Continuous Magnitude Production of Loudness

Abstract

Continuous magnitude estimation and continuous cross-modality matching with line length can efficiently track the momentary loudness of time-varying sounds in behavioural experiments. These methods are known to be prone to systematic biases but may be checked for consistency using their counterpart, magnitude production. Thus, in Experiment 1, we performed such an evaluation for time-varying sounds. Twenty participants produced continuous cross-modality matches to assess the momentary loudness of fourteen songs by continuously adjusting the length of a line. In Experiment 2, the resulting temporal line length profile for each excerpt was played back like a video together with the given song and participants were asked to continuously adjust the volume to match the momentary line length. The recorded temporal line length profile, however, was manipulated for segments with durations between 7 to 12 s by eight factors between 0.5 and 2, corresponding to expected differences in adjusted level of −10, −6, −3, −1, 1, 3, 6, and 10 dB according to Stevens’s power law for loudness. The average adjustments 5 s after the onset of the change were −3.3, −2.4, −1.0, −0.2, 0.2, 1.4, 2.4, and 4.4 dB. Smaller adjustments than predicted by the power law are in line with magnitude-production results by Stevens and co-workers due to “regression effects.” Continuous cross-modality matches of line length turned out to be consistent with current loudness models, and by passing the consistency check with cross-modal productions, demonstrate that the method is suited to track the momentary loudness of time-varying sounds.

Public Perceptions on the Importance of Ecosystem Services From Vulnerable Forest: A Case Study of Ampang Forest Reserve, Selangor, Malaysia

“…It is important, as it often astutely highlights public awareness and addresses concerns about conserving natural ecosystems. In the literature across geographies and multidisciplinary contexts, perception is measured based on self-report measures, including magnitude estimation (e.g., Turpin et al, 2015), magnitude production (e.g., Schlittenlacher & Ellermeier, 2021), method of adjustment (e.g., Chen et al, 2019), forced-choice (e.g., Gendron et al, 2014), and Likert scale reporting (e.g., Ranacher et al, 2017). While various measurements of perception exist in the literature, a common theme when using public perceptions is to outline the judgments people make (Slovic, 1987).…”