The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification

Abstract:

A memory-based scaling model--ANCHOR--is proposed and tested. The perceived magnitude of the target stimulus is compared to a set of anchors in memory. Anchor selection is probabilistic and sensitive to similarity, base-level strength, and recency. The winning anchor provides a reference point near the target and thereby converts the global scaling problem into a local comparison. An explicit correction strategy determines the final response. Two incremental learning mechanisms update the locations and base-level activations of the anchors. This gives rise to sequential, context, transfer, practice, and other dynamic effects. The scale unfolds as an adaptive map. A hierarchy of models is tested on a battery of quantitative measures from two experiments in absolute identification and category rating.

Reprint (pdf) ANCHOR Project Software Companion paper