Measuring human aversion to sound without verbal descriptors

Molino, John A.

doi:10.3758/bf03203947

Cited by 7 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 65 subjects partitioned among the experiments were mainly suburban housewives with high school educations. By using binary choice, no were required concerning subjects' knowledge of number Further, by the task as simply choosing the sound in each pair that the person would rather hear, verbal characterizations of the sounds' attributes, and of the subjects' reactions, were unnecessary (see Molino, 1974, for arguments against verbal descriptors in psychophysical tasks). The object of the experiments was to determine preference differences among the sounds, and then to account for these differences using various acoustical measures of the sounds.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Approach to the Analysis of Binary Choice Data

Cermak

Lieberman

Benson

1982

Applied Psychological Measurement

View full text Add to dashboard Cite

An integrated package of statistics is described for measuring the relationship between binary dominance data, such as choices, and predictor variables that measure the objects or stimuli. The degree of fit between predictors and choices is defined, and procedures for determining the maximum possible fit and minimum acceptable fit are described. The statistics are applied, for illustrative purposes, to a practical data set.In the course of work on a problem in applied psychological measurement, a collection of techniques for analyzing binary choice data was assembled. Individually, the techniques are not new. Collectively, the techniques allow the asking of a set of questions that previously had not been applied to binary choice data in an integrated way. The questions themselves are commonly asked of other sorts of data To make the discussion concrete, suppose there is a set of items and an ordering of them by preferability (or by utility or goodness, for example) is being sought. Suppose also that various attributes of each item can be measured and that there is some point in accounting for the preferability of items in terms of these measured attributes.Some index of fit between preferability and a of an attribute can be computed, for example, a correlation. The following questions can be asked or are implicit in the use of most fit statistics: (1) How well does a given measure account for preferability compared to the maximum possible fit? (2) How well does the measure perform compared to the minimum possible fit? If rated preferability and some measure of the were being correlated, these questions would be trivial: The maximum possible correlation is 1.0 (or -1.0) and the minimum is 0.0. If accounting for choices between pairs of items by means of measured attributes were being attempted, the same questions would be more difficult. The topic of this paper is the problem of assessing how well a measure, or an independent variable, accounts for observed choices. In solving this problem several questions must be asked of the data, including the two questions above. Methods are presented for answering these questions, and the present approach to the problem is illustrated with a practical data set.First, why collect disaggregated choice data? More generally, why observe binary dominance data? Dominance is one of the most primitive relationships between two items, and choice is a basic observable indicator of the relative preferability of two items. Choice between two items is a simple task for untrained individuals; it avoid the use of response scales, and it does not require persons to match numbers to items. The experimenter assumes very little about persons, at NORTH DAKOTA STATE UNIV LIB on June 9, 2015 apm.sagepub.com Downloaded from

show abstract

Section: Methodsmentioning

confidence: 99%

An Integrated Approach to the Analysis of Binary Choice Data

Cermak

Lieberman

Benson

1982

Applied Psychological Measurement

View full text Add to dashboard Cite

show abstract

Loudness in the Laboratory, Part II: Non-Steady-State Sounds

Kuwano

Namba²

2010

Loudness

View full text Add to dashboard Cite

Adults' Tolerance of Cries: An Experimental Investigation of Acoustic Features

Bisping¹,

Steingrueber²,

Oltmann³

et al. 1990

Child Development

View full text Add to dashboard Cite

Variations in the aversiveness of a newborn's distress cry were examined using experimental manipulation of 3 acoustic features of the cry (fundamental frequency, intonation, spectral complexity) and of the information given to subjects about the infant's health status. The subjects (N = 164 medical students) were allowed to control the sound pressure level (range: 50 dB [A]-105 dB[A]) of the different cry versions by repeatedly pressing a button (operant titration). For the nonmanipulated (original) cry version, the accepted sound pressure level decreased as fundamental frequency increased at above 610 Hz. This effect was modified by experimental manipulation of spectral complexity as well as intonation. A preference of intonated cries over monotonous cries was most pronounced between 534 and 686 Hz. Significant interactions of the information variable with the intonation and the complexity variables suggest that the listener's reaction to certain physical cry characteristics can be altered substantially by inducing different cognitive sets.

show abstract

Measuring human aversion to sound without verbal descriptors

Cited by 7 publications

References 15 publications

An Integrated Approach to the Analysis of Binary Choice Data

An Integrated Approach to the Analysis of Binary Choice Data

Loudness in the Laboratory, Part II: Non-Steady-State Sounds

Adults' Tolerance of Cries: An Experimental Investigation of Acoustic Features

Contact Info

Product

Resources

About