PubMed Health⌕ Search

PubMed · 6452500

Internal representation of simple temporal patterns.

Abstract

In this study the imitation of several periodically repeating simple temporal patterns consisting of two or more intervals varying in their duration ratios has been investigated. The errors that subjects typically made in their imitations and the systematic changes that occurred during repeated imitations indicate that both musically trained and untrained subjects map temporal sequences onto an interval structure the nature of which is revealed by studying which patterns are correctly and which incorrectly reproduced. A "beat-based" model for the perception of temporal sequences is proposed. This model states that the first step in the processing of a temporal sequence consists of a segmentation of the sequence into equal intervals bordered by events. This interval is called the beat interval. How listeners select this beat interval is only partly understood. In a second step, intervals smaller than the beat interval are expressed as a subdivision of the beat interval in which they occur. The number of within-beat structures that can be represented in the model is, however, limited. Specifically, only beat intervals that are subdivided into either equal intervals or intervals in a 1:2 ratio fit within the model. The partially hierarchical model proposed, though in need of further elaborations, shows why the number of temporal patterns that can be correctly conceptualized is limited. The relation of the model to other models is discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D J Povel. 1981. Internal representation of simple temporal patterns.. https://doi.org/10.1037/%2F0096-1523.7.1.3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Brain potentials reflect behavioral differences in true and false recognition.

People often falsely recognize nonstudied lures that are semantically similar to previously studied words. Behavioral research suggests that such false recognition is based on high semantic overlap between studied items and lures that yield a feeling of familiarity, whereas true recognition is more often associated with the recollection of details. Despite this behavioral evidence for differences between true and false recognition, research measuring brain activity (PET, fMRI, ERP) has not clearly differentiated corresponding differences in brain activity. A median split was used to separate subjects into Good and Poor performers based on their discrimination of studied targets from similar lures. Only Good performers showed late (1000--1500 msec), right frontal event-related brain potentials (ERPs) that were more positive for targets and lures compared with new items. The right frontal differences are interpreted as reflecting postretrieval evaluation processes that were more likely to be engaged by Good than Poor performers. Both Good and Poor performers showed a parietal ERP old/new effect (400--800 msec), but only Poor performers showed a parietal old/lure difference. These results are consistent with the view that the parietal and frontal ERP old/new effects reflect dissociable processes related to recollection.

Discrimination Learning↗

Idiosyncratic profiles of collinearity error using segments and dot pairs.

It is well known that judgments of oblique line segments are more variable and less accurate than are judgments of horizontal or vertical segments, i.e., the "oblique effect." A prior study from our laboratories confirmed these differentials for a task in which the collinearity of segments at various angular positions was judged. Further, that study found that each observer manifests a distinct, idiosyncratic profile of errors across the 360 degrees range. These error tendencies are conspicuous in models derived by harmonic analysis, and we describe significant excursions of a given model as "delta errors." The present experiments found complex profiles of delta error with various stimulus and test conditions. A given subject manifested similar models of delta error when judging collinearity of dot pairs versus line segments. In the prior work the segments to be judged were asymmetrically positioned upon the test sheet. However, the asymmetric positioning is not responsible for the errors, as the present work found errors excursions with a round display field. Similar profiles of delta error were found when subjects were allowed to mark an open space, versus being required to respond at a specific distance (indicated by a target circle). This addresses questions of whether the error should be measured as an angle. In the two final experiments, we present evidence that the source of delta error within the nervous system is at the point of binocular synthesis of the information from the two eyes, or beyond, and the effects are not due to errors of reaching. Potential neural substrates for these complex, idiosyncratic error tendencies are discussed.

Discrimination Learning↗

Haptic after-effect of successively touched curved surfaces.

A flat surface is more often judged to be convex after the touching of a concave surface than after the touching of a convex surface. This haptic after-effect increases with the time of contact with the curved surface till it saturates, and it decreases with the time-lapse between the touching of the first surface and the next one. In this paper, the haptic after-effect of two successively touched spherically curved surfaces is investigated. It is found that both surfaces contribute to the after-effect, but the after-effect is not additive. The time course of the after-effect of two successive surfaces can be described by a first-order integrator with a single time constant of about 7 s and an amplitude equal to the difference between the saturation levels of the after-effects of the two surfaces when measured in isolation. The new saturation level is therefore equal to that of the after-effect of the most recently touched surface.

Discrimination Learning↗