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The Development of Change Detection

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Title: The Development of Change Detection
Language: English
Authors: Shore, David I., Burack, Jacob A., Miller, Danny
Source: Developmental Science. Sep 2006 9(5):490-497.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
Peer Reviewed: Y
Physical Description: PDF
Page Count: 8
Publication Date: 2006
Document Type: Journal Articles
Reports - Research
Descriptors: Infants, Young Adults, Memory, Computer Software, Perceptual Development, Attention, Identification, Visual Stimuli, Evaluation Methods, Color, Visual Perception
DOI: 10.1111/j.1467-7687.2006.00516.x
ISSN: 1363-755X
Abstract: Changes to a scene often go unnoticed if the objects of the change are unattended, making change detection an index of where attention is focused during scene perception. We measured change detection in school-age children and young adults by repeatedly alternating two versions of an image. To provide an age-fair assessment we used a bimanual choice rather than open-ended verbal responses. The difference in detection speed and accuracy between 50-ms versus 250-ms blank screens between views indexed change detection in short-term visual memory independent of sensory and response processes. Younger children were significantly less efficient than older participants, especially when an object changed color or had a part deleted. Changes in object orientation were detected more readily. These results point to important differences in the perceptual reality of younger and older children.
Abstractor: As Provided
Number of References: 35
Entry Date: 2009
Accession Number: EJ850130
Database: ERIC
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  Value: <anid>AN0021785321;5g501sep.06;2019May28.13:10;v2.2.500</anid> <title id="AN0021785321-1">The development of change detection. </title> <p>Changes to a scene often go unnoticed if the objects of the change are unattended, making change detection an index of where attention is focused during scene perception. We measured change detection in school‐age children and young adults by repeatedly alternating two versions of an image. To provide an age‐fair assessment we used a bimanual choice rather than open‐ended verbal responses. The difference in detection speed and accuracy between 50‐ms versus 250‐ms blank screens between views indexed change detection in short‐term visual memory independent of sensory and response processes. Younger children were significantly less efficient than older participants, especially when an object changed color or had a part deleted. Changes in object orientation were detected more readily. These results point to important differences in the perceptual reality of younger and older children.</p> <p>The development of attention during the first dozen years of life is thought to influence the way that objects and events are perceived ([<reflink idref="bib3" id="ref1">3</reflink>]; [<reflink idref="bib4" id="ref2">4</reflink>]; [<reflink idref="bib19" id="ref3">19</reflink>]). Young children most probably experience the world in a different way from adults, although it has historically been difficult to document the perceptual reality or experience of any observer ([<reflink idref="bib2" id="ref4">2</reflink>]; [<reflink idref="bib12" id="ref5">12</reflink>]; [<reflink idref="bib16" id="ref6">16</reflink>]; [<reflink idref="bib34" id="ref7">34</reflink>]).</p> <p>In a recent review, [<reflink idref="bib9" id="ref8">9</reflink>]) proposed a two‐dimensional framework for studying individual differences in visual attention, especially those involving age‐related changes. By combining the dimension of processing <emph>control</emph> (often referred to as 'automatic' or unconscious versus 'controlled' or conscious processing; [<reflink idref="bib27" id="ref9">27</reflink>]) with the dimension of processing <emph>origin</emph> (innate processes versus those acquired through experience; [<reflink idref="bib20" id="ref10">20</reflink>]), they identified four basic modes of visual selection. These included reflexive (automatic, innate), habitual (automatic, learned), explorative (controlled, innate), and deliberative modes of selectivity (controlled, learned). Enns and Trick noted a conspicuous lack of research on the way that the explorative mode of attention changed during childhood, despite the considerable developmental research on three of the four modes of attention (reflexes, habits, and deliberation). This is somewhat surprising because a basic tenet of developmental psychology is that humans, as 'infovores', constantly explore new environments and incorporate the new knowledge they gain from them. Yet, lab research is typically focused only on more highly circumscribed, deliberative tasks, in the study of the conscious or controlled aspects of attention.