Biomedical subjects

Siegfried Macho

Publications and source records attributed to Siegfried Macho.

3 recordsLinked to original sources

PubMed2004

Modeling associative recognition: a comparison of two-high-threshold, two-high-threshold signal detection, and mixture distribution models.

A 2-high-threshold signal detection (HTSDT) model, a mixture distribution (SON) model, and 2-high-threshold (HT) models with responses distributed over 1 or several response categories were fit to results of 6 experiments from 2 studies on associative recognition: R. Kelley and J. T. Wixted (2001) and A. P. Yonelinas (1997). HTSDT assumes that associative recognition is based on conscious recollection and familiarity assessment, whereas according to SON and HT, associative information results in a shift of familiarity. The modeling results cast doubt on the prominent role of conscious recollection, and as far as models are valid, parameter estimation suggests 2 processes in associative recognition: a shift in familiarity that is due to associative information and the determination of the source of familiarity of pairs.

Association↗

PubMed2002

Cognitive modeling with spreadsheets.

Spreadsheet implementations of two different types of cognitive models--a neural network model and a statistical model--are presented. The two examples illustrate how to employ the facilities of spreadsheets, the spreadsheet data structure, array functions, the built-in function library, and the integrated optimizer, for building cognitive models. The two presented models are new extensions of existing models. They are used for simulating data from experiments illustrating that the extended versions are able to explain experimental results that could not be simulated by the original models. The whole simulation study demonstrates that spreadsheets are a handy tool, especially for researchers without programming knowledge who want to build cognitive models and for instructors teaching cognitive modeling.

Algorithms↗

PubMed2002

Recursive retrospective revaluation of causal judgments.

Recursive causal evaluation is an iterative process in which the evaluation of a target cause, T, is based on the outcome of the evaluation of another cause, C, the evaluation of which itself depends on the evaluation of a 3rd cause, D. Retrospective revaluation consists of backward processing of information as indicated by the fact that the evaluation of T is influenced by subsequent information that is not concerned with T directly. Two experiments demonstrate recursive retrospective revaluation with contingency information presented in list format as well as with trial-by-trial acquisition. Existing associative models are unable to predict the results. The model of recursive causal disambiguation that conceptualizes the revaluation as a recursive process of disambiguation predicts the pattern of results correctly.

Adult↗