We designed a laboratory study to investigate the influence of social interaction on category learning. The objective in the present study is to examine what kind of teaching behavior can improve an agent's learning of categories. In a computerbased study participants learned four categories for sixteen objects which appear on a computer screen. The objects' categories determine what kind of manipulation is to be done on the objects. Five tutors and twenty participants were recruited to participate. For the study the tutors were placed in front of a computer in one room whereas the learners were in another room. The learners' task was to manipulate the objects appropriately through the instructions they received from the tutor on their screens via six symbols. These six symbols were the only way for the tutor to communicate with the learner. We call this a bottom-up learning as the it relies entirely on the perception of the tutors' symbols without any prior knowledge of their meaning. The focus in the present study is not on the ability by the learner to acquire knowledge of the categories but on the types of instructions that the tutor gave during the trials and the effects of the feedback given to the learner. Therefore, the feedback given by the tutors via the symbols was classified and quantified.
Abstract. This study investigates the acquisition of integrated object manipulation and categorization abilities through a series of experiments in which human adults and artificial agents were asked to learn to manipulate two-dimensional objects that varied in shape, color, weight, and color intensity. The analysis of the obtained results and the comparison of the behavior displayed by human and artificial agents allowed us to identify the key role played by features affecting the agent/environment interaction, the relation between category and action development, and the role of cognitive biases originating from previous knowledge.2
Some areas of biological research use artificial means to explore the natural world. But how the natural and artificial are related across wideranging research areas is not always clear. Relations differ further for bioengineering fields. We propose a taxonomy which would serve to elucidate distinct relations; there are three ways in which the natural is linked to the artificial, corresponding with distinct methods of investigation: i) a comparative approach (natural vs artificial) in which artificial systems are treated in the same way as natural systems, ii) a modeling approach (natural via artificial) in which we use artificial systems to learn about features of natural ones, and iii) an engineering approach (natural pro artificial) in which natural systems are used to draw inspiration for artefacts. Ambiguities about and between these approaches limit the development of fields and impact negatively on interdisciplinary communication.
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