</p> <p>The <emph>change detection task</emph> is a recently developed tool to better understand explorative attention (for reviews see [<reflink idref="bib21" id="ref11">21</reflink>]; [<reflink idref="bib33" id="ref12">33</reflink>]). In this task, two versions of the same scene are presented in rapid succession and the participant's task is to identify any differences between the two scenes. When no blank interval is presented between scenes, or when the interval is shorter than approximately 80 ms, detection of differences in a scene is effortless and automatic because the change creates a spatially local flicker or motion signal that is registered by the sensory system as a salient luminance transient ([<reflink idref="bib18" id="ref13">18</reflink>]; [<reflink idref="bib21" id="ref14">21</reflink>]). However, when the blank interval between scenes exceeds the temporal limits of visible persistence (also called iconic imagery) then the change is no longer available to the sensory system. Instead, the presentation of a new scene after 80 ms or more produces luminance transients everywhere in the display and change can, therefore, only be detected if the current object or feature at a given location is noticeably different from the representation of the scene already in short‐term visual memory ([<reflink idref="bib13" id="ref15">13</reflink>]; [<reflink idref="bib23" id="ref16">23</reflink>]; [<reflink idref="bib29" id="ref17">29</reflink>]). In this way, the change detection task is an index of the short‐term memory of scenes, provided that the duration of the interval exceeds the limits of visible persistence or iconic imagery.</p> <p>Attention contributes to successful change detection in several critical ways. One, accuracy in change detection is more likely for objects of central than of marginal interest in the scene ([<reflink idref="bib23" id="ref18">23</reflink>]; [<reflink idref="bib29" id="ref19">29</reflink>]). Two, when changes in objects and features are expected ([<reflink idref="bib1" id="ref20">1</reflink>]), or when attention is drawn reflexively to the location of change ([<reflink idref="bib26" id="ref21">26</reflink>]), then change detection accuracy is substantially improved over changes that occur to unexpected objects or locations away from the focus of attention. Three, change detection accuracy decreases as the number of items in the display is increased, pointing to the limited capacity of short‐term memory for scenes ([<reflink idref="bib21" id="ref22">21</reflink>]; [<reflink idref="bib28" id="ref23">28</reflink>]; [<reflink idref="bib25" id="ref24">25</reflink>]). Thus, change detection is a reliable index of where and to which objects and features attention is directed in the short‐term memory representations of a visual scene.</p> <p>Flickering two versions of a scene interleaved with a blank interval is not the only way to study change detection in visual short‐term memory. Rather, the recent surge in research on this topic began with reports that observers did not notice changes made during an eye movement while inspecting a photograph. For example, two gentlemen wearing hats in one scene might have their hats switched in the altered version. Although a change such as this was readily detected when it occurred during a fixation, it was missed on a majority of trials when it occurred during an eye movement from one region of the scene to another ([<reflink idref="bib10" id="ref25">10</reflink>]; [<reflink idref="bib14" id="ref26">14</reflink>]). Other reports followed, indicating that similar results could be obtained if the changes occurred during a 'cut' in a videotaped sequence of real‐world actions ([<reflink idref="bib13" id="ref27">13</reflink>]; [<reflink idref="bib30" id="ref28">30</reflink>]), during the unexpected presentation of small 'mudsplashes' added to the picture while it was being viewed ([<reflink idref="bib17" id="ref29">17</reflink>]), and even if the changes occur during a real‐world conversation between an unwitting participant and an actor. In the latter case, the actor exchanged places with another actor when a door that was being carried by two actors came between the participant and the initial actor ([<reflink idref="bib32" id="ref30">32</reflink>]). Finally, even large changes to a scene can go unnoticed without any accompanying scene interruptions ([<reflink idref="bib31" id="ref31">31</reflink>]). Again, the critical ingredient is that observers must not be attending to the objects or regions of the scene that are undergoing gradual change while the scene is viewed.</p> <p>In the present study, we used a modified form of the flicker method to examine age‐related differences in explorative attention among children and adults ([<reflink idref="bib21" id="ref32">21</reflink>]; [<reflink idref="bib32" id="ref33">32</reflink>]). First, as shown in Figure 1, a changing and an unchanging set of alternating images were presented side by side on the same screen so that the participants merely had to indicate the side of the screen in which a change occurred. The use of a forced‐choice procedure rather than the typical open‐ended verbal response minimized the potential for the results to be influenced by developmental differences in linguistic ability or in the response criteria used to indicate the detection of a change.</p> <p>Graph: 1 The sequence of events on a single trial. Two images were shown, one on either side of fixation, for 250 ms, followed by a blank interval for either 50 ms or 250 ms. This was followed by an unchanged image on one side and a changed image on the other side. Changes could occur through a color change, part deletion, or change in object orientation. Participants responded by indicating the side containing the change.