The Role of Unimodal Feedback Pathways in Gender Perception During Activation of Voice and Face Areas

Abstract

Cross-modal effects provide a model framework for investigating hierarchical inter-areal processing, particularly, under conditions where unimodal cortical areas receive contextual feedback from other modalities. Here, using complementary behavioral and brain imaging techniques, we investigated the functional networks participating in face and voice processing during gender perception, a high-level feature of voice and face perception. Within the framework of a signal detection decision model, Maximum likelihood conjoint measurement (MLCM) was used to estimate the contributions of the face and voice to gender comparisons between pairs of audio-visual stimuli in which the face and voice were independently modulated. Top–down contributions were varied by instructing participants to make judgments based on the gender of either the face, the voice or both modalities (N = 12 for each task). Estimated face and voice contributions to the judgments of the stimulus pairs were not independent; both contributed to all tasks, but their respective weights varied over a 40-fold range due to top–down influences. Models that best described the modal contributions required the inclusion of two different top–down interactions: (i) an interaction that depended on gender congruence across modalities (i.e., difference between face and voice modalities for each stimulus); (ii) an interaction that depended on the within modalities’ gender magnitude. The significance of these interactions was task dependent. Specifically, gender congruence interaction was significant for the face and voice tasks while the gender magnitude interaction was significant for the face and stimulus tasks. Subsequently, we used the same stimuli and related tasks in a functional magnetic resonance imaging (fMRI) paradigm (N = 12) to explore the neural correlates of these perceptual processes, analyzed with Dynamic Causal Modeling (DCM) and Bayesian Model Selection. Results revealed changes in effective connectivity between the unimodal Fusiform Face Area (FFA) and Temporal Voice Area (TVA) in a fashion that paralleled the face and voice behavioral interactions observed in the psychophysical data. These findings explore the role in perception of multiple unimodal parallel feedback pathways.

Conjoint measurement of perceived transparency and perceived contrast in variegated checkerboards

“…The rapid increase in number of trials for this type of scaling procedure (Maximum Likelihood Difference Scaling (MLDS), MLCM) renders the procedures less attractive from a pragmatic point of view and might sometimes outweigh the theoretical benefits discussed above. Alternative strategies such as subsampling ( Knoblauch & Maloney, 2012 ; Abbatecola et al, 2021 ), having a reduced number of stimuli per dimension ( Sun et al, 2021 ), or the use of so-called embedded methods from the machine learning community (see Haghiri et al, 2020 , for example) are currently being explored and might allow more efficient ways of perceptual scale measurements in the future.…”

Understanding the Impact of Face Masks on the Processing of Facial Identity, Emotion, Age, and Gender

“…In this sense, inferences regarding primary facial dimensions such as identity, emotion, gender or age, can be made based on other cues and modalities than the obvious ones. These include: voice (Golan and Baron-Cohen, 2006 ; Knoblauch et al, 2021 ), gate (Cutting and Kozlowski, 1977 ), body gestures (Aviezer et al, 2012 ), and other social context of a situation (Mondloch, 2012 ). For example, voice characteristics can assist observers to disambiguate gender (Knoblauch et al, 2021 ).…”

“…These include: voice (Golan and Baron-Cohen, 2006 ; Knoblauch et al, 2021 ), gate (Cutting and Kozlowski, 1977 ), body gestures (Aviezer et al, 2012 ), and other social context of a situation (Mondloch, 2012 ). For example, voice characteristics can assist observers to disambiguate gender (Knoblauch et al, 2021 ).…”

“…People have learned to cope with such debilitating conditions by harnessing unique processing strategies. For example, people can take advantage of additional contextual cues from voice (Golan and Baron-Cohen, 2006 ; Knoblauch et al, 2021 ), gate (Cutting and Kozlowski, 1977 ), body gestures (Aviezer et al, 2012 ), and the social context of a situation (Mondloch, 2012 ) to make correct inferences on whether a face belongs to a given category. Moreover, not all aspects of performance (e.g., speed) should suffer to the same extent.…”

Visual biases in evaluation of speakers’ and singers’ voice type by cis and trans listeners

“…The way the brain perceives gender must integrate different modalities. This multimodal aspect is echoed in the work of Peynircioǧlu et al (2017) , Brang (2019) , and Abbatecola et al (2021) , who all designed experiments modeled on either McGurk effect or Ventriloquist effect to investigate auditory–visual interactions in voice/face gender perception. As a reminder, the McGurk effect is an illusion caused by multimodal integration in which the auditory element of one stimulus (e.g., the spoken syllable “ba”) is merged with the visual element of a second stimulus (e.g., the lip movement of “ga”), leading to the perception of an intermediate stimulus (e.g., “da”) which was never presented.…”