Different features of real-world objects are represented in a dependent manner in long-term memory
| Autor: | Dana Assaf, Halely Balaban, Moran Arad Meir, Roy Luria |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Structure (mathematical logic) Memory Long-Term Dependency (UML) Long-term memory Cognitive neuroscience of visual object recognition Experimental and Cognitive Psychology Object (philosophy) Feature (linguistics) Developmental Neuroscience Encoding (memory) Mental Recall Visual Perception Humans Female Psychology Set (psychology) General Psychology Cognitive psychology |
| Zdroj: | Journal of Experimental Psychology: General. 149:1275-1293 |
| ISSN: | 1939-2222 0096-3445 |
| Popis: | In the present study, we examined how real-world objects are represented in long-term memory. Two contrasting views exist with regard to this question: one argues that real-world objects are represented as a set of independent features, and the other argues that they form bound integrate representations. In 5 experiments, we tested the different predictions of each view, namely whether the different features of real-world items are remembered and forgotten independently from each other, in a feature-based manner, or conversely are stored and lost in an object-based manner, with all features depending upon each other. Across various stimuli, learning tasks (incidental or explicit), experimental setups (within- or between-subjects design), feature-dimensions, and encoding times, we consistently found that information is forgotten in an object-based manner. When an object ceases to be fully remembered, all of its features are lost, instead of only some of the object's features being lost whereas other features are still remembered. Furthermore, we found support for a strong form of dependency among the different features, namely a hierarchical structure. We conclude that visual long-term memory is object-based, challenging previous findings. (PsycInfo Database Record (c) 2020 APA, all rights reserved). |
| Databáze: | OpenAIRE |
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