</p> <p>The second innovation was a direct comparison of performance between two conditions that differed only in the blank screen durations between views of the two scenes. A 50‐ms blank condition was intended to permit sensory cues such as local motion and local flicker to signal the change. Thus, an accurate detection of change in this condition required that (<reflink idref="bib1" id="ref34">1</reflink>) a spatially local transient be detected by the sensory system, followed by (<reflink idref="bib2" id="ref35">2</reflink>) correct response selection (localizing the transient to the left or right side of the display) and then (<reflink idref="bib3" id="ref36">3</reflink>) correct response execution (pressing the spatially corresponding response key). In contrast, accurate change detection in a 250‐ms blank condition could only be accomplished if the participants compared one scene with the other in short‐term memory since 250 ms is longer than the duration of visible persistence (iconic imagery). When such a comparison was successful, the detected change could be indicated by selecting the correct response and executing it (processes 2 and 3 above).</p> <p>Performance differences between the 50‐ms and the 250‐ms conditions could, therefore, be linked uniquely to the ability to detect change between a visual short‐term memory of a scene and a scene presently on view. This interpretation is based on the assumptions that the task demands of response selection and response execution (processes 2 and 3) do not differ between these two conditions and that the detection of a spatially localized motion or flicker transient is not itself an error‐prone process in school‐age children. The latter assumption is consistent with considerable evidence that spatial attention is oriented reflexively and reliably to the location of unique luminance transients in observers of all ages (for a review, see [<reflink idref="bib19" id="ref37">19</reflink>]). However, even if some of these assumptions are incorrect, and younger children are less able than older children to detect and respond to a local luminance transient in the 50‐ms condition, then our proposed subtractive comparison will be conservative, as it will overestimate the 'true' ability of young observers to detect scene changes using visual short‐term memory. This will work <emph>against</emph> our effort to document that younger children have a reduced ability in this regard.</p> <p>Two different types of images were used in order to allow for the assessment of the generality of the findings. Half of the images were photographs of common objects and toys (see Figure 2); and the other half were colored line drawings of concrete objects (e.g. a truck, a baseball, a bat, a bicycle, etc.). Pilot testing with adult participants was used to help select images and object changes that were generally matched for task difficulty across these two types of images.</p> <p>Graph: 2 Examples of display images, showing the original (changed) and two of the altered versions (color change, part deletion). The rotation condition is not shown because it is simply a mirror reversal of the original. The labels of Easy and Hard are given only for illustration purposes. Easy images shown resulted in the smallest mean correct RT for each media type (drawing, photograph); hard images shown resulted in the largest mean correct RT. Images scanned from Photo Language, manufactured by Nathan.</p> <p>Another factor relevant to the presentation of these images was whether the change involved a switch in color, the disappearance and reappearance of an object part, or a change in orientation of one of the objects in the scene. The comparisons across these types of changes must be interpreted with caution, both in this and in previous studies (e.g. [<reflink idref="bib23" id="ref38">23</reflink>]), because baseline salience to observers has not been equated. Nonetheless, any differences that are identified may inform future research about the links and differences in the nature of scene memory between children and adults. For example, children and adults may be similar in their representation at the object level, but children may retain less detail at the level of specific features (e.g. colors) and parts.</p> <p>The change detection task was administered to three groups of children with average ages of 6, 8, 10 years, and one group of young adults. The children's ages were selected both to study performance in an age range in which deliberative attention changes are noted and to ensure that the task was understood by all participants. The dependent measures were correct response time (RT) and percentage errors.</p> <hd id="AN0021785321-2">Method</hd> <p></p> <hd id="AN0021785321-3">Participants</hd> <p>Eighty‐five participants were recruited from a private elementary school in the Montreal area. Six of these participants were removed from the analyses because the average error rate for each was greater than 10%. This left 79 participants in four age groups, including 16 (six males) 5–7‐year‐olds (<emph>M</emph> = 6.68, SD = 0.93), 21 (10 males) 7–9‐year‐olds (<emph>M</emph> = 8.55, SD = 0.70), 22 (12 males) 9–12‐year‐olds (<emph>M</emph> = 10.82, SD = 0.93), and 20 adult (10 males) undergraduate and graduate students between 18 and 30 years of age (<emph>M</emph> = 26.90, SD = 2.34).