The effects of high variability training on voice identity learning

Lavan, Nadine; Knight, Sarah; Hazan, Valérie; McGettigan, Carolyn

doi:10.1016/j.cognition.2019.104026

Cited by 29 publications

(30 citation statements)

References 30 publications

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“…Listeners were asked to listen carefully and try to memorize the voice and the name (cf. Lavan, Knight, Hazan & McGettigan, 2019; Lavan, Knight & McGettigan, 2019, for studies using a similar training paradigm). For the valenced training groups, an additional vignette was presented as text alongside the name on the screen during playback (e.g., ‘She helped an elderly man cross the road’ and ‘She is very patient when dealing with other people’s problems’ for positive valence; and ‘She is lying about her age in her dating profile’ and ‘She didn't apologise even though she knew she was in the wrong’ for negative valence).…”

Section: Methodsmentioning

confidence: 99%

How does familiarity with a voice affect trait judgements?

Lavan

Mileva

McGettigan

2020

British J of Psychology

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From only a single spoken word, listeners can form a wealth of first impressions of a person’s character traits and personality based on their voice. However, due to the substantial within‐person variability in voices, these trait judgements are likely to be highly stimulus‐dependent for unfamiliar voices: The same person may sound very trustworthy in one recording but less trustworthy in another. How trait judgements differ when listeners are familiar with a voice is unclear: Are listeners who are familiar with the voices as susceptible to the effects of within‐person variability? Does the semantic knowledge listeners have about a familiar person influence their judgements? In the current study, we tested the effect of familiarity on listeners’ trait judgements from variable voices across 3 experiments. Using a between‐subjects design, we contrasted trait judgements by listeners who were familiar with a set of voices – either through laboratory‐based training or through watching a TV show – with listeners who were unfamiliar with the voices. We predicted that familiarity with the voices would reduce variability in trait judgements for variable voice recordings from the same identity (cf. Mileva, Kramer & Burton, Perception, 48, 471 and 2019, for faces). However, across the 3 studies and two types of measures to assess variability, we found no compelling evidence to suggest that trait impressions were systematically affected by familiarity.

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Section: Methodsmentioning

confidence: 99%

How does familiarity with a voice affect trait judgements?

Lavan

Mileva

McGettigan

2020

British J of Psychology

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“…Online experimental platforms are increasingly used as an alternative, or complement, to in-lab work (Assaneo et al, 2019;Kell et al, 2018;Lavan, Knight, Hazan, et al, 2019;McPherson & McDermott, 2018;Slote & Strand, 2016;Woods & McDermott, 2018;Zhao et al, 2019). This process has been hastened in recent months by the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

An online headphone screening test based on dichotic pitch

et al. 2020

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Online experimental platforms can be used as an alternative to, or complement, lab-based research. However, when conducting auditory experiments via online methods, the researcher has limited control over the participants’ listening environment. We offer a new method to probe one aspect of that environment, headphone use. Headphones not only provide better control of sound presentation but can also “shield” the listener from background noise. Here we present a rapid (< 3 min) headphone screening test based on Huggins Pitch (HP), a perceptual phenomenon that can only be detected when stimuli are presented dichotically. We validate this test using a cohort of “Trusted” online participants who completed the test using both headphones and loudspeakers. The same participants were also used to test an existing headphone test (AP test; Woods et al., 2017, Attention Perception Psychophysics). We demonstrate that compared to the AP test, the HP test has a higher selectivity for headphone users, rendering it as a compelling alternative to existing methods. Overall, the new HP test correctly detects 80% of headphone users and has a false-positive rate of 20%. Moreover, we demonstrate that combining the HP test with an additional test–either the AP test or an alternative based on a beat test (BT)–can lower the false-positive rate to ~ 7%. This should be useful in situations where headphone use is particularly critical (e.g., dichotic or spatial manipulations). Code for implementing the new tests is publicly available in JavaScript and through Gorilla (gorilla.sc).

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Section: Introductionmentioning

confidence: 99%

An online headphone screening test based on dichotic pitch

Milne

Bianco

Poole

et al. 2020

Preprint

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Online experimental platforms can be used as an alternative, or complement, to lab-based research. However, when conducting auditory experiments via online methods, the researcher has limited control over the participants’ listening environment. We offer a new method to probe one aspect of that environment, headphone use. Headphones not only provide better control of sound presentation but can also “shield” the listener from background noise. Here we present a rapid (< 3 minute) headphone screening test based on Huggins Pitch (HP), a perceptual phenomenon that can only be detected when stimuli are presented dichotically. We validate this test using a cohort of “Trusted” online participants who completed the test using both headphones and loudspeakers. The same participants also trialled a widely used headphone test (AP test; Woods et al., 2017). We demonstrate that compared to the AP test, the HP test has a higher selectively for headphone users, rendering it as a compelling alternative to the existing screening method. Overall, the new HP test correctly detects 80% of headphone users and has a false positive rate of 20%. Moreover, we demonstrate that there is little overlap between participants who pass both HP and AP tests over loudspeakers. Therefore, combining the two tests can lower the false positive rate to ~7% (but at the expense of an increased false negative rate). This should be useful in situations where headphone use is particularly critical (e.g. dichotic or spatial manipulations). An implementation of the new test is available with JavaScript and through Gorilla (gorilla.sc).

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The effects of high variability training on voice identity learning

Cited by 29 publications

References 30 publications

How does familiarity with a voice affect trait judgements?

How does familiarity with a voice affect trait judgements?

An online headphone screening test based on dichotic pitch

An online headphone screening test based on dichotic pitch

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