A memory-based scaling model—ANCHOR—is proposed and tested. The perceived magnitude of the target stimulus is compared with 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 2 experiments in absolute identification and category rating. Category rating is a widely used method of data collection in experimental psychology. Ratings come in a wide variety of guises: psychophysical scales, similarity judgments, typicality judgments, confidence ratings, attitude questionnaires, health selfreports, and many others. The participants in all these tasks are asked to rate things using an ordered set of categories such as 1,..., 7 or strongly agree,..., strongly disagree. Most people

This paper presents an unusual prediction made by the DUAL-based model of judgment JUDGEMAP and its verification. The model is shortly presented as well as the simulation data obtained with it. These data predict that people will use the information on an irrelevant dimension when judging another dimension. This prediction is then tested in a psychological experiment and confirmed.

This paper presents a memory-based model of direct psychophysical scaling. The model is based on an extension of the cognitive architecture ACT-R and uses anchors that serve as prototypes for the stimuli classified within each response category. Using the ANCHOR model as a specific example, a general Bayesian framework is introduced. It provides principled methods for making data-based inferences about models of this kind. The internal representations in the model are analyzed as hidden variables that are constructed from the stimuli according to probabilistic representation rules. In turn, the hidden representations produce overt responses via probabilistic performance rules. Incremental learning rules transform the model into a dynamic system. A parameter-fitting algorithm is formulated and tested on experimental data.