The classification of graphical elements.

J G Hollands

doi:10.1037/h0087411

Home/Archive/PubMed

PubMed · 12674368

The classification of graphical elements.

J G Hollands

View original source ↗Export metadata ↓

Abstract

In three experiments, participants classified stimuli depicting pie charts and stacked bar graphs on two criteria: a proportion shown in the graph, and the graph's overall size (scaling). Sorting times and errors were measured. For stacked bars, performance was impaired when participants sorted on the proportion and scaling varied. No such impairment occurred for pie charts. Experiment 1 showed that varying scaling produced Garner interference in classification of proportions with stacked bars, but not pies. Experiment 2 showed that this result held when the position of the pie slice was varied; Experiment 3 results showed facilitation for particular combinations of proportion and scaling levels. In general, the results showed that proportion and scaling had an asymmetric integral relation for stacked bar graphs, but were separable dimensions for pie charts.

Explore related subjects

Attention Computer Graphics Discrimination Learning Female Humans Male Mental Processes Pattern Recognition, Visual Photic Stimulation Psychophysics Reaction Time Size Perception Visual Perception Comparative Study Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

J G Hollands. 2003. The classification of graphical elements.. https://doi.org/10.1037/h0087411

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

KEEP EXPLORING

Related citations

PubMed2003

Error in skilled performance: a control model of prescribing.

A control model of skilled performance is proposed as a framework for understanding why prescribing errors occur at a particular rate. Model error rate depends on skill level, system design characteristics, the range of different types of prescriptions that are produced and the time available to complete each prescription. The parameters of the model can be adjusted so it produces error rates that are quantitatively equal to those found in studies of the incidence of different types of prescribing error. The model will also produce error rates that are qualitatively consistent with the results of studies that show increases in prescribing error rates as a result of increases in distractions and workload. The model is used to determine the likely effectiveness of different prescribing error prevention interventions.

Attention↗

PubMed2003

Error-proneness as a handicap signal.

This paper describes two discrete signalling models in which the error-proneness of signals can serve as a handicap signal. In the first model, the direct handicap of sending a high-quality signal is not large enough to assure that a low-quality signaller will not send it. However, if the receiver sometimes mistakes a high-quality signal for a low-quality one, then there is an indirect handicap to sending a high-quality signal. The total handicap of sending such a signal may then still be such that a low-quality signaller would not want to send it. In the second model, there is no direct handicap of sending signals, so that nothing would seem to stop a signaller from always sending a high-quality signal. However, the receiver sometimes fails to detect signals, and this causes an indirect handicap of sending a high-quality signal that still stops the low-quality signaller of sending such a signal. The conditions for honesty are that the probability of an error of detection is higher for a high-quality than for a low-quality signal, and that the signaller who does not detect a signal adopts a response that is bad to the signaller. In both our models, we thus obtain the result that signal accuracy should not lie above a certain level in order for honest signalling to be possible. Moreover, we show that the maximal accuracy that can be achieved is higher the lower the degree of conflict between signaller and receiver. As well, we show that it is the conditions for honest signalling that may be constraining signal accuracy, rather than the signaller trying to make honest signals as effective as possible given receiver psychology, or the signaller adapting the accuracy of honest signals depending on his interests.

Attention↗

PubMed2003

Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest.

We assessed the relation between hemodynamic and electrical indices of brain function by performing simultaneous functional MRI (fMRI) and electroencephalography (EEG) in awake subjects at rest with eyes closed. Spontaneous power fluctuations of electrical rhythms were determined for multiple discrete frequency bands, and associated fMRI signal modulations were mapped on a voxel-by-voxel basis. There was little positive correlation of localized brain activity with alpha power (8-12 Hz), but strong and widespread negative correlation in lateral frontal and parietal cortices that are known to support attentional processes. Power in a 17-23 Hz range of beta activity was positively correlated with activity in retrosplenial, temporo-parietal, and dorsomedial prefrontal cortices. This set of areas has previously been characterized by high but coupled metabolism and blood flow at rest that decrease whenever subjects engage in explicit perception or action. The distributed patterns of fMRI activity that were correlated with power in different EEG bands overlapped strongly with those of functional connectivity, i.e., intrinsic covariations of regional activity at rest. This result indicates that, during resting wakefulness, and hence the absence of a task, these areas constitute separable and dynamic functional networks, and that activity in these networks is associated with distinct EEG signatures. Taken together with studies that have explicitly characterized the response properties of these distributed cortical systems, our findings may suggest that alpha oscillations signal a neural baseline with "inattention" whereas beta rhythms index spontaneous cognitive operations during conscious rest.

Attention↗