Abstract
Flexibility and adaptability are desirable features of cognitive technical systems. However, in comparison to humans, the development of these features for technical systems is still at the beginning. One approach to improve their realization is to study human cognitive processes and to develop appropriate algorithms, which can be transferred and implemented into technical systems. One example for a typical task common to humans and robots is to find a specific task-relevant object (or target) among other similar but task-irrelevant objects (or distractors). Although this task is quite demanding, humans are doing well in finding task-relevant objects even in unknown environments by applying specific search strategies. For example, when an object is located in a familiar rather than in a new context, humans use the context information to localize the object without recognizing the context as familiar. This phenomenon is known as contextual cueing: it is supposed that implicitly learned context information of the environment, i.e. the spatial layout of objects and their relations, guides visual-spatial attention to the target location and thus helps to localize the task-relevant object.
However, in most of the previous psychological studies, artificial objects were used to investigate this effect and thus the ecologic validity is at least dubious. Therefore, the study reported here uses natural objects (LEGO® bricks). In contrast to artificial objects, natural objects are not only different in their visual features but also in their action relevance. Visual search is found to be faster and more accurate when the target is presented within a familiar context and when the knowledge about the context is implicit. This result is encouraging for further adaptation of stimulus material as well as for transferring psychological knowledge to technical applications.
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Schankin, A., Stursberg, O., Schubö, A. (2008). The Role of Implicit Context Information in Guiding Visual-Spatial Attention. In: Caputo, B., Vincze, M. (eds) Cognitive Vision. ICVW 2008. Lecture Notes in Computer Science, vol 5329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92781-5_8
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DOI: https://doi.org/10.1007/978-3-540-92781-5_8
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