Biomedical subjects

Stephan Lewandowsky

Publications and source records attributed to Stephan Lewandowsky.

3 recordsLinked to original sources

PubMed2003

Context-gated knowledge partitioning in categorization.

According to the knowledge partitioning framework, people sometimes master complex tasks by creating multiple independent parcels of partial knowledge. Research has shown that knowledge parcels may contain mutually contradictory information, and that each parcel may be used without regard to knowledge that is demonstrably present in other parcels. This article reports 4 experiments that investigated knowledge partitioning in categorization. When component boundaries of a complex categorization were identified by a context cue, a significant proportion of participants learned partial and independent categorization strategies that were chosen on the basis of context. For those participants, a strategy used in one context was unaffected by knowledge demonstrably present in other contexts, suggesting that knowledge partitioning in categorization can be complete.

Cognition↗

PubMed2002

Simplified learning in complex situations: knowledge partitioning in function learning.

The authors explored the phenomenon that knowledge is not always integrated and consistent but may be partitioned into independent parcels that may contain mutually contradictory information. In 4 experiments, using a function learning paradigm, a binary context variable was paired with the continuous stimulus variable of a to-be-learned function. In the first 2 experiments, when context predicted the slope of a quadratic function, generalization was context specific. Because context did not predict function values, it is suggested that people use context to gate separate learning of simpler partial functions. The 3rd experiment showed that partitioning also occurs with a decreasing linear function, whereas the 4th study showed that partitioning is absent for a linearly increasing function. The results support the notion that people simplify complex learning tasks by acquiring independent parcels of knowledge.

Humans↗

PubMed2002

An endogenous distributed model of ordering in serial recall.

We introduce a distributed model of memory for serial order, called SOB, that produces ordered serial recall by relying on encoding and retrieval processes that are endogenous to the model. SOB explains the basic shape of the serial position curve, the pattern of errors during recall (including the balance between transpositions, omissions, intrusions, and erroneous repetitions), the effects of list length on the distribution of errors, the overall level of recall and response latency, and the effects of natural language frequency on recall performance. In addition, contrary to several recent suggestions, SOB demonstrates that distributed representations can support unambiguous recall, selective response suppression, and novelty-sensitive encoding.

Humans↗