</p> <hd id="AN0021785321-4">Stimuli</hd> <p>Each image measured 11.3° by 8.0° of visual angle. Displays were presented with VScope 1.2.7 software ([<reflink idref="bib22" id="ref39">22</reflink>]) on a 333 MHz Macintosh PowerBook G3 with a 14.1″ active LCD screen (approximately 30° of visual angle horizontally and 23.7° of visual angle vertically) at an approximate viewing distance of 50 cm. The '?' key and the 'Z' key were used for participant responses and were covered with colored stickers to facilitate learning. The images were adapted from a set of educational cards that were designed for individuals with language disorders (Photo Language, manufactured by Nathan). Each card contained a color image of an inanimate object. The images were scanned with an HP DeskJet scanner and were then modified with Adobe PhotoShop software. The main difference between the drawings and photos was the level of realistic detail and method of construction. Drawings had spot colors of constant gradient for each feature whereas the photos had realistic color gradients and naturalistic hues. The photos also appeared to have greater depth and realism than the drawings that were more akin to cartoons or picture book images.</p> <hd id="AN0021785321-5">Procedure</hd> <p>A total of 24 images of inanimate objects were selected (12 photographs and 12 drawings) and subjected to three different types of change: color, part deletion, and object orientation, resulting in a total of 96 different images. Images were displayed for 250 ms and separated by blank intervals of either 50 ms or 250 ms. The factors of image type (photo, drawing) and blank interval (50 ms, 250 ms) were varied across testing blocks whereas the type of change (color, part deletion, object orientation) was varied within a block of trials. Each participant was therefore tested in four blocks of 36 trials for a total of 144 unique trials. The testing session took approximately 25 minutes.</p> <p>Each trial consisted of the repetition of four display screens, including the presentation of the side‐by‐side images for 250 ms, a blank interval of either 50 ms or 250 ms, the presentation of the images again (with an alteration randomly on the left or the right) for 250 ms, and then another blank interval of either 50 ms or 250 ms. This sequence was repeated until the participant responded or until 4 seconds elapsed. Responses were scored as errors if the unaltered side of the screen was selected or if 4 seconds elapsed without a response. Prior to testing, six practice trials were administered to the participants to assure comprehension of the task. If more than two errors were made, the practice trials were repeated.</p> <hd id="AN0021785321-6">Measurement issues</hd> <p>One complication in using RT to measure change detection is that a larger number of display alternations are presented within a fixed period of viewing time in the 50‐ms condition than in the 250‐ms condition. Since this makes more image comparisons possible in the 50‐ms condition, at least in principle, some researchers have proposed using as the dependent variable the mean number of alternations required for successful change detection ([<reflink idref="bib24" id="ref40">24</reflink>]). However, this measure introduces other potential confounds in a developmental study. For example, younger children invariably take longer to respond because of differences in sensory and response factors, which would artificially inflate the number of alternations counted for them, even if their perceptual processes were at adult levels. Thus, their change detection ability would be overestimated.</p> <p>We approach this problem as follows. One, we do not make any claims about the absolute time required to detect change, as all comparisons between groups and stimuli are based on relative differences. Thus, if the RT measure is biased in favor of younger children in the 50‐ms condition (because their generally slower RT gives them more 'looks'), the actual difference between the 50‐ms and 250‐ms conditions will be underestimated for them. In this way, our comparisons between age groups are <emph>conservative</emph>, since any reported differences would be even larger if we used the number of display alternations required for successful change detection. Two, each of our analyses of correct RT was mirrored by the same pattern in the analyses of percentage errors.</p> <hd id="AN0021785321-7">Results</hd> <p>The analyses of percentage errors (Figure 3) and correct RT (Figure 4) revealed a consistent and graded improvement in change detection with age over the four groups of participants. This is seen most clearly in the composite change detection index based on the differences between the 50‐ms and 250‐ms blank interval conditions (Figure 5).</p> <p>Graph: 3 Mean percentage errors for all ages (<reflink idref="bib6" id="ref41">6</reflink>, 8, 10, and 26 years), materials (photographs and drawings), and change types (color, missing, rotate) used in the experiment. Error bars represent the between‐participants standard error of the mean.</p> <p>Graph: 4 Mean correct response time (RT) for all ages (<reflink idref="bib6" id="ref42">6</reflink>, 8, 10, and 26 years), materials (photographs and drawings), and change types (color, missing, rotate) used in the experiment. Error bars represent the between‐participants standard error of the mean.