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Stimuli (panels A-D) and results (panel E) from Experiment 4. Panel A shows the dollhouse with all locations filled. Panel B shows the 12 regions. Panels C and D show an eight-region stimulus pair in which the changing regions were the inner-lower left-rear and the inner-lower right-front.
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Previous research measuring visual short-term memory (VSTM) suggests that the capacity for representing the layout of objects is fairly high. In four experiments, we further explored the capacity of VSTM for layout of objects, using the change detection method. In Experiment 1, participants retained most of the elements in displays of 4 to 8 elemen...
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... further examine the nature of the memory repre sentation, we restricted changes to occur within 1 of 3 superregions on each trial. As shown in Figure 4D, there was an outer superregion, in which changes altered the scene envelope. In addition, there were two superregions within the house: an innerfront and an innerback region. ...
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... stimuli were then created by cutting and pasting from a set of highly colorful digital pictures that together showed each state of each region. Filled regions were randomly chosen with the constraint that changes (the deleted and new region) always occurred within one of the three superregions, each for one third of the trials (see Figure 4D). Fifty As can be seen in Table 3, d ′ values ranged from 1.8 to 2.2 in the regular conditions and from 1.1 to 1.7 in the irregular conditions. ...
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... stimuli were generated from pictures of a highly colorful dollhouse with furniture and other scene appropriate objects (shown in Figures 4A-4D). All stim uli were generated from this one dollhouse scene. ...
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... location of changes varied among the three super regions: outer, innerfront, and innerback ( Figure 4D). Performance varied modestly across region type, being most accurate for changes in the outer regions (82.5%), followed by the innerfront regions (78.0%) and the inner back regions (73.4%) [for the main effect of su perregion, F(2,22) 5 6.23, p , .001, ...
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... decreased modestly with increasing dis play size, from 81.5% correct at four regions to 75.5% and 77.0% at six and eight regions, respectively ( Figure 4E). This was a total drop of 6% or 4.5% as the number of ob jects in the regions increased by an average of 8.4 objects. ...
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... were three reliable interactions in the analysis, involving display size, superregion, and response. The interaction of display size and response was modest in magnitude (see Figure 4E) [F(2,22) 5 6.68, p , .001], suggesting that there was some variation in decision bias, with same responses becoming more likely with larger displays. ...
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... were three reliable interactions in the analysis, involving display size, superregion, and response. The interaction of display size and response was modest in magnitude (see Figure 4E) [F(2,22) 5 6.68, p , .001], suggesting that there was some variation in decision bias, with same responses becoming more likely with larger displays. Percentages correct for all relevant conditions are shown in Table 5. A same bias was involved in the other two interactions as well: the superregion 3 display entities and their relations) within those regions. There would be nothing special about the outer regions beyond the ease of encoding their states and their interrelations. Salience should also be high for the innerfront regions, as opposed to that for the innerback regions, because of their increased visibility. This is reflected in the ordering of performance in Experiment 4. A weakness of the network model is that additional relations can be added to account for many results, mak ing the model difficult to falsify. In order to deal with the plethora of relations that may be encoded in a network, Sanocki (1999) proposed comparing conditions in which there should be more or fewer relations. This approach was used in Experiment 3, in which a standard matrix with horizontal and vertical structure was compared with an irregular matrix. The standard matrix should support the encoding of more relations between the elements than the irregular matrix, and the increased memory capacity in the standard condition is consistent with this ...
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... stimuli were generated from pictures of a highly colorful dollhouse with furniture and other scene appropriate objects (shown in Figures 4A-4D). All stim uli were generated from this one dollhouse scene. Twelve regions were defined in the scene, with each region con taining its own collection of identifiable, fixedlocation Pashler's k was 5.0 stimulus regions. The SE of the estimate was 0.28 regions. Nine of the 12 observers had capacities greater than four regions, and the difference be tween 4 and the observers' capacities was reliable [t(12) 5 3.68, p , .01]. Thus, most observers held more than four stimulus regions in memory. If the observers represented all of the objects in the regions (average of 2.17 per re gion), the total number of stimulus objects contained in memory would be ...
