Is the Richness of Our Visual World an Illusion? Transsaccadic Memory for Complex Scenes

Blackmore, Susan; Brelstaff, Gavin; Nelson, Kay M.; Trościanko, Tom

doi:10.1068/p241075

Cited by 167 publications

(89 citation statements)

References 17 publications

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“…A second line of evidence against an unlimited-capacity parallel process comes directly from the change detection literature. Changes made to objects in scenes frequently go undetected, regardless of whether these changes have occurred during eye movements (Currie, McConkie, CarlsonRadvansky, & Irwin, 2000;Grimes, 1996;Henderson & Hollingworth, 1999;Hollingworth & Henderson, 2000;McConkie & Currie, 1996) or at some other time during a trial when the motion transient accompanyingthe change would likely be masked (Blackmore, Brelstaff, Nelson, & Troscianko, 1995;O'Regan, Rensink, & Clark, 1999;Pashler, 1988;Rensink et al, 1997;Simons & Chabris, 1999). In one of the few discussions explicitly addressing the implications of these detection failures for an unlimited-capacity model of change detection, Rensink (2000a) writes:…”

Section: Implications Of Change Detection Constraint On Capacity Limimentioning

confidence: 99%

Detecting changes between real-world objects using spatiochromatic filters

Zelinsky

2003

Psychonomic Bulletin & Review

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Our ability to detect change between real-world scenes is rich in information about how we represent the visual world. Of particular recent interest has been the information also available from change detection failures, cases in which observers are seemingly "blind" to the occurrence of a change (Rensink, 2002;. Whereas correct change detection judgments imply a successful representation and comparison of the objects undergoing change, systematic failures to detect change may indicate weaknesses or holes in a representational structure.In the present study, I use behavioral and imageprocessing techniques to explore the representational constraints revealed by a change detection task. I will first discuss three forms of representational limitation likely to affect change detection behavior, loosely characterized as memory-, attention-, and similarity-based constraints. I will argue that renewed attention should be paid especially to the visual similarity relationships between the objects undergoing change. Second, I will explore the theoretical implications that these various forms of constraint have for capacity-limited models of change detection, arguing that higher level processes or parameters should be assumed only after similarity-based constraints have been found inadequate for explaining the observed data. Third, I will report a behavioral experiment demonstrating a similarity-based constraint, one involving an effect of orientation and categorical similarity on change detection. Last, I will outline an imagebased representational substrate capable of capturing these effects of visual similarity on real-world change detection performance. This f inal contribution of the study is particularly important in that a simple technique is described that can be used to quantify the visual similarity relationships between pre-and postchange patterns regardless of their featural complexity. Moreover, this technique is framed in terms of a computational model. Despite the prominent use of realistic stimuli in the change detection literature, no computationally explicit model has been demonstrated to work for real-world changes. The present work is intended to help bridge this gap between change detection theory and the stimuli commonly used to investigate this behavior. Sources of Representational Constraint in a Change Detection TaskThe representational weaknesses revealed by change detection failure are described most often in terms of memory and attentional constraints. Memory-based detection failures will result if the objects undergoing change are not represented in an enduring form (Grimes, 1996;O'Regan, 1992), or if a coherent representation of the prechange object does not exist at the time of the postchange comparison (Rensink, 2000a). Memory might also constrain performance if the number of objects in the prechange scene exceeds a capacity limit imposed by working memory (but see Rensink, 2000b), or if the build-up I thank George McConkie and Gary Wolverton for the stimulus presentation environment used in...

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Section: Implications Of Change Detection Constraint On Capacity Limimentioning

confidence: 99%

Detecting changes between real-world objects using spatiochromatic filters

Zelinsky

2003

Psychonomic Bulletin & Review

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“…Other global disruptions that have been used in investigations of change blindness are picture shifts (Blackmore, Brelstaff, Nelson, & Troscianko, 1995), film cuts in motion pictures and physical occluders in real world situations (Simons & Levin, 1998). Furthermore, change blindness can occur when the change takes place without an occluder, namely if a multitude of distracting local transients, known as ''mudsplashes'', are presented parallel to that produced by the change (O'Regan, Rensink, & Clark, 1999) or when gradual changes occur slow enough not to give rise to the activation of the visual transient channels (Saiki, in press;Simons, Franconeri, & Reimer, 2000).…”

Section: Change Blindnessmentioning

confidence: 99%

Change detection and occlusion modes in road-traffic scenarios

Velichkovsky

Dornhoefer

Kopf

et al. 2002

Transportation Research Part F: Traffic Psychology and Behaviou

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Change blindness phenomena are widely known in cognitive science, but their relation to driving is not quite clear. We report a study where subjects viewed colour video stills of natural traffic while eye movements were recorded. A change could occur randomly in three different occlusion modes-blinks, blanks and saccades-or during a fixation (as control condition). These changes could be either relevant or irrelevant with respect to the traffic safety. We used deletions as well as insertions of objects. All occlusion modes were equivalent concerning detection rate and reaction time, deviating from the control condition only. The detection of relevant changes was both more likely and faster than that of irrelevant ones, particularly for relevant insertions, which approached the base line level. Even in this case, it took about 180 ms longer to react to changes when they occurred during a saccade, blink or blank. In a second study, relevant insertions and the blank occlusion were used in a driving simulator environment. We found a surprising effect in the dynamic setting: an advantage in change detection rate and time with blanks compared to the control condition. Change detection was also good during blinks, but not in saccades. Possible explanation of these effects and their practical implications are discussed.

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“…If visual memory did have a high capacity, the radical changes in visual appearances which occur between saccades might overload the system (just as the memory capacity of present-day computers is taxed by highresolution, real-time, continuous image acquisition). Human observers are insensitive to large changes in a complex scene especially when these changes coincide with a shift in the image requiring a saccade, (Blackmore, Brelstaff, Nelson & Troscianko, 1995) or a blank interstimulus interval (Rensink, O'Regan & Clark, 1997).…”

Section: Introductionmentioning

confidence: 99%

Limitations for Change Detection in Multiple Gabor Targets

Wright¹,

Green²,

Baker³

2000

Visual Cognition

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We investigate the limitations on the ability to detect when a target has changed, using Gabor targets as simple quantifiable stimuli. Using a partial report technique to equalise response variables, we show that the log of the Weber fraction for detecting a spatial frequency change is proportional to the log of the number of targets, with a set-size effect that is greater than that reported for visual search. This is not a simple perceptual limitation, because pre-cueing a single target out of four restores performance to the level found when only one target is present. It is argued that the primary limitation on performance is the division of attention across multiple targets, rather than decay within visual memory. However in a simplified change detection experiment without cueing, where only one target of the set changed, not only was the set size effect still larger, but it was greater at 2000 msec ISI than at 250 msec ISI, indicating a possible memory component. The steepness of the set size effects obtained suggests that even moderate complexity of a stimulus in terms of number of component objects can overload attentional processes, suggesting a possible low-level mechanism for change blindness.

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Is the Richness of Our Visual World an Illusion? Transsaccadic Memory for Complex Scenes

Cited by 167 publications

References 17 publications

Detecting changes between real-world objects using spatiochromatic filters

Detecting changes between real-world objects using spatiochromatic filters

Change detection and occlusion modes in road-traffic scenarios

Limitations for Change Detection in Multiple Gabor Targets

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