Memory for semantically inconsistent objects in scenes is greater than that for semantically consistent objects - a phenomenon known as the inconsistent object advantage (Hollingworth & Henderson, Visual Cognition, 7(1-3), 213-235, 2000). Semantically inconsistent objects are also fixated longer and more often than consistent objects (Henderson et al., Journal of Experimental Psychology: Human Perception and Performance, 25(1), 210-228, 1999), potentially leaving less time for encoding the rest of the scene in which the objects occur. To determine whether semantically inconsistent objects are stored in memory with fewer of their scene's visual details, participants studied scenes that contained either semantically consistent or inconsistent target objects. After study, target objects were presented at test either in their original scene from the study phase or in a different scene of the same category. Recognition of semantically consistent objects, but not inconsistent objects, was more difficult when placed in a different scene. A disruption in object-scene semantics in the inconsistent condition may: (1) reduce memory for the visual features of the scene, (2) result in looser object-to-scene binding in memory, or both. This disruption may be due to the attentional and cognitive demands of processing the inconsistent object, leading to fewer visual details of the scene being encoded, but leaving unaffected the memory representation of the inconsistent object. This observation provides a new perspective on the inconsistent object advantage and poses interesting questions for future research, such as the impact of attentional deployment on encoding of scenes of inconsistent objects and the specific levels of scene information affected.
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http://dx.doi.org/10.3758/s13414-025-03037-2 | DOI Listing |
Despite the great success achieved, deep learning technologies usually suffer from data scarcity issues in real-world applications, where existing methods mainly explore sample relationships in a vanilla way from the perspectives of either the input or the loss function. In this paper, we propose a batch transformer module, BatchFormerV1, to equip deep neural networks themselves with the abilities to explore sample relationships in a learnable way. Basically, the proposed method enables data collaboration, e.
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Department of Psychology, California State University San Marcos, 333 S Twin Oaks Valley Rd, San Marcos, CA, 92096, USA.
Memory for semantically inconsistent objects in scenes is greater than that for semantically consistent objects - a phenomenon known as the inconsistent object advantage (Hollingworth & Henderson, Visual Cognition, 7(1-3), 213-235, 2000). Semantically inconsistent objects are also fixated longer and more often than consistent objects (Henderson et al., Journal of Experimental Psychology: Human Perception and Performance, 25(1), 210-228, 1999), potentially leaving less time for encoding the rest of the scene in which the objects occur.
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