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... location of changes varied among the three super regions: outer, innerfront, and innerback ( Figure 4D). Performance varied modestly across region type, being most accurate for changes in the outer regions (82.5%), followed by the innerfront regions (78.0%) and the inner back regions (73.4%) [for the main effect of su perregion, F(2,22) 5 6.23, p , .001, h 2 p 5 .36]. How ever, performance within each region type was reliably greater than the 50% floor ( ps , .001). Thus, observers were maintaining information about layout in each of the four grayscale array pairs were created by converting the color pairs to grayscale in Adobe Photoshop. Each of the resulting 108 stimulus pairs was used twice, once for each ...
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... further examine the nature of the memory repre sentation, we restricted changes to occur within 1 of 3 superregions on each trial. As shown in Figure 4D, there was an outer superregion, in which changes altered the scene envelope. In addition, there were two superregions within the house: an innerfront and an innerback region. On 33% of the trials, for example, the change was the deletion of objects in one innerback region and the ap pearance of objects in another innerback region. If the memory representation encodes the entire configuration, changes should be detected within each superregion. If the memory representation loses discrete objects or re gions as capacity is reached, certain superregions (e.g., innerback regions) may suffer most. Alternatively, if the memory representation is defined mainly by its outer en velope, outer changes should be detected much better than are inner ...
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... decreased modestly with increasing dis play size, from 81.5% correct at four regions to 75.5% and 77.0% at six and eight regions, respectively ( Figure 4E). This was a total drop of 6% or 4.5% as the number of ob jects in the regions increased by an average of 8.4 objects. The main effect of display size was reliable [F(2,22) 5 6.53, p , .001, h 2 p 5 .37]. The d′ values were 2.14, 1.52, and 1.78 for each respective display size (Table 4). Again, these results are consistent with a gradual loss of fidelity with display size. size interaction [F(4,44) 5 7.49, p , .001] and the three way interaction of display size, response, and superregion [F(4,44) 5 7.07, p , .001]. As can be seen, same re sponses become more likely with larger displays and less salient critical regions (i.e., inner, but not outer, critical regions). These changes in bias are relatively small; they do not contravene the finding that change detection was fairly accurate across the four ...
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... the color condition, 54 pairs of stimulus layouts were deter mined by a random algorithm for choosing regions. The stimuli were then created by cutting and pasting from a set of highly colorful digital pictures that together showed each state of each region. Filled regions were randomly chosen with the constraint that changes (the deleted and new region) always occurred within one of the three superregions, each for one third of the trials (see Figure 4D). Fifty As can be seen in Table 3, d ′ values ranged from 1.8 to 2.2 in the regular conditions and from 1.1 to 1.7 in the irregular conditions. The difference in sensitivity between conditions was highly reliable [t(15) 5 4.95, p , .001]. Changes in bias in the main conditions were fairly small (Table 3). The sensitivity effects indicate that organization in regular horizontal and vertical matrices contributes to memory capacity. This result provides new evidence that organizational factors contribute to VSTM. We suggest that the greater symmetry and more regular relations of regular displays lead to more efficient memory coding be cause they support the creation of wellorganized groups. The VSTM encoding system may work at least as well with 2D relations as with apparent depth ...
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... However, it is commonly suggested that memory representations are the sustained maintenance of perceptual representations (Harrison & Tong, 2009;Serences et al., 2009;Sreenivasan et al., 2014). Furthermore, working memory for location information tends to have better performance than for other features (Rajsic & Wilson, 2014;Sanocki et al., 2010). Indeed, most tests of visual working memory rely on spatial cues to indicate the to-be-tested item (as opposed to feature cues) due to superior performance for spatial cues (e.g., Heuer et al., 2016;Li & Saiki, 2015). ...
... The results of Experiments 1A and 1C/1D, which had very distinct methods of cuing by location versus color, all point to the same finding-that accessing a memory representation through reference to its location is slower than through reference to its color. This is a surprising result not just because it differs from perception, as in Experiment 1B, but also because memory performance for spatial information is consistently found to be better than for other features such as color (e.g., Rajsic & Wilson, 2014;Sanocki et al., 2010). One would expect that the feature that is better remembered would allow more efficient access. ...