</p> <p>Graph: 5 Age‐related improvements in change detection as measured in both RT and errors. Scores are the differences between the 50‐ms and the 250‐ms interval conditions, in order to index change detection independently from age changes in sensory, decision, and motor response processes. Error bars represent the between‐participants standard error of the mean.</p> <p>Correct RT was submitted to a non‐recursive outlier rejection procedure that based the long and short RT cut‐off on the number of observations in the cell ([<reflink idref="bib35" id="ref43">35</reflink>]). This resulted in the removal of 307 observations (2.5% of all correct responses). The remaining RT and percentage error data were submitted separately to a four‐factor mixed‐design ANOVA with the between‐participants factor of age (<reflink idref="bib6" id="ref44">6</reflink>, 8, 10, 26 years) and the repeated measures factors of time (50 ms, 250 ms), image (photographs, drawings) and change (color, part deletion, object orientation). All <emph>p</emph> values were less than.001 unless otherwise noted.</p> <p>The participants were generally faster and more accurate in responding in the 50‐ms blank interval condition than in the 250‐ms condition [<emph>F</emph>(<reflink idref="bib1" id="ref45">1</reflink>, 81) = 111.74 for errors; <emph>F</emph>(<reflink idref="bib1" id="ref46">1</reflink>, 81) = 250.32 for RT], replicating previous findings indicating that change detection based on sensory signals (motion and flicker) is more efficient than change detection based on short‐term memory ([<reflink idref="bib21" id="ref47">21</reflink>]). The size of this difference also decreased with increasing age of the participants [<emph>F</emph>(<reflink idref="bib3" id="ref48">3</reflink>, 81) = 2.78 for errors; <emph>F</emph>(<reflink idref="bib3" id="ref49">3</reflink>, 81) = 3.09 for RT; <emph>p</emph> < .05 for both], such that change detection was more efficient for older participants.</p> <p>Detecting change was generally more difficult for all ages viewing drawings rather than photos [<emph>F</emph>(<reflink idref="bib1" id="ref50">1</reflink>, 81) = 19.00 for errors; <emph>F</emph>(<reflink idref="bib1" id="ref51">1</reflink>, 81) = 143.52 for RT]. In addition, the difference between the two blank interval conditions was larger for drawings than for photos [<emph>F</emph>(<reflink idref="bib1" id="ref52">1</reflink>, 81) = 11.94 for errors; <emph>F</emph>(<reflink idref="bib1" id="ref53">1</reflink>, 81) = 20.67 for RT], consistent with generally less efficient change detection for the more difficult pictorial discriminations. The interaction of Image Type × Interval × Age group was not significant [<emph>F</emph>(<reflink idref="bib3" id="ref54">3</reflink>, 81) < 1.0 for both errors and RT].</p> <p>Change detection was most difficult for deleted parts, followed by changes in color and then changes in object orientation [<emph>F</emph>(<reflink idref="bib1" id="ref55">1</reflink>, 81) = 54.92 for errors; <emph>F</emph>(<reflink idref="bib1" id="ref56">1</reflink>, 81) = 195.65 for RT] and these differences interacted with interval [<emph>F</emph>(<reflink idref="bib2" id="ref57">2</reflink>, 162) = 33.98 for errors; <emph>F</emph>(<reflink idref="bib2" id="ref58">2</reflink>, 162) = 51.76 for RT]. Specifically, the longer blank duration exaggerated the differences in detecting changes of different kinds. This two‐way interaction was further modulated by the factor of age, but only for RT [<emph>F</emph>(<reflink idref="bib6" id="ref59">6</reflink>, 162) = 2.34, <emph>p</emph> = .034; <emph>F</emph>(<reflink idref="bib6" id="ref60">6</reflink>, 162) = 1.07, <emph>ns</emph> for errors]. Based on a simple effects analysis, this three‐way interaction resulted from an age‐related effect for part deletion [<emph>F</emph>(<reflink idref="bib3" id="ref61">3</reflink>, 81) = 3.12; <emph>p</emph> = .028] and color changes [<emph>F</emph>(<reflink idref="bib3" id="ref62">3</reflink>, 81) = 2.84; <emph>p</emph> = .043], but only a marginal effect of age for object orientation [<emph>F</emph>(<reflink idref="bib3" id="ref63">3</reflink>, 81) = 2.46; <emph>p</emph> = .069].</p> <p>The pattern of change detection for the three change types also interacted with image type [<emph>F</emph>(<reflink idref="bib2" id="ref64">2</reflink>, 162) = 8.48 for errors; <emph>F</emph>(<reflink idref="bib2" id="ref65">2</reflink>, 162) = 64.28 for RT]. Once again, this interaction reflected the synergistic effects of task difficulty, such that change detection was generally least efficient for drawings with deleted features and easiest for photos containing rotated objects. This two‐way interaction was further modulated by the interval condition in the same predictable way: the long interval exaggerated the differences in change detection already reported due to change type and image type [<emph>F</emph>(<reflink idref="bib2" id="ref66">2</reflink>, 162) = 3.20, <emph>p</emph> = .043 for errors; <emph>F</emph>(<reflink idref="bib2" id="ref67">2</reflink>, 162) = 11.13 for RT]. None of these higher‐order interactions were involved in significant interactions with age.