4.5 Article

Distance, shape and more: recognition of object features during active electrolocation in a weakly electric fish

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JOURNAL OF EXPERIMENTAL BIOLOGY
卷 210, 期 17, 页码 3082-3095

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COMPANY BIOLOGISTS LTD
DOI: 10.1242/jeb.005694

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environmental imaging; object recognition; shape perception; contour; volume; feature extraction.

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In the absence of light, the weakly electric fish Gnathonemus petersii detects and distinguishes objects in the environment through active electrolocation. In order to test which features of an object the fish use under these conditions to discriminate between differently shaped objects, we trained eight individuals in a food-rewarded, two-alternative, forced-choice procedure. All fish learned to discriminate between two objects of different shapes and volumes. When new object combinations were offered in non-rewarded test trials, fish preferred those objects that resembled the one they had been trained to ( S+) and avoided objects resembling the one that had not been rewarded ( S -). For a decision, fish paid attention to the relative differences between the two objects they had to discriminate. For discrimination, fish used several object features, the most important ones being volume, material and shape. The importance of shape was demonstrated by reducing the objects to their 3-dimensional contours, which sufficed for the fish to distinguish differently shaped objects. Our results also showed that fish attended strongly to the feature 'volume', because all individuals tended to avoid the larger one of two objects. When confronted with metal versus plastic objects, all fish avoided metal and preferred plastic objects, irrespective of training. In addition to volume, material and shape, fish attended to additional parameters, such as corners or rounded edges. When confronted with two unknown objects, fish weighed up the positive and negative properties of these novel objects and based their decision on the outcome of this comparison. Our results suggest that fish are able to link and assemble local features of an electrolocation pattern to construct a representation of an object, suggesting that some form of a feature extraction mechanism enables them to solve a complex object recognition task.

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