... For one, there is considerable neuroimaging evidence that information in early visual areas is maintained during working memory (Harrison & Tong, 2009;Serences et al., 2009;Sreenivasan et al., 2014). More importantly, though, this explanation predicts that spatial information should be relatively poor in memory, whereas evidence from Experiment 2, as well as other work (Rajsic & Wilson, 2014;Sanocki et al., 2010), demonstrates that spatial information is quite accurate in memory. In the same context in which we show slow access with location cues, we find more accurate detection and updating for location, relative to color. ...
Attentional mechanisms allow us to focus on objects that would help us achieve our goals while ignoring those that would distract us. Attention can also be focused internally toward specific items in memory. But does selection within memory work similarly to selection within perception? Perceptual attention is fast and effective at selecting regions of space. Across five experiments, we used a memory search task to investigate whether spatial selection is also efficient for selection in memory. Participants remembered four items on a grid before being asked to access their memory of one item and update one of its features. We found that it took longer to access an item when referenced by its spatial location than by its color, despite memory accuracy for location being superior. We conclude that there must be multiple, distinct memory representations in the brain and that selection in memory is different from perceptual selection. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
... A growing body of work points to an important role for knowledge of statistical regularities in VWM, and suggests that use of statistical regularities allows for more efficient memory (Bae, Olkkonen, Allred, & Flombaum, 2015;Brady, Konkle, & Alvarez, 2009;Brady & Tenenbaum, 2013;Corbett, 2016;Huttenlocher, Hedges, & Vevea, 2000;Orhan & Jacobs, 2013;Sanocki, Sellers, Mittelstadt, & Sulman, 2010;Sims et al., 2012;Swan, Collins, & Wyble, 2016;Victor & Conte, 2004). For example, and Corbett (2016) showed that subjects' memories for items in a display are biased toward items' summary statistics, meaning statistical regularities averaged over multiple 1 Here, 'bit allocation' specifically refers to changes in the pattern of memory errors resulting from adapting VWM to the current task. ...
... An emerging body of research on VWM has demonstrated that memory is sensitive to the statistics of visual information (Orbán et al., 2008;Orhan & Jacobs, 2013;Brady & Tenenbaum, 2013;Huttenlocher et al., 2000;Corbett, 2016;Victor & Conte, 2004;Sanocki et al., 2010), and some work has attempted to tie this phenomenon to fundamental principles of information theory (Brady et al., 2009;Sims et al., 2012;Victor & Conte, 2004). Other research has shown that the precision of VWM is task-specific (Fougnie et al., 2010;Sims, 2015;Swan et al., 2016), with greater memory precision for features that are task-relevant. ...
Human brains are finite, and thus have bounded capacity. An efficient strategy for a capacity-limited agent is to continuously adapt by dynamically reallocating capacity in a task-dependent manner. Here we study this strategy in the context of visual working memory (VWM). People use their VWM stores to remember visual information over seconds or minutes. However, their memory performances are often error-prone, presumably due to VWM capacity limits. We hypothesize that people attempt to be flexible and robust by strategically reallocating their limited VWM capacity based on two factors: (a) the statistical regularities (e.g., stimulus feature means and variances) of the to-be-remembered items, and (b) the requirements of the task that they are attempting to perform. The latter specifies, for example, which types of errors are costly versus irrelevant for task performance. These hypotheses are formalized within a normative computational modeling framework based on rate-distortion theory, an extension of conventional Bayesian approaches that uses information theory to study rate-limited (or capacity-limited) processes. Using images of plants that are naturalistic and precisely controlled, we carried out two sets of experiments. Experiment 1 found that when a stimulus dimension (the widths of plants' leaves) was assigned a distribution, subjects adapted their VWM performances based on this distribution. Experiment 2 found that when one stimulus dimension (e.g., leaf width) was relevant for distinguishing plant categories but another dimension (leaf angle) was irrelevant, subjects' responses in a memory task became relatively more sensitive to the relevant stimulus dimension. Together, these results illustrate the task-dependent robustness of VWM, thereby highlighting the dependence of memory on learning.
