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Stimulus Features in Signal Detection

1971; Acoustical Society of America; Volume: 49; Issue: 6B Linguagem: Inglês

10.1121/1.1912577

1520-9024

Al Ahumada, John D. Lovell,

Structural Health Monitoring Techniques

Short bursts of computer-generated Gaussian noise were rated by observers for the presence or absence of a 500-Hz signal tone burst. A multiple regression analysis found for each observer the linear combination of the energies in narrow bands around the tone frequency that best predicts his total ratings. The estimates of the regression coefficients provide graphs of the “frequency responses” of the observers. Most of the reliable variance in the total ratings was accounted for by the regression analysis in terms of energy in narrow bands. Differences among observers are explained in terms of differential weighting by observers of features labeled “tone presence,” “pitch,” and “loudness.”