| Lizenz: Creative Commons Namensnennung 4.0 International PDF - Veröffentlichte Version (413kB) |
- URN zum Zitieren dieses Dokuments:
- urn:nbn:de:bvb:355-epub-461760
- DOI zum Zitieren dieses Dokuments:
- 10.5283/epub.46176
| Dokumentenart: | Artikel | ||||
|---|---|---|---|---|---|
| Open Access Art: | Gold (mit APC - bezahlt UR) | ||||
| Titel eines Journals oder einer Zeitschrift: | Frontiers in Psychology | ||||
| Verlag: | Frontiers | ||||
| Band: | 2021 | ||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 11 | ||||
| Seitenbereich: | S. 580667 | ||||
| Datum: | 7 Dezember 2020 | ||||
| Institutionen: | Biologie und Vorklinische Medizin > Institut für Physiologie | ||||
| Identifikationsnummer: |
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| Dewey-Dezimal-Klassifikation: | 100 Philosophie und Psychologie > 150 Psychologie | ||||
| Status: | Veröffentlicht | ||||
| Begutachtet: | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden: | Ja | ||||
| Dokumenten-ID: | 46176 |
Zusammenfassung
Recent research has shown that observers store a vast amount of viewed real-world objects in visual long-term learning with high precision (e.g., Standing, 1973; Vogt and Magnussen, 2007; Brady et al., 2008), even when objects have been processed without any attention and intention of learning (Kuhbandner et al., 2017). However, one open issue that has attracted considerable attention recently is ...
Zusammenfassung
Recent research has shown that observers store a vast amount of viewed real-world objects in visual long-term learning with high precision (e.g., Standing, 1973; Vogt and Magnussen, 2007; Brady et al., 2008), even when objects have been processed without any attention and intention of learning (Kuhbandner et al., 2017). However, one open issue that has attracted considerable attention recently is the nature of the stored visual long-term memory representations. Since objects are higher-level constructs that represent patterns of lower-level features, two contrasting views have been put forward: objects may be stored in in the form of sets of independent feature representations or in the form of unitary feature-bound object representations (e.g., Brady et al., 2013; van den Honert et al., 2017).
In two simultaneously published recent papers, contradictory conclusions are drawn. In a paper by Utochkin and Brady (2020), the authors conclude that objects are stored as sets of independent features, based on a series of experiments showing that object features are only weakly bound and can easily be unbound in long-term memory, a conclusion which is also supported by a previous study of Brady et al. suggesting that objects features are forgotten independently of each other (Brady et al., 2013). By contrast, in a paper by Balaban et al. (2019), the authors conclude that objects are stored as unitary feature-bound representations, based on a series of experiments following the protocol of the study by Brady et al. (2013; Experiment 2) but analyzing the data with an alternative analytical method, suggesting that object features are forgotten in a dependent manner.
At first glance, one could be tempted to conclude from such contradictory findings that further research is needed to clarify which view is actually correct. However, there is another possibility that is not considered in either of the two papers: it may be that visual information can be flexibly stored in visual long-term memory both feature-based and object-based, depending on the requirements of the current situation. If so, debates about whether visual objects are stored either feature-based or object-based may be misleading. Instead, the relevant question that should be explored in future research would be which factors determine whether objects are stored in visual long-term memory feature-based or object-based.
Such a theoretical assertion is based on two assumptions. First, it must be the case that real-world objects can be stored in visual long-term memory both feature-based and object-based. Second, it must be the case that the different storage formats have different functionalities and can be flexibly used. Regarding the first assumption, the contradictory findings reported in the papers by Brady et al. (2013), Balaban et al. (2019), and Utochkin and Brady (2020) can be taken as evidence that real-world objects can be stored both feature-based and object-based based. This is also supported by the fact that both in studies examining feature memory (e.g., Magnussen and Dyrnes, 1994; Magnussen et al., 2003) and in studies examining object memory (e.g., Ceraso et al., 1998; Walker and Cuthbert, 1998), the existence of long-lasting memory representations has been proven. The assumption that visual objects can be stored both feature-based and object-based is also found in prominent memory models such as the multiple-entry, modular memory framework (Johnson, 1983), postulating that there are feature-based and object-based memory subsystems. Furthermore, the existence of qualitatively different types of representational formats has also been recognized in theories about the hierarchical structure of visual memory (for a review, see, e.g., Brady et al., 2011). In fact, in research on visual working memory, it has been shown that both feature-based and object-based representations have to be assumed to fully explain the observed performance patterns (e.g., Fougnie et al., 2010, 2013).
To shed light on the different functionalities of feature-based and object-based memory representations, it is helpful to see how real-world objects are initially represented in the cognitive system during perception. Broadly speaking, two qualitatively different processing steps are involved (e.g., Tarr, 1995; Riesenhuber and Poggio, 1999; Serences and Yantis, 2006). First, visual features such as orientation, colors, and so forth, are extracted from the visual input, a process by which a representation of the visual input in terms of a collection of independent features is created. Second, informative features are recoded into bound object representations and uninformative features discounted, leading to the phenomenological experience of perceiving coherent objects. Importantly, object representations are not ad hoc formed independent of previous visual experiences. Rather, the recoding of features is informed by a stored inner model of the structure of the world that that reflects the current visual knowledge about objects derived from previous visual experiences.
As already postulated by Piaget (1970) and recently elaborated in theories about the so-called predictive brain (e.g., Clark, 2013), in order to allow adaptive learning, two opposing requirements have to be met by our visual system. On the one hand, to keep stability, incoming information has to be processed with respect to stored inner model of the world (assimilation). On the other hand, to allow adaptation, the current inner world model has to be continuously updated based on inconsistent incoming information (accommodation). The storing of feature-based vs. object-based memory representations may serve the fulfillment of these opposing requirements. As long as the current inner world model is appropriate, it can be imposed on incoming visual information so that visual experiences can be resource-efficiently stored as coherent objects based on current inner object models. Thus, object-based memory representations may serve the function of assimilation. In situations where the current inner world model does not sufficiently represent the incoming information, the inner model itself has to be updated based on the inconsistent information. In such a case, it would be functional to store visual experiences in the form of feature representations. Thus, feature-based memory representations may serve the function of accommodation (for an illustration, see Figure 1).
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