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Humans have a massive capacity to store detailed information in visual long-term memory. The present studies explored the fidelity of these visual long-term memory representations and examined how conceptual and perceptual features of object categories support this capacity. Observers viewed 2,800 object images with a different number of exemplars presented from each category. At test, observers indicated which of 2 exemplars they had previously studied. Memory performance was high and remained quite high (82 % accuracy) with 16 exemplars from a category in memory, demonstrating a large memory capacity for object exemplars. However, memory performance decreased as more exemplars were held in memory, implying systematic categorical interference. Object categories with conceptually distinctive exemplars showed less interference in memory as the number of exemplars increased. Interference in memory was not predicted by the perceptual distinctiveness of exemplars from an object category, though these perceptual measures predicted visual search rates for an object target among exemplars. These data provide evidence that observers ’ capacity to remember visual information in long-term memory depends more on conceptual structure than perceptual distinctiveness.

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

Using the violation-of-expectancy method, we investigated 10-month-old infants’ ability to rely on dynamic features in object individuation processes. Infants were first familiarized to events in which two different objects repeatedly appeared and disappeared, one at a time from behind a screen; at test, the screen was removed, revealing either one or two objects. In Experiment 1, one self-moving non-rigid agent and one inert object were involved in each trial, while in Experiment 2 two different agents were presented. Infants preferred to look at one-object outcomes in Experiment 1, but they did not show any preference for one- or two-object outcomes in Experiment 2. The results suggest that infants can use dynamic information to detect agents in complex individuation tasks before they can rely on shape or surface features. We propose that the sortals AGENT and INERT OBJECT appear in development before 12 months without a substantial contribution of linguistic experience. These findings may motivate a revision of current theories on the development of kind-based individuation and object files.

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