/10.1177/001872086500700206
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Mechanisms of human attention allow selective processing of just the relevant events among the many stimuli bombarding our senses. Most laboratory studies examine attention within just a single sense, but in the real world many important events are specified multimodally, as in verbal communication. Speech comprises visual lip movements as well as sounds, and lip-reading contributes to speech perception, even for listeners with good hearing, by a process of audiovisual integration. Such examples raise the problem of how we coordinate our spatial attention across the sensory modalities, to select sights and sounds from a common source for further processing. Here we show that this problem is alleviated by allowing some cross-modal matching before attentional selection is completed. Cross-modal matching can lead to an illusion, whereby sounds are mislocated at their apparent visual source; this crossmodal illusion can enhance selective spatial attention to speech sounds.
How should a visual search task be terminated when no target is found? Such searches could end after a serial search through all items, but blank trials in many tasks are terminated too quickly for that to be plausible. This paper proposes a solution based on Wolfe's (1994) Guided Search model. The probability that each item is a target is computed in parallel based on items' differences from each other and their similarity to the desired target. This probability is expressed as an activation. Activations are examined in decreasing order until the target is found or until an activation threshold is reached. This threshold is set adaptively by the observer--more conservative following misses, more liberal following successful trials. In addition, observers guess on some trials. The probability of a guess increases as trial duration increases. The model successfully explains blank trial performance. Specific predictions are tested by experiments.
In this paper human pattern recognition is modeled in terms of how human observers learn to describe patterns in terms of their perceived parts, their unary (part) and binary (relational) attributes and the way in which such attribute states "evidence' different classes of shapes. This approach, originally developed in the area of computer vision, is concerned with algorithms which enable the learning of shape descriptions from examples and the classification of new data (generalization) efficiently and accurately. An object in such an "evidence-based' system is represented by a set of rules, where each rule provides a certain amount of evidence for each object class in the database. The accumulated class evidence over all activated rules can then be used to determine the classification probability. We have examined how well this model reflects human perception by training observers to classify compound Gabor patterns and then testing them with versions of such patterns which were segmented (gray-level transformed) versions of the original training set. If the observers were to construct rules to define each pattern class in terms of perceived parts and their relations, then it should be expected that classification performance would generalize to these new patterns from the original set. Results confirm this hypothesis and the specific feature extraction, learning and rule generation model used to predict performance.