</p> <hd id="AN0021785321-8">Discussion</hd> <p>Change detection gradually improved across four groups of participants from 6 years of age to young adulthood. This finding was evident in both the analyses of correct RT and in errors made in change detection. The robustness of this finding is highlighted by the use of an analysis in which the comparison of performance in a sensory condition (50‐ms interval) with a short‐term memory condition (250‐ms interval) led to a conservative estimate of change detection. Younger children were less able to detect changes using their short‐term memory representations of a scene and a scene that is currently on view over and above any developmental differences in sensory change detection or in response selection and execution. This conclusion is consistent with findings from tasks of the detection of impossible figures ([<reflink idref="bib8" id="ref68">8</reflink>]) and of the role of symmetry in pattern perception ([<reflink idref="bib6" id="ref69">6</reflink>]). However, it also extends these earlier findings that may have limited generality simply because young children are less able to stay on a deliberative task relative to older children ([<reflink idref="bib9" id="ref70">9</reflink>]), whereas the participants in this study did not need to maintain an experimenter‐defined specific target image in short‐term memory. In the present study, younger children were less able to detect changes to a scene even when they freely explored the scene for changes that could occur over a wide range of features, parts and whole objects.</p> <p>A secondary finding was that change detection was more difficult for drawings than for pictures and for part deletions and insertions as compared to changes in color or object orientation. Large age‐related differences were observed in the detection of deleted parts and in the detection of color changes, but less so for changes in object orientation. This is consistent with a reduced sensitivity in younger participants to the details of objects (i.e. specific colors and parts), but not a similar reduction in sensitivity to the orientation of whole objects.</p> <p>The testing of change detection in school‐age children was intended to bolster the understanding of the development of the exploratory mode of visual selectivity, as change detection represents a unique combination of controlled (conscious) processing in combination with the absence of a well‐specified goal or task to accomplish. Humans often simply need to learn more about their environment, especially when it is new, before they are able to form more specific goals. Some aspects of a scene are processed preferentially, even when a person explores an unfamiliar environment, with no other goal than to gain new information (see review by [<reflink idref="bib5" id="ref71">5</reflink>]). The change detection task, in which change can occur in any of a potentially large number of ways, is one way to begin to tap into this unique mode of selectivity. The evidence from the present study seems to reveal the relatively greater sensitivity of younger observers to overall meaning of an object with respect to themselves (object orientation) than to the particular details of an object (color, missing parts).</p> <p>This particular pattern of sensitivity is consistent with the reverse‐hierarchy theory of visual experience ([<reflink idref="bib11" id="ref72">11</reflink>]), in which the ordering of our conscious experience is inverted with respect to the ordering of the lower‐level visual operations that give rise to these conscious experiences. For example, in order to register an entire object, the visual system must first process the image through a number of stages that include spatially localized and highly specialized parallel operations that analyze the edges and colors of the object. In contrast, conscious experience begins with whole objects and their meanings, and the specific details of these objects are only attended to much later ([<reflink idref="bib15" id="ref73">15</reflink>]). The present findings that the largest age‐related differences in sensitivity occurred for the most detailed changes are consistent with this theoretical position, and suggest that the visual experience of children and adults is most similar at the whole object level. This hypothesis clearly warrants further research.</p> <p>Of course, the particular version of the change detection task used in this study has its limitations. One, the images we tested in no way mimicked the rich visual environments that humans tend to explore on a daily basis. Two, the possible types of changes that could occur and that were repeated often in different pictures were highly restricted. Yet, the finding of age differences, despite these severe limitations in generality to everyday environments and in the restricted nature of the task participants performed, bodes well for further research in this area. These limitations suggest that testing in more realistic environments and over a larger range of possible change types will reveal even more striking age‐related differences in visual selectivity. Moreover, these findings suggest that this method is appropriate for addressing such questions as the 'entry level' of detail with which a scene is viewed, and which objects are of central as opposed to peripheral interest to a viewer, without asking them any direct questions that might alter their spontaneous approach to a scene.