A model of memory retrieval is described. The model embodies 4 main claims: (a) temporal memory— traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity—similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness—performance on a range of memory tasks is determined by interference from near psychological neighbors; and (d) interference-based forgetting—all memory loss is due to interference and not trace decay. The model is applied to data on free recall and serial recall. The account emphasizes qualitative similarity in the retrieval principles involved in memory performance at all timescales, contrary to models that emphasize distinctions between short-term and long-term memory.

In unidimensional absolute identification tasks, participants identify stimuli that vary along a single dimension. Performance is surprisingly poor compared with discrimination of the same stimuli. Existing models assume that identification is achieved using long-term representations of absolute magnitudes. The authors propose an alternative relative judgment model (RJM) in which the elemental perceptual units are representations of the differences between current and previous stimuli. These differences are used, together with the previous feedback, to respond. Without using long-term representations of absolute magnitudes, the RJM accounts for (a) information transmission limits, (b) bowed serial position effects, and (c) sequential effects, where responses are biased toward immediately preceding stimuli but away from more distant stimuli (assimilation and contrast).

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of

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.

N. Stewart, G. D. A. Brown, and N. Chater (2005) presented a relative judgment model (RJM) of absolute identification, in which the current stimulus is judged relative to the preceding stimulus. S. Brown, A. A. J. Marley, and Y. Lacouture (2007) found that the RJM does not predict their finding of increased accuracy after large stimulus jumps, except at the expense of other effects. In fact, the RJM does predict both the core effects and increased accuracy after large jumps (although it underestimates this effect) when better constrained parameters are estimated from the trial-by-trial raw data rather than from summary plots. Further, a modified RJM, in which the stimulus from two trials ago is sometimes used as a referent, provides a better fit.

Running head: Bootstrap Markov chain Monte CarloBootstrap Markov Chain Monte Carlo 2 A novel procedure is described for accelerating the convergence of Markov chain Monte Carlo computations. The algorithm uses an adaptive bootstrap technique to generate candidate steps in the Markov Chain. It is efficient for symmetric, convex probability distributions, similar to multivariate Gaussians, and it can be used for Bayesian estimation or for obtaining maximum likelihood solutions with confidence limits. As a test case, the Law of Categorical Judgment (Corrected) was fitted with the algorithm to data sets from simulated rating scale experiments. The correct parameters were recovered from practical-sized data sets simulated for Full Signal Detection Theory and its special cases of standard Signal Detection Theory and