Abstract
We examined whether the abilities of observers to perform an analogue of a real-world monitoring task involving detection and identification of changes to items in a visual display could be explained better by models based on signal detection theory (SDT) or high threshold theory (HTT). Our study differed from most previous studies in that observers were allowed to inspect the initial display for 3s, simulating the long inspection times typical of natural viewing, and their eye movements were not constrained. For the majority of observers, combined change detection and identification performance was best modelled by a SDT-based process that assumed that memory resources were distributed across all eight items in our displays. Some observers required a parameter to allow for sometimes making random guesses at the identities of changes they had missed. However, the performance of a small proportion of observers was best explained by a HTT-based model that allowed for lapses of attention.
Highlights
Operators in many work environments are required to monitor and detect changes in visual displays
We included a contemporary variant of high threshold theory (HTT) which allows for lapses of attention [5] and a variant in which limitations in the precision of memory representations [10] result in imperfect change detection for stimuli held in memory. We examined whether these models could parsimoniously describe both change detection and change identification performance
Several measures of change detection and identification performance averaged across observers are shown in Table 1 for each of the two conditions of change probability
Summary
Operators in many work environments (e.g., air traffic control) are required to monitor and detect changes in visual displays. Where such changes are not signalled by transients, their detection depends in part on the operator’s ability to encode the information presented in the display and retain it in memory until the display can be resampled. Change Detection and Identification and increase the probability that visual information is recoded and stored in a more durable form, e.g., as verbal labels. Where such recoding occurs, human performance may be better modelled by robust, noiseless representations than by noisy ones. Requiring models to fit change identification data in addition to change detection data provides a stronger test of their relative validities
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