ANCHOR: A Dynamic, Memory-Based Model of Psychophysical Scaling

Abstract:

We propose ANCHOR: a memory-based model of category rating and absolute identification. It gives a principled quantitative account of over a dozen empirical phenomena (Petrov & Anderson, 2000, 2005). The stimuli are represented stochastically by internal magnitudes that must then be mapped onto the overt response scale. ANCHOR's defining claims are that (1) this mapping is memory-based and (2) stabilized by explicit corrections. Specifically, magnitude-response associations -- anchors -- stored in memory compete to match the target. Anchor selection is stochastic and depends on the similarity with the target and the base-level activations of the anchors, which in turn track their frequency and recency. These memory mechanisms integrate ANCHOR with the cognitive architecture ACT-R. Explicit correction strategies promote homomorphism and ensure stability even in the absence of feedback. A competitive learning mechanism updates the anchor locations, producing context effects that contradict earlier instance-based models. Three experiments confirm ANCHOR's predictions.