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06, p > 0.05. Study 2 replicated results of Study 1 in that sequential and random training yielded similar performance. Further, Gefitinib Study 2 found that motion did not facilitate unsupervised learning of novel 3-D objects because the training effect was similar when training with or without motion for both sequential and random sequences. Studies 3 and 4: Spatiotemporal information yields better generalization to new object views than temporal information The surprising finding in previous experiments is the similar improvement in object discrimination following spatiotemporal continuous and random training of object views. One interpretation is that temporal proximity is sufficient for linking among views shown during training, and that spatiotemporal continuity does not provide additional information. However, two additional factors may have contributed to the pattern of the results: (a) Training and testing were conducted on the same views; therefore, temporal proximity during training was sufficient to associate these particular object views, consequently obtaining a learning effect. (b) Training employed a large number of object views, which was perhaps more than sufficient for generating a robust representation of the object view space, irrespective of the training sequence. To examine whether sequential training produces more robust representations than random training when these factors are controlled, we conducted two additional experiments. As in the prior studies we measured performance before and after unsupervised sequential or random training, where the former contains spatiotemporal continuity and the latter Oxygenase only temporal proximity. However, here we measured performance on untrained views to assess the generalization of the learning effect. Study 3 used the same training with 24 views as Study 1, but tested on new views between the trained views. www.selleckchem.com/products/Dasatinib.html Study 4 trained with only seven views spaced 30�� apart, and tested on new views between the trained views. We reasoned that if sequential training produces a more robust representation than random training, generalization performance will be higher for the former than the latter, in particular when the number of training views is reduced. Results of Study 3 indicated that when training with 24 views as in the prior experiments, learning generalizes to untrained views. Postlearning, participants significantly improved their accuracy on untrained views. Performance increased from 52.19% �� 0.98% pretraining (d��: 0.17 �� 0.05) to 67.50% �� 2.60% posttraining (d��: 1.12 �� 0.19). Main effect of training is significant (Figure 4a; F(1, 15) = 32.16, p
