Biomedical subjects

Michael L Kalish

Publications and source records attributed to Michael L Kalish.

3 recordsLinked to original sources

PubMed2005

Error-driven knowledge restructuring in categorization.

Knowledge restructuring occurs when people shift to a new strategy or representation during learning. Although knowledge restructuring can frequently be experimentally encouraged, there are instances in which people resist restructuring and continue to use an expedient but imperfect initial strategy. The authors report 3 category learning experiments that reconciled those conflicting outcomes by postulating that, for restructuring to occur, learners must be dissatisfied with their knowledge and a usable alternative must be available. In line with expectation, restructuring was elicited only when an alternative strategy was pointed out and when people's initial expedient strategy entailed performance error. Neither error nor information about the alternative strategy by itself was sufficient to induce restructuring.

Attention↗

PubMed2004

Population of linear experts: knowledge partitioning and function learning.

Knowledge partitioning is a theoretical construct holding that knowledge is not always integrated and homogeneous but may be separated into independent parcels containing mutually contradictory information. Knowledge partitioning has been observed in research on expertise, categorization, and function learning. This article presents a theory of function learning (the population of linear experts model--POLE) that assumes people partition their knowledge whenever they are presented with a complex task. The authors show that POLE is a general model of function learning that accommodates both benchmark results and recent data on knowledge partitioning. POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction.

Analysis of Variance↗

PubMed2002

A multidimensional scaling approach to mental multiplication.

Adults consistently make errors in solving simple multiplication problems. These errors have been explained with reference to the interference between similar problems. In this paper, we apply multidimensional scaling (MDS) to the domain of multiplication problems, to uncover their underlying similarity structure. A tree-sorting task was used to obtain perceived dissimilarity ratings. The derived representation shows greater similarity between problems containing larger operands and suggests that tie problems (e.g., 7 x 7) hold special status. A version of the generalized context model (Nosofsky, 1986) was used to explore the derived MDS solution. The similarity of multiplication problems made an important contribution to producing a model consistent with human performance, as did the frequency with which such problems arise in textbooks, suggesting that both factors may be involved in the explanation of errors.

Adult↗