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Resources for the maintenance of associated features

Visuo-spatial resources

2.3. Resources for the maintenance of associated features

2.3. Resources for the maintenance of associated features

As the resources involved in the maintenance of single features are not yet entirely agreed upon, it is complicated to tackle the more complex question what resources the maintenance of associated features relies on. However, this obstacle has systematically been avoided by focusing only on the question whether the maintenance of associated features requires

additional domain-general attentional resources. To anticipate the conclusion, the opinions are mixed. We have already detailed in chapter one the studies executed in the framework of the multi-component model on this topic. We will only briefly recapitulate the results of these

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studies and add the results from studies carried out independently from the context of the multi-component network.

Most research has focused on the maintenance of associated features belonging to the visuo-spatial domain, for example the maintenance of colored shapes. These will be discussed in the forthcoming section on within-domain associations. The following section will then discuss the few studies that have investigated the resources involved in the maintenance of cross-domain associations.

Within-domain associations

From the beginning of memory research on associated features, an intriguing question has been whether this binding process and its subsequent maintenance required attentional resources. We have referred in chapter one to two opposing views on this subject which have been expressed initially by Luck and Vogel (1997) and Wheeler and Treisman (2002). Luck and Vogel (1997) had observed that an integrated maintenance of several features leads to better memory performance than maintenance of each feature separately. Participants could maintain only about four features from a same domain when presented in isolation (four colors or four orientations). They could however maintain about eight features (four colors and four orientations) when these were shown in an integrated format, i.e. as four colored oriented bars. Luck and Vogel even showed that up to sixteen features could be maintained, as long as these features were presented integrated in no more than four objects. This led them to conclude that working memory capacity is defined in terms of objects, rather than in terms of features, with a capacity limit of four. Wheeler and Treisman (2002) questioned Luck and Vogel’s reasoning by suggesting that features from different domains might by maintained in domain-specific buffers. In the case of the results of Luck and Vogel, this would mean that the domain-specific buffer for colors could maintain four features and the domain-specific buffer for orientations could maintain four orientations. There would thus be no reason to suppose a priori object-based maintenance. Wheeler and Treisman suggested subsequently that the associations between features could be maintained, but this binding depends on focused attention. When attention is drawn away from these bindings, they fall apart in their constituent parts. This suggestion could not be verified for the Luck and Vogel study, as there was no explicit test of the association between features. Performance was only tested at the feature level. Luck and Vogel had however discarded the hypothesis that features are

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maintained within capacity-limited domain-specific buffers by showing that eight colors distributed over four objects could be maintained as well as four colors distributed over four objects. All of these eight colors would hence be maintained in the same domain-specific buffer dedicated to color maintenance. Nevertheless, attempts to replicate this finding have failed (e.g., Olson & Jiang, 2002; Wheeler & Treisman, 2002). The suggestion by Wheeler and Treisman that features are maintained within domain-specific buffers and held together by attention seemed thus a hypothesis that had to be explored further.

Subsequently, several studies have investigated the particular role of attention within the creation and maintenance of associations between features. The most common paradigm to investigate the additional involvement of attentional resources was the change detection paradigm. Two possible versions (differing concerning the test display) of this task are displayed in panel a of Figure 2.3. Making use of this paradigm, the encoding and binding of feature associations is compared to the encoding and maintenance of single features, in the presence or absence of a concurrent attention-demanding task. If encoding and/or maintaining the associations depend on attentional resources, then the addition of a concurrent attention-demanding task should have a more devastating effect on the encoding and/or maintenance of features associations than on the encoding and/or maintenance of single features (see Figure 2.3, panel b). The majority of studies did not find any differential effect of the attention-demanding task on the maintenance of feature associations compared with the maintenance of single features, denying thus the particular role attention might play in encoding or

maintaining the binding between visual features. This was the case for the studies described in chapter one, executed within the framework of the multicomponent model (Allen et al., 2006;

Allen et al., 2009; Karlsen et al., 2010) as well as for a number of other studies (e.g., Johnson, Hollingworth, & Luck, 2008; Morey & Bieler, 2013; Vergauwe, Langerock, & Barrouillet, 2014).

Figure 2.3: a) Two variants of the change detection task, using either a single probe test or a whole display test b) hypothetical results associated to the possible hypothesis concerning the additional involvement of attentional resources within the creation and maintenance of feature associations.

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This conclusion was confirmed by studies using other paradigms. Delvenne, Cleeremans and Laloyaux (2010) for example, manipulated attention during the retention interval making use of the retro-cue paradigm (see Figure 2.4). Within this paradigm, a spatial cue is shown after the disappearance of the study array to mark the item on which the change may occur. It has been shown that in comparison to no-cue conditions, the presence of a retro-cue increases change detection performance (e.g., Griffin & Nobre, 2003; Landman,

Spekreijse, & Lamme, 2004). This increase in performance is explained by the fact that the retro-cue focusses attention on the cued items, solidifying memory for those items and eliminating interference from the remaining items. The study by Delvenne et al. (2010) marked two feature associations by the retro-cue (see Figure 2.4), instead of one as is usually done. If a participant has to maintain only one feature association, then maintaining both features without their association would be sufficient at test to decide whether a change had occurred or not. If the retro-cue points to two feature associations, then participants are obliged to maintain the bindings as well, as at test a recombination of these features could be presented.

