Biomedical subjects

Michael G Reynolds

Publications and source records attributed to Michael G Reynolds.

2 recordsLinked to original sources

PubMed2006

Metacognitive errors in change detection: missing the gap between lab and life.

Studies of change detection suggest that people tend to overestimate their ability to detect visual changes. In a recent laboratory study of change detection and human intention, Beck et al., found that individuals have an inadequate understanding that intention can improve change detection performance and that its importance increases with scene complexity. We note that these findings may be specific to unfamiliar situations such as those generated routinely in studies of change detection. In two questionnaire studies, we demonstrate that when participants consider real world scenarios such as driving, people are well aware that the intention to detect changes improves detection performance, especially in complex scenes. We suggest several reasons why change detection findings like Beck et al.'s do not generalize to real world situations. More broadly, we suggest a possible way to bridge the gap between lab and life.

Cognition↗

PubMed2006

Neighborhood effects in reading aloud: new findings and new challenges for computational models.

A word from a dense neighborhood is often read aloud faster than a word from a sparse neighborhood. This advantage is usually attributed to orthography, but orthographic and phonological neighbors are typically confounded. Two experiments investigated the effect of neighborhood density on reading aloud when phonological density was varied while orthographic density was held constant, and vice versa. A phonological neighborhood effect was observed, but not an orthographic one. These results are inconsistent with the predominant role ascribed to orthographic neighbors in accounts of visual word recognition and reading aloud. Consistent with this interpretation, 6 different computational models of reading aloud failed to simulate this pattern of results. The results of the present experiments thus provide a new understanding of some of the processes underlying reading aloud, and new challenges for computational models.

Attention↗