Model of Unsupervised Learning

To guide our research, we have developed a tentative model describing how people might learn categories in unsupervised environments and use them to guide the encoding of specific instances. The learners in this model are assumed to be engaged in unguided exploration of a given domain of objects, i.e., learning is unsupervised and learners are simply attending to the features of individual objects without explicitly searching for categories among them. Importantly, human learners have a limited...

S

Salience-in-comparison, category knowledge and, 95 Scalar expectancy theory (SET), timing and, 304, 306-308 Scalar property, temporal learning and, 280, 293 temporal learning and, 284-286, 291 Schedule-induced activities, timing and, 297 Schedules, cyclic, temporal learning and, 275, 277,289-290, 292 Schedules of reinforcement temporal learning and, 265-272, 291-293 timing and, 309,312,317 Secondary categorization, achievement of goals and, 46, 48, 50, 52,57 Secondary reinforcement, timing and,...

Temporal Learning

Higa I. Introduction II. Experimental Background Performance on Patterned Interval Schedules of Reinforcement III. A Markovian Dynamic Hypothesis IV. A Diffusion-Generalization Model V. Conclusion References I. Introduction II. Contemporaneous Effects III. Retrospective Timing IV. Prospective Timing V. Time Horizons VI. Generalizations VII. Conclusions References Index Contents of Recent Volumes

The Misinformation Effect

The recollections of people who have initially seen an important event such as an accident or crime can be altered by the introduction of new information that occurs after the important event. When the new information is misleading it can produce errors in what a person reports. A large degree of distorted reporting has been found in scores of studies involving a wide variety of materials. People have recalled nonexistent broken glass and tape recorders, a clean-shaven man as having a mustache,...