Audiovisual integration in macaque face patch neurons.

Primate social communication depends on the perceptual integration of visual and auditory cues, reflected in the multimodal mixing of sensory signals in certain cortical areas. The macaque cortical face patch network, identified through visual, face-selective responses measured with fMRI, is assumed to contribute to visual social interactions. However, whether face patch neurons are also influenced by acoustic information, such as the auditory component of a natural vocalization, remains unknown. Here, we recorded single-unit activity in the anterior fundus (AF) face patch, in the superior temporal sulcus, and anterior medial (AM) face patch, on the undersurface of the temporal lobe, in macaques presented with audiovisual, visual-only, and auditory-only renditions of natural movies of macaques vocalizing. The results revealed that 76% of neurons in face patch AF were significantly influenced by the auditory component of the movie, most often through enhancement of visual responses but sometimes in response to the auditory stimulus alone. By contrast, few neurons in face patch AM exhibited significant auditory responses or modulation. Control experiments in AF used an animated macaque avatar to demonstrate, first, that the structural elements of the face were often essential for audiovisual modulation and, second, that the temporal modulation of the acoustic stimulus was more important than its frequency spectrum. Together, these results identify a striking contrast between two face patches and specifically identify AF as playing a potential role in the integration of audiovisual cues during natural modes of social communication.

Probabilistic encoding of vocalizations in macaque ventral lateral prefrontal cortex.

We examined strategies for classifying macaque vocalizations into their corresponding categories, as well as whether or not there was evidence that prefrontal auditory neurons were related to this process. We found that static estimates of the spectral and temporal contrasts of the calls were not effective features for discriminating among the call classes. A hidden Markov model (HMM), however, was more effective at discriminating among the call classes, reaching a performance of almost 75% correct. Finally, we found that the responses of prefrontal auditory neurons could be predicted more effectively as linear functions of the probabilistic output of the HMM than as linear functions of the spectral features of the calls. This provides evidence that, for call recognition, the macaque auditory system likely performs dynamic processing of vocalizations, and that prefrontal auditory neurons carry a signal related to the output of this processing.