... The presumed sparseness clashes with average observers' ability to successfully recognize thousands of scene images (Standing, 1973;Konkle et al., 2010). Large amounts of information, moreover, can be retained about both a scene's spatial layout and the objects therein (Friedman, 1979;Sanocki et al., 2010). These findings suggest a special aptness of memory for scenes may exist. ...
... However, it likely preserves the heterogeneity of the visual field, which may be shaped by the typical eccentricity-dependent degradation of acuity and color sensitivity from fovea to periphery. Furthermore, memory capacity is much larger for scene layout information than for single objects in a scene (Sanocki et al., 2010). Therefore, latent representations may come associated with a substantial amount of scene layout information. ...
An unresolved problem in eye movement research is how a representation is constructed on-line from several consecutive fixations of a scene. Such a scene representation is generally understood to be sparse; yet, for meeting behavioral goals a certain level of detail is needed. We propose that this is achieved through the buildup of latent representations acquired at fixation. Latent representations are retained in an activity-silent manner, require minimal energy expenditure for their maintenance, and thus allow a larger storage capacity than traditional, activation based, visual working memory. The latent representations accumulate and interact in working memory to form to the scene representation. The result is rich in detail while sparse in the sense that it is restricted to the task-relevant aspects of the scene sampled through fixations. Relevant information can quickly and flexibly be retrieved by dynamical attentional prioritization. Latent representations are observable as transient functional connectivity patterns, which emerge due to short-term changes in synaptic weights. We discuss how observing latent representations could benefit from recent methodological developments in EEG-eye movement co-registration.
... Furthermore, memory capacity for individual visual features appears to mildly benefit when these features belong to the same perceptual object, as opposed to different objects (Delvenne & Bruyer, 2004;Luck & Vogel, 1997;Olson & Jiang, 2002;Xu, 2002aXu, , 2002b. Memory for features and locations also benefits from preservation of the overall spatial structure of the memorized features (Jiang, Chun, & Olson, 2004;Jiang, Olson, & Chun, 2000), as well as from grouping those features into coherent spatial layouts (Phillips, 1974;Sanocki, Sellers, Mittelstadt, & Sulman, 2010;Woodman, Vecera, & Luck, 2003;Xu & Chun, 2007). ...
Performance on visual short-term memory for features has been known to depend on stimulus complexity, spatial layout, and feature context. However, with few exceptions, memory capacity has been measured for abruptly appearing, single-instance displays. In everyday life, objects often have a spatiotemporal history as they or the observer move around. In three experiments, we investigated the effect of spatiotemporal history on explicit memory for color. Observers saw a memory display emerge from behind a wall, after which it disappeared again. The test display then emerged from either the same side as the memory display or the opposite side. In the first two experiments, memory improved for intermediate set sizes when the test display emerged in the same way as the memory display. A third experiment then showed that the benefit was tied to the original motion trajectory and not to the display object per se. The results indicate that memory for color is embedded in a richer episodic context that includes the spatiotemporal history of the display.
... Using a change detection paradigm, Sanocki et al. (2010) recently found that participants tended to be rather good at detecting changes to object layout within a display. The good performance observed on this task was suggested to be due to the efficiency with which display items can be grouped by location. ...
At any given moment, our awareness of what we 'see' before us seems to be rather limited. If, for instance, a display containing multiple objects is shown (red or green disks), when one object is suddenly covered at random, observers are often little better than chance in reporting about its colour (Wolfe, Reinecke, & Brawn, Visual Cognition, 14, 749-780, 2006). We tested whether, when object attributes (such as colour) are unknown, observers still retain any knowledge of the presence of that object at a display location. Experiments 1-3 involved a task requiring two-alternative (yes/no) responses about the presence or absence of a colour-defined object at a probed location. On this task, if participants knew about the presence of an object at a location, responses indicated that they also knew about its colour. A fourth experiment presented the same displays but required a three-alternative response. This task did result in a data pattern consistent with participants' knowing more about the locations of objects within a display than about their individual colours. However, this location advantage, while highly significant, was rather small in magnitude. Results are compared with those of Huang (Journal of Vision, 10(10, Art. 24), 1-17, 2010), who also reported an advantage for object locations, but under quite different task conditions.
