Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems

The present paper analyzes the self-generated explanations (from talk-aloud protocols) that “Good” and “Poor” students produce while studying worked-out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self-explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example-independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self-explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self-explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation-based learning to account for these psychological findings.     

The effects of massive repetition on speeded recognition of faces

Models of face processing suggest that recognizing a person should prime recognition of a consecutive, but different, image of the same person. This prediction is tested in four experiments using large blocks of different views of the same person. The experiments demonstrate that reaction times decreased according to a negative power function as the number of repetitions increased. After sufficient repetitions, however, the reaction times lengthened. The presentation of a different familiar person between blocks of repetitions caused the reaction time for the target to increase to a level equivalent to that with no repetitions. Experiments 2 and 3 investigated the effect of different intervening stimuli (unfamiliar faces and objects). Such stimuli reduced the effect of mass repetition-but the reduction using a familiar face was greater than that with either unfamiliar faces or objects. Experiment 4 confirmed that the effects of massive repetition occur for a face familiarity task as well as for face identification tasks. The results are discussed in termsof the predictions of Burton's (1994) IACL model.     

The Subject (of) Listening

Jean-Luc Nancy's phenomenology of listening (trans. 2007) makes a series of claims about the sonic/auditory nature of the subject. First among these is the claim that the subject is a subject to the extent that it is listening, that it is all ears. The subject emerges on the back of the resonance of timbre in the body and the body's becoming-rhythmic. These claims are phrased often in musical terms, or making use of terms and rhetoric from the domains of music theory and music psychology. This article explores the role of music in Nancy's phenomenology with a particular view to the nature of listening in the contemporary world: both how listening is figured as a part of what Nancy terms the inoperative community and how listening figures the cognitive and phenomenological constitution of the subject.