Figure 2.4: Example of the retro-cue paradigm as used by Delvenne et al. (2010).

Instead of decreasing attention as was done in the previous studies using the change detection paradigm, this study increased attention on the two cued items by means of

focusing. The results showed that the maintenance of feature associations benefited to a same extent of the retro-cue as the maintenance of single features. Delvenne et al. reasoned that if associated features were held together by attention, then they should have less benefitted from the retro-cue as they were already within the center of attention. This was however not the case. This study seemed thus to confirm previous conclusions that attention is not particularly necessary for the encoding and maintenance of feature associations.

Gajewski and Brockmole (2006) made use of the retro-cue paradigm as well. They compared memory performance for single features with memory for feature associations, making use of a recall paradigm. At test, participants had to recall the item at the location marked by a square. In valid cue trials, the item to recall corresponded to the item that had

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been cued before. In invalid trials, the item to recall concerned an item that had not been cued before. It had been shown previously that even explicit invalid cues lead nonetheless to an increase of attentional resources to the cued locations and a withdrawal of attention from the non-cued locations (Schmidt, Vogel, Woodman, & Luck, 2002). Despite a ratio of 18 valid against 90 invalid cues in the study of Gajewski and Brockmole, leaving hence no doubt to the participants about the overall non-validity of the cues, an analysis of the recall

performance for single features confirmed that performance was better when the item asked to recall corresponded to the cued item. Participants focused thus indeed their attention on the cued items. Gajewski and Brockmole reasoned that if the maintenance of feature associations depends on attention, the non-cued associations should fall apart as attention is withdrawn from these items. This should then result in a frequent recall of only one of the features in the invalid cue trials. In contrast, if the maintenance of feature associations does not particularly depend on attention, recall in the invalid cue trials should result in either recall of the entire feature association or a complete loss of the association. Based on the recall rate of single features in a condition in which they were presented in isolation, expected percentages of recall were calculated under both hypotheses. The obtained results favored once more the hypothesis assuming no particular need for attentional resources to maintain feature associations.

Cowan, Blume and Saults (2013) added further evidence to this conclusion. They used the change detection paradigm, but in a different way than the studies mentioned above.

Those studies all compared the maintenance of single features, with the maintenance of the link between features. In the single feature conditions, participants were tested with a feature that was either an old or a new item. In the feature association condition, participants were tested with a feature association that could be either a correct association or a recombination of features presented at study. In the feature association conditions, new features were never introduced. Cowan and colleagues approached the question about the attentional resources for the maintenance of the association from a different point of view. According to Cowan, the maintenance of features depends on attention. If the maintenance of the link between features also depends on attention, then there should be less attention available for the maintenance of the features. Cowan et al. compared feature recognition in three different conditions. At study, feature associations were shown. In a first condition, participants were asked to attend only to color, or only to shape. They were afterwards tested only on the attended feature. In the

“attend both” condition, participants had to remember the colors and the shapes, but not the

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link. They could be tested on the color or on the shape, but never on the associations. In the

“binding condition”, participants had to maintain the features as well as their associations.

They could be tested on feature memory for color or shape, but they could be tested as well on the correct association of the features. The results showed that the maintenance of the association in the “binding” condition did not result in lower feature memory than in the

“attend both” condition (see Figure 2.5) and Cowan et al. could hence conclude that the maintenance of the association did not require attentional resources.

Figure 2.5: Results of the study by Cowan et al. (2013) in which participants had to attend only to color or only to shape, to both color and shape without the link, or to color and shape and the link between these features. © 2013 American Psychological Association

Figure 2.5 also shows that feature memory is however lower when attending to two features than when attending to only one feature. This is in contrast with the study by Luck and Vogel (1997), who showed similar performance in both cases. We will come back on this difference in chapter three on the capacity limits.

We have stated in chapter one that research within the verbal domain had also favored the hypothesis that attention is not particularly necessary for the creation and maintenance of feature associations. The study by Baddeley et al. (2009) compared memory for associated words (as sentences or text) with memory for single words, in the presence or absence of a concurrent attention-demanding task. Here also, it was observed that the negative effect of the concurrent task was as pronounced for the maintenance of single words as for the

maintenance of associated words. In the same way as within the visuo-spatial domain, it was shown that attention did not seem to be the glue that keeps verbal items together.