</p> <p>A final limitation to note concerns our interpretation of the differential rate of development in sensitivity to details versus whole objects. Caution is advised here because the factors that were associated with the slowest developmental rate were also the same factors that adult participants found most difficult. This indicates that our developmental interpretation cannot be fully distinguished in these data from the possibility that children simply show the slowest rates of development for those tasks that were most difficult. Although this distinction was previously sorted out in other attentional tasks (e.g. [<reflink idref="bib4" id="ref74">4</reflink>]; [<reflink idref="bib7" id="ref75">7</reflink>]), so that task difficulty does not account for the relative rate of development in all cases, it still needs to be considered in the study of age differences in change detection.</p> <hd id="AN0021785321-9">Acknowledgements</hd> <p>The data presented in this paper were derived from the Master's thesis by DM in the Department of Educational Psychology at McGill University that was supervised by DIS and JAB. DIS was supported by post‐doctoral fellowships from the Killam Trust Funds (Dalhousie University) and the Rotman Research Institute (Tanenbaum Fellowship), and a discovery grant from the Natural Science and Engineering Research Council of Canada. JAB, DM and SJ were supported by a research grant from the Social Sciences and Humanities Research Council of Canada to JAB. JTE was supported by a research grant from the Natural Science and Engineering Research Council of Canada.</p> <ref id="AN0021785321-10"> <title> References </title> <blist> <bibl id="bib1" idref="ref20" type="bt">1</bibl> <bibtext> Austen, E., & Enns, J.T. (2003). Change detection in an attended face depends on the expectation of the observer. Journal of Vision, 3, 64 – 74.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref4" type="bt">2</bibl> <bibtext> Broadbent, D.E. (1958). Perception and communication. London: Oxford University Press.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref1" type="bt">3</bibl> <bibtext> Brodeur, D.A., Trick, L.M., & Enns, J.T. (1997). Selective attention over the lifespan. In Jacob A. Burack & James T. Enns (Eds.), Attention, development, and psychopathology (pp. 74 – 94). New York: Guilford Press.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref2" type="bt">4</bibl> <bibtext> Burack, J.A., Enns, J.T., Iarocci, G., & Randolph, B. (2000). Age differences in visual search for compound patterns: long versus short range grouping. Developmental Psychology, 36, 731 – 740.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref71" type="bt">5</bibl> <bibtext> Egeth, H.E., & Yantis, S. (1997). Visual attention: control, representation, and time course. Annual Review of Psychology, 48, 269 – 297.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref41" type="bt">6</bibl> <bibtext> Enns, J.T. (1987). A developmental look at pattern symmetry in perception and memory. Developmental Psychology, 23, 839 – 850.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref75" type="bt">7</bibl> <bibtext> Enns, J.T. (1993). What can be learned about attention from studying its development? Canadian Psychology, 34, 271 – 281.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref68" type="bt">8</bibl> <bibtext> Enns, J.T., & Girgus, J.S. (1986). A developmental study of visual integration over space and time. Developmental Psychology, 22, 491 – 499.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref8" type="bt">9</bibl> <bibtext> Enns, J.T., & Trick, L. (2006). Four modes of selection. In E. Bialystok & G. Craik (Eds.), Lifespan cognition: Mechanisms of change (pp. 43 – 56). New York: Oxford University Press.</bibtext> </blist> <blist> <bibtext> Grimes, J. (1996). On the failure to detect changes in scenes across saccades. In K. Akins (Ed.), Perception (pp. 89 – 110). New York: Oxford University Press.</bibtext> </blist> <blist> <bibtext> Hochstein, S., & Ahissar M. (2002). View from the top: hierarchies and reverse hierarchies in the visual system. Neuron, 36, 791 – 804.</bibtext> </blist> <blist> <bibtext> James, W. (1890). The principles of psychology. Oxford: Holt.</bibtext> </blist> <blist> <bibtext> Levin, D.T., & Simons, D.J. (1997). Failure to detect changes to attended objects in motion pictures. Psychonomic Bulletin and Review, 4, 501 – 506.</bibtext> </blist> <blist> <bibtext> McConkie, G.W., & Currie, C.B. (1996). Visual stability across saccades while viewing complex pictures. Journal of Experimental Psychology: Human Perception and Performance, 22, 563 – 581.</bibtext> </blist> <blist> <bibtext> Navon, D. (1977). Forest before trees: the precedence of global features in visual perception. Cognitive Psychology, 9, 353 – 383.</bibtext> </blist> <blist> <bibtext> O'Regan, J.K. (1992). Solving the 'real' mysteries of visual perception: the world as an outside memory. Canadian Journal of Psychology, 46, 461 – 488.</bibtext> </blist> <blist> <bibtext> O'Regan, J.K., Rensink, R.A., & Clark, J.J. (1999). Change‐blindness as a result of 'mudsplashes'. Nature, 398, 34.</bibtext> </blist> <blist> <bibtext> Phillips, W.A. (1974). On the distinction between sensory storage and short‐term visual memory. Perception and Psychophysics, 16, 283 – 290.</bibtext> </blist> <blist> <bibtext> Plude, D.J., Enns, J.T., & Brodeur, D. (1994). The development of selective attention: a life‐span overview. Acta Psychologica, 86, 227 – 272.</bibtext> </blist> <blist> <bibtext> Posner, M.I. (1980). Orienting of attention. The VIIth Sir Frederic Bartlett Lecture. Quarterly Journal of Experimental Psychology, 32 (1), 3 – 25.