... Similarly, several studies (Brady & Tenenbaum, 2010;Sanocki, Sellers, Mittelstadt, & Sulman, 2010;Victor & Conte, 2004) have shown that observers can take advantage of perceptual regularities in working memory displays to remember more individual items from those displays. Brady and Tenenbaum (2010) investigated checkerboard-like displays and conceptualized their findings in terms of hierarchical encoding, in which the gist of the display is encoded in addition to specific information about a small number of items that are least consistent with the gist. ...
Influential models of visual working memory treat each item to be stored as an independent unit and assume that there are no interactions between items. However, real-world displays have structure that provides higher-order constraints on the items to be remembered. Even in the case of a display of simple colored circles, observers can compute statistics, such as mean circle size, to obtain an overall summary of the display. We examined the influence of such an ensemble statistic on visual working memory. We report evidence that the remembered size of each individual item in a display is biased toward the mean size of the set of items in the same color and the mean size of all items in the display. This suggests that visual working memory is constructive, encoding displays at multiple levels of abstraction and integrating across these levels, rather than maintaining a veridical representation of each item independently.
... Similarly, several studies (Brady & Tenenbaum, 2010;Sanocki, Sellers, Mittelstadt, & Sulman, 2010;Victor & Conte, 2004) have shown that observers can take advantage of perceptual regularities in working memory displays to remember more individual items from those displays. Brady and Tenenbaum (2010) investigated checkerboard-like displays and conceptualized their findings in terms of hierarchical encoding, in which the gist of the display is encoded in addition to specific information about a small number of items that are least consistent with the gist. ...
When remembering a real-world scene, people encode both detailed information about specific objects and higher-order information like the overall gist of the scene. However, existing formal models of visual working memory capacity (e.g., Cowan's K) generally assume that people encode individual items but do not represent the higher-order structure of the display. We present a probabilistic model of VWM that generalizes Cowan's K to encode not only specific items from a display, but also higher-order information. While higher-order information can take many forms, we begin with a simple summary representation: how likely neighboring items are to be the same color. In Experiment 1, we test this model on displays of randomly chosen colored dots (Luck & Vogel, 1997). In Experiment 2, we generalize the model to displays where the dots are purposefully arranged in patterns. In both experiments, 75 observers detected changes in each individual display, which allowed us to calculate d' for a particular change in a particular display (range: d′=0.8-3.8). Results show that observers are highly consistent about which changes are easy or difficult to detect, even in standard colored dot displays (split-half correlations=0.60-0.76). Furthermore, the correlation between observers d′ and the model d′ is r=0.45 (p<0.01) in the randomly generated displays and r=0.72 (p<0.001) in the patterned displays, suggesting the model's simple summary representation captures which changes people are likely to detect. By contrast, the simpler model of change detection typically used in calculations of VWM capacity does not predict any reliable differences in difficulty between displays. We conclude that even in simple VWM displays items are not represented independently, and that models of VWM need to be expanded to take into account this non-independence between items before we can usefully make predictions about observers' memory capacity in real-world scenes.
In multimodal interaction, information is presented to users through multiple channels, e.g., sight, sound, touch, smell, and taste. Too much information delivered in a short time, however, may result in information overload that overflows people’s information processing capacity. We summarized the methods of quantifying the capacity by categorizing them into the span of storage or the speed of processing. The span of storage mainly includes short-term memory and working memory capacity and multiple object tracking capacity. Working memory is required in many intellectual functions, and its capacity could be tested with change detection tasks, self-ordered tasks, and complex span tasks. Whether different modalities have separate capacities, whether objects or features are stored, and whether the capacity works as discrete slots or a continuous resource pool were discussed. The speed of processing could be calculated as the information transfer rate with the stimuli and responses matrix; Entropy is used for more complex stimuli such as languages. The relative capacity of multitasking, which is often incorporated in multimodal interaction, could be calculated with the capacity coefficient. The application of these methods to the non-traditional modalities in human-computer interaction, e.g., touch, smell, and taste, was discussed.
