David N. Perkins

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

Abstract Recent findings of transfer and nontransfer in such areas as planning and problem management skills, computer programming instruction, and literacy-related cognitive skills reveal paradoxes that invite explanation. In this article, we separate the "how" of transfer—the mechanisms that lead to it—from the "what" of transfer—the kind of knowledge and skill that might get transferred. We argue that transfer occurs in two ways. Low-road transfer depends on extensive, varied practice and occurs by the automatic triggering of well-learned behavior in a new context. High-road transfer occurs by intentional mindful abstraction of something from one context and application in a new context. Such transfer can either be of the forward-reaching kind, whereby one mindfully abstracts basic elements in anticipation for later application, or of the backward-reaching kind, where one faces a new situation and deliberately searches for relevant knowledge already acquired. Findings of transfer or nontransfer reflect whether the conditions for either low-road or high-road transfer were met. Qualitative predictions stemming from this theory of the mechanisms of transfer are offered and discussed.

We examine how technologies, particularly computer technologies that aid in cognitive processing, can support intellectual performance and enrich individuals’ minds. We distinguish between effects with and of a technology: Effects with occur when people work in partnership with machines, whereas effects of occur when such partnerships have subsequent cognitive spin-off effects for learners working away from machines. It is argued that effects both with and of depend on the individual's mindful engagement in the partnership. Such mind-machine collaborations also invite reexamination of prevailing conceptions of intelligence and ability: Are they properties of the individual or of the joint system? We respond to these dilemmas by offering two views, one emphasizing mainly the upgraded performance in a person-machine system of partnership, the other emphasizing more the educationally valued cognitive residue that can result. The use of computer tools to extend the reach of minds is briefly discussed within wider normative, theoretical, and practical contexts.

Social learning is in the air. Daily observations and experiences as well as recent scholarly traditions suggest that a certain amount of learning takes place beyond the confines of the individual mind. Learning appears to involve social aspects. Scenarios ranging from a group of children collaboratively trying to solve the question of how to construct a kite to a university professor writing a research paper with a colleague advance the case for a social side to learning. But impressions do not make social learning an obvious category. Are there any theoretical and empirical grounds to justify social learning as a distinctive phenomenon? Is there anything qualitatively different in this kind of learning to distinguish it from the familiar individual conception of learning? Can one make the case that social learning is more than an epiphenomenon or individual learning multiplied, that the social aspects of learning are anything more than the kind of secondary help a learner might get from audiovisual displays, bookmarks, and road signs? If we can raise the question of whether social learning is a valid and viable phenomenon, the opposite question might equally well be raised: Is it not possible that solo learning is simply a figment of the traditional laboratory-based psychology, on the one hand, and of a socially shared respect for the individual qua individual, on the other? The idea of social learning is not really new, having been an important part of early developments of the science of psychology (folkspsychology, as formulated, for example, by Munsterberg [1914, cited in Cole & Engestrom, 1993]). This branch of psychology fell into neglect because of its Gestalt-like nature and thus its alleged lack of rigor, its central phenomena left to anthropology and sociology to handle. It was distinguished from the more rigorous laboratory-based, experimentally oriented, and far more prestigious psychology of Ebbinghouse. Social learning has thus continued to be largely ignored by psychologists over the years, relegated at best to the study of background context, not really on a par with the learning of the individual (Gardner, 1985). This relative neglect now appears to have been corrected. With the growing interest in Vygotsky's theory, retrospective examinations of the role of social inter-

Most views of good thinking and its development hold that good thinking depends on general and specific abilities. A theory of good thinking based on the concept of dispositions is proposed here. Dispositions are often considered to be a matter of motivation. However, defined here is an expanded concept called triadic dispositions which emphasizes (a) inclinations, which may reflect motivation, habit, policy, or other factors; (b) sensitivity to occasion; and (c) abilities themselves. Advanced is a list of seven general dispositions that are argued to be collectively sufficient and individually necessary for a general characterization of good thinking. For example, these include the disposition to be broad and adventurous, the disposition toward sustained intellectual curiosity, and the disposition to be metacognitive. Finally, it is argued that a dispositional perspective on good thinking is a generative way of approaching issues concerning theories of thinking, the generality of thinking abilities, cnceptual development, culture, and education