The McGurk effect from single and mixed speaker stimuli

Rosenblum, Lawrence D.; Yakel, Deborah A.

doi:10.1121/1.1366356

Cited by 3 publications

(2 citation statements)

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“…Numerous filtering approaches do not suppress integration (Rosenblum and Saldaña, 1996; Campbell and Massaro, 1997; Jordan et al, 2000; MacDonald et al, 2000) suggesting that the use of multiple visual cues [e.g., luminance patterns (Jordan et al, 2000); kinematics (Rosenblum and Saldaña, 1996)]. Additionally, neither the gender (Walker et al, 1995) nor the familiarity (Rosenblum and Yakel, 2001) of the face impacts the robustness of AV speech integration. As will be discussed later, AV speech integration also remains resilient to large AV asynchronies (cf.…”

Section: Weighting Sensory Evidence Against Internal Non-invariancementioning

confidence: 99%

Speech through ears and eyes: interfacing the senses with the supramodal brain

Wassenhove¹

2013

Front. Psychol.

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The comprehension of auditory-visual (AV) speech integration has greatly benefited from recent advances in neurosciences and multisensory research. AV speech integration raises numerous questions relevant to the computational rules needed for binding information (within and across sensory modalities), the representational format in which speech information is encoded in the brain (e.g., auditory vs. articulatory), or how AV speech ultimately interfaces with the linguistic system. The following non-exhaustive review provides a set of empirical findings and theoretical questions that have fed the original proposal for predictive coding in AV speech processing. More recently, predictive coding has pervaded many fields of inquiries and positively reinforced the need to refine the notion of internal models in the brain together with their implications for the interpretation of neural activity recorded with various neuroimaging techniques. However, it is argued here that the strength of predictive coding frameworks reside in the specificity of the generative internal models not in their generality; specifically, internal models come with a set of rules applied on particular representational formats themselves depending on the levels and the network structure at which predictive operations occur. As such, predictive coding in AV speech owes to specify the level(s) and the kinds of internal predictions that are necessary to account for the perceptual benefits or illusions observed in the field. Among those specifications, the actual content of a prediction comes first and foremost, followed by the representational granularity of that prediction in time. This review specifically presents a focused discussion on these issues.

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Section: Weighting Sensory Evidence Against Internal Non-invariancementioning

confidence: 99%

Speech through ears and eyes: interfacing the senses with the supramodal brain

Wassenhove¹

2013

Front. Psychol.

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“…Multiple speakers were presented to each participant, but this was not expected to have an influence on audiovisual perception. That is, switching speaker identity from trial to trial has been observed to influence perception of either auditory or visual speech (Mullenix, Pisoni, & Martin, 1989;Yakel, Rosenblum, & Fortier, 2000), but identity variation does not appear to influence the audiovisual McGurk effect (Rosenblum & Yakel, 2001). Thus, the present experiment was designed to compare self-speech with nonself-speech.…”

Section: Introductionmentioning

confidence: 99%

Can you McGurk yourself? Self-face and self-voice in audiovisual speech

Aruffo

Shore

2011

Psychon Bull Rev

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We are constantly exposed to our own face and voice, and we identify our own faces and voices as familiar. However, the influence of self-identity upon self-speech perception is still uncertain. Speech perception is a synthesis of both auditory and visual inputs; although we hear our own voice when we speak, we rarely see the dynamic movements of our own face. If visual speech and identity are processed independently, no processing advantage would obtain in viewing one's own highly familiar face. In the present experiment, the relative contributions of facial and vocal inputs to speech perception were evaluated with an audiovisual illusion. Our results indicate that auditory self-speech conveys a processing advantage, whereas visual self-speech does not. The data thereby support a model of visual speech as dynamic movement processed separately from speaker recognition.

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