The vast majority of studies added thus evidence to the assumption that the encoding and maintenance of feature associations does not depend on attentional resources. However,

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not all studies agreed on this conclusion. First of all, there is the study by Wheeler and Treisman (2002) that claims that attention is necessary for the maintenance of feature associations. Wheeler and Treisman used a change detection paradigm to compare feature maintenance (shape, color or location) with the maintenance of feature associations (colored shapes or colored locations). They observed that the maintenance of feature associations as compared to the maintenance of the worst of its single features was particularly lower when these were tested making use of a whole display (see Figure 2.3, panel a, p. 60). When the test concerned a single probe, this difference between feature associations and the worst

remembered feature was however not observed. Wheeler and Treisman explained this difference by the difference in attention required by the test display. The use of a whole display results in a higher attentional load than the use of a single test probe. As the

maintenance of feature associations is particularly hindered by a whole display test, but not by single probe test, this indeed suggests these feature associations to be particularly dependent on attention. However, from what has been documented on the suffix paradigm in chapter one, we are not so sure the locus of the effect relies effectively on an attentional mechanism.

Ueno, Allen, et al. (2011) and Ueno, Mate, et al. (2011) have described a filtering system for incoming stimuli. This filter is attentional in nature. Its filtering capacities fail however often when incoming stimuli resemble the stimuli to be maintained or are drawn from a same pool.

When the filter fails, information to be maintained is automatically overwritten at the object level. As feature information is represented both at the feature and the object level, its redundancy advantages recovery of this feature information. Yet, information about the associations is represented only at the object level, and in case of a failure of the filter, this information is overwritten and lost (see Figure 2.6, panel c and d). Although the filter is attentional in nature, it does not require more attentional resources to filter out feature associations than single features. The filtering mechanism was suggested to function on a feature basis and not an object basis. When at least one feature of a feature association might be confounded with a study item, the feature association as a whole passes through the filter.

The subsequent overwriting process does not seem to be particularly dependent on attention and takes place automatically. The whole display condition in the Wheeler and Treisman study (2002) would be a typical example of information making it through the filter and resulting henceforth in overwriting. From this point of view, the poorer memory performance for feature associations observed in the study of Wheeler and Treisman would thus not present convincing evidence in favor of the specific reliance on attentional resources for the maintenance of feature associations.

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Figure 2.6: The role of attention in the maintenance of feature associations. The upper panels represent the predominant role attention plays according to Wheeler and Treisman (2002). Panel a) represents the case of a single test probe and panel b) the case of a whole display probe. The lower panels c) and d) represent the same respective situations but from the point of view of Ueno, Mate, et al. (2011) that does not prioritize the role of attention in the maintenance of feature associations.

However, a number of other studies that experimentally manipulated the attentional availability did find evidence for attention to play an important role in the maintenance of feature associations. For example, Fougnie and Marois (2009) showed that the negative effect of a continuous tracking task was more pronounced on the maintenance of feature associations (i.e., colored shapes), then on the maintenance of single features (i.e., colors or shapes). In the same way, Brown and Brockmole (2010) also observed that the maintenance of color-shape associations was more negatively affected by an attention-demanding task (backward counting) than was the maintenance of single features. This was the case both in a population of younger and older adults. These results clearly run counter the above-cited studies that did not find any differential role of attention.

How to reconcile these results? Several studies suggested that the binding process between shape and color is too basic to require additional attentional resources (e.g., Allen et al., 2006; Baddeley et al., 2010). Ekcer, Maybery and Zimmer (2013) showed indeed

evidence that the automaticity of binding might depend on the type of binding. They found for example the binding of intrinsic feature associations (i.e., all features belong to a same object, e.g., color and shape) to be automatic, while this was not the case for extrinsic feature

associations (i.e., association of a feature to contextual factors, e.g., color and location).

However, attempts to render the binding process more attention-demanding by presenting the

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features in different modalities (Allen et al., 2009) or separated in space or time (Karlsen et al., 2010) did not succeed. In addition, this would still not explain why among the studies exploring intrinsic color-shape bindings some did show evidence for a need of additional attentional resources while others did not.

A comparative study by Allen, Hitch, Mate and Baddeley (2012) suggested that the measures used might have something to do with the discrepant results. They repeated the study by Brown and Brockmole (2010) in which the maintenance of feature associations was found to recruit additional attentional resources. They used however different recognition measures for the change detection paradigm used. While Brown and Brockmole reported only A’, a non-parametric measure to estimate a persons’ sensitivity to detect a change, Allen et al.

compared the results obtained by making use of A’, d’(i.e., a parametric sensitivity measure) and corrected recognition scores (i.e., hits minus false alarms; see chapter three for a more detailed description of these measures). In general the results of Allen et al. converged with the hypothesis that the maintenance of feature associations does not require additional

attentional resources in comparison to the maintenance of single features. Only one indication towards a higher need for attention for the maintenance of associations was found, and it was exactly when using the A’ measure as Brown and Brockmole had done. Thus, Allen et al.

partially neutralized the results by Brown and Brockmole and strongly recommend researchers to explore several measures to come up with a coherent overview.

We so far have given a general overview of experimental evidence in favor or against the hypotheses that the maintenance of feature associations depends particularly on attentional resources. Now let us consider how the three working memory models presented in chapter one have interpreted these results.

The multi-component model

The multi-component model has been at the basis of several studies mentioned above

The multi-component model has been at the basis of several studies mentioned above