</bibtext> </blist> <blist> <bibtext> Rensink R.A. (2002). Change detection. Annual Review of Psychology, 53, 245 – 277.</bibtext> </blist> <blist> <bibtext> Rensink, R.A., & Enns, J.T. (1992). Reference manual for Vscope and Emaker. Microsoft Software.</bibtext> </blist> <blist> <bibtext> Rensink, R.A., O'Regan, J.K., & Clark, J.J. (1997). To see or not to see: the need for attention to perceive changes in scenes. Psychological Science, 8, 368 – 373.</bibtext> </blist> <blist> <bibtext> Rensink, R.A., O'Regan, J.K., & Clark, J.J. (2000). On the failure to detect changes in scenes across brief interruptions. Visual Cognition, 7, 127 – 145.</bibtext> </blist> <blist> <bibtext> Richards, E., Jolicoeur, P., & Stolz, J. (submitted). The shift from feature‐based to object‐based representations in search for a change: The role of short‐term consolidation.</bibtext> </blist> <blist> <bibtext> Scholl, B.J. (2000). Attenuated change blindness for exogenously attended items in a flicker paradigm. Visual Cognition, 7, 377 – 396.</bibtext> </blist> <blist> <bibtext> Shiffrin, R.M., & Schneider, W. (1977). Controlled and automatic human information processing: II Perceptual learning, automatic attending and a general theory. Psychological Review, 84 (2), 127 – 190.</bibtext> </blist> <blist> <bibtext> Smilek, D., Eastwood, J.D., & Merikle, P.M. (2000). Does unattended information facilitate change detection? Journal of Experimental Psychology: Human Perception and Performance, 26, 480 – 487.</bibtext> </blist> <blist> <bibtext> Shore, D.I., & Klein, R. (2000). The effect of scene inversion on change‐blindness. Journal of General Psychology, 127, 27 – 44.</bibtext> </blist> <blist> <bibtext> Simons, D.J. (1996). In sight, out of mind: when object representations fail. Psychological Science, 8, 301 – 305.</bibtext> </blist> <blist> <bibtext> Simons, D.J., Franconeri, S.L., & Reimer, R.L. (2000). Change blindness in the absence of a visual disruption. Perception, 29, 1143 – 1154.</bibtext> </blist> <blist> <bibtext> Simons, D.J., & Levin, D.T. (1997). Change blindness. Trends in Cognitive Sciences, 1, 261 – 267.</bibtext> </blist> <blist> <bibtext> Simons, D.J., & Rensink, R.A. (2005). Change blindness: past, present, and future. Trends in Cognitive Sciences, 9 (1), 16 – 20.</bibtext> </blist> <blist> <bibtext> Stroud, J.M. (1967). The fine structure of psychological time. Annals of the New York Academy of Sciences, 138, 623 – 631.</bibtext> </blist> <blist> <bibtext> Van Selst, M., & Jolicoeur, P. (1994). A solution to the effect of sample size on outlier elimination. The Quarterly Journal of Experimental Psychology, 47, 631 – 650.</bibtext> </blist> </ref> <aug> <p>By David I. Shore; Jacob A. Burack; Danny Miller; Shari Joseph and James T. Enns</p> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib19" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib12" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib16" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib34" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib27" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib21" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib33" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib18" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib13" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib29" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib26" firstref="ref21"></nolink> <nolink nlid="nl14" bibid="bib28" firstref="ref23"></nolink> <nolink nlid="nl15" bibid="bib25" firstref="ref24"></nolink> <nolink nlid="nl16" bibid="bib10" firstref="ref25"></nolink> <nolink nlid="nl17" bibid="bib14" firstref="ref26"></nolink> <nolink nlid="nl18" bibid="bib30" firstref="ref28"></nolink> <nolink nlid="nl19" bibid="bib17" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib32" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib31" firstref="ref31"></nolink> <nolink nlid="nl22" bibid="bib22" firstref="ref39"></nolink> <nolink nlid="nl23" bibid="bib24" firstref="ref40"></nolink> <nolink nlid="nl24" bibid="bib35" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib11" firstref="ref72"></nolink> <nolink nlid="nl26" bibid="bib15" firstref="ref73"></nolink>
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  Data: The Development of Change Detection
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
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  Data: 10.1111/j.1467-7687.2006.00516.x
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  Data: Changes to a scene often go unnoticed if the objects of the change are unattended, making change detection an index of where attention is focused during scene perception. We measured change detection in school-age children and young adults by repeatedly alternating two versions of an image. To provide an age-fair assessment we used a bimanual choice rather than open-ended verbal responses. The difference in detection speed and accuracy between 50-ms versus 250-ms blank screens between views indexed change detection in short-term visual memory independent of sensory and response processes. Younger children were significantly less efficient than older participants, especially when an object changed color or had a part deleted. Changes in object orientation were detected more readily. These results point to important differences in the perceptual reality of younger and older children.
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