People often fail to detect large changes to scenes, provided that the changes occur during a visual disruption. This phenomenon, known as ''change blindness,'' occurs both in the laboratory and in real-world situations in which changes occur unexpectedly. The pervasiveness of the inability to detect changes is consistent with the theoretical notion that we internally represent relatively little information from our visual world from one glance at a scene to the next. However, evidence for change blindness does not necessarily imply (...) the absence of such a representation-people could also miss changes if they fail to compare an existing representation of the pre-change scene to the post-change scene. In three experiments, we show that people often do have a representation of some aspects of the pre-change scene even when they fail to report the change. And, in fact, they appear to ''discover'' this memory and can explicitly report details of a changed object in response to probing questions. The results of these real-world change detection studies are discussed in the context of broader claims about change blindness. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...) memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)