Putting the pieces together: Revealing face–voice integration through the facial overshadowing effect

Tomlin, Rebecca; Stevenage, Sarah V.; Hammond, Sarah

doi:10.1080/13506285.2016.1245230

Cited by 8 publications

(20 citation statements)

References 31 publications

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“…Our results for multimodal dominance perception replicate and support the findings of Rezlescu et al (2015). However, they show an interestingly different pattern of results to reports of the facial overshadowing effect (Tomlin, Stevenage, & Hammond, 2016), an advantage for visual information in identity recognition. This highlights the importance of both context and task demands, and is consistent with face and voice models proposing that identity, affect, and speech information is processed along functional pathways that are mostly independent yet have some scope to interact with one another (Belin et al, 2011; Young & Bruce, 2011).…”

Section: Discussionsupporting

confidence: 78%

Audiovisual integration in social evaluation.

Mileva

Tompkinson

Watt

et al. 2018

Journal of Experimental Psychology: Human Perception and Perfor

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Our social evaluation of other people is influenced by their faces and their voices. However, rather little is known about how these channels combine in forming 'first impressions'. Over five experiments we investigate the relative contributions of facial and vocal information for social judgements: dominance and trustworthiness. The experiments manipulate each of these sources of information within-person, combining faces and voices giving rise to different social attributions. We report that vocal pitch is a reliable source of information for judgements of dominance (Study 1) but not trustworthiness (Study 4). Faces and voices make reliable, but independent contributions to social evaluation. However, voices have the larger influence in judgements of dominance (Study 2), whereas faces have the larger influence in judgements of trustworthiness (Study 5). The independent contribution of the two sources appears to be mandatory, as instructions to ignore one channel do not eliminate its influence (Study 3). Our results show that information contained in both the face and the voice contributes to first impression formation. This combination is, to some degree, outside conscious control, and the weighting of channel contribution varies according the trait being perceived.

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Section: Discussionsupporting

confidence: 78%

Audiovisual integration in social evaluation.

Mileva

Tompkinson

Watt

et al. 2018

Journal of Experimental Psychology: Human Perception and Perfor

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“…Therefore, studying the role of facial information for voice memory has longstanding traditional grounds. Essentially, while most studies show an effect of the presence of faces on subsequent voice recognition of unfamiliar speakers, it is controversial whether faces facilitate [ 15 ] or hamper [ 16 , 17 , 18 , 19 , 20 ] voice learning, compared to unimodal voice learning. (For possible factors mediating the effect, see [ 13 , 16 , 21 ].)…”

Section: Introductionmentioning

confidence: 99%

“…Essentially, while most studies show an effect of the presence of faces on subsequent voice recognition of unfamiliar speakers, it is controversial whether faces facilitate [ 15 ] or hamper [ 16 , 17 , 18 , 19 , 20 ] voice learning, compared to unimodal voice learning. (For possible factors mediating the effect, see [ 13 , 16 , 21 ].) While face-associated benefits for voice memory might be facilitated by redundant dynamic information from voices and seen lip-movements [ 13 , 22 , 23 , 24 ], face-induced costs have been explained in terms of ‘face-overshadowing’, i.e., distraction from voices during learning.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, while the presence of faces per se plays a role for audio-visual voice learning, there is hardly any data on whether and how certain facial characteristics affect voice memory. Behavioral research showed that the ‘face-overshadowing effect’ in voice recognition following voice–face learning is eliminated when the face is inverted during learning [ 16 ]. While this provides evidence that the negative impact of faces on voice processing can be nullified by disrupting facial identity recognition, the question remains whether certain facial characteristics can facilitate voice processing.…”

Section: Introductionmentioning

confidence: 99%

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Neural Correlates of Voice Learning with Distinctive and Non-Distinctive Faces

2023

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Recognizing people from their voices may be facilitated by a voice’s distinctiveness, in a manner similar to that which has been reported for faces. However, little is known about the neural time-course of voice learning and the role of facial information in voice learning. Based on evidence for audiovisual integration in the recognition of familiar people, we studied the behavioral and electrophysiological correlates of voice learning associated with distinctive or non-distinctive faces. We repeated twelve unfamiliar voices uttering short sentences, together with either distinctive or non-distinctive faces (depicted before and during voice presentation) in six learning-test cycles. During learning, distinctive faces increased early visually-evoked (N170, P200, N250) potentials relative to non-distinctive faces, and face distinctiveness modulated voice-elicited slow EEG activity at the occipito–temporal and fronto-central electrodes. At the test, unimodally-presented voices previously learned with distinctive faces were classified more quickly than were voices learned with non-distinctive faces, and also more quickly than novel voices. Moreover, voices previously learned with faces elicited an N250-like component that was similar in topography to that typically observed for facial stimuli. The preliminary source localization of this voice-induced N250 was compatible with a source in the fusiform gyrus. Taken together, our findings provide support for a theory of early interaction between voice and face processing areas during both learning and voice recognition.

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“…An even stronger effect of learning by expectation occurs with crossmodal information. When looking to a woman, one already expects to hear a female voice when she speaks [12]. This also causes an overfitting behavior when we have experienced very few examples in our lives: if we grew up near an opera house and never heard any other music style, every time we see a live show we would expect the singer to sing an opera.…”

Section: Introductionmentioning

confidence: 99%

Expectation Learning for Adaptive Crossmodal Stimuli Association

Barros¹,

Parisi²,

Fu³

et al. 2018

Preprint

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The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning model exhibits a self-adaptable behavior, setting the first steps towards the development of deep learning architectures for crossmodal stimuli association.

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Putting the pieces together: Revealing face–voice integration through the facial overshadowing effect

Cited by 8 publications

References 31 publications

Audiovisual integration in social evaluation.

Audiovisual integration in social evaluation.

Neural Correlates of Voice Learning with Distinctive and Non-Distinctive Faces

Expectation Learning for Adaptive Crossmodal Stimuli Association

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