The Neural Code for Face Orientation in the Human Fusiform Face Area

Abstract

Humans recognize faces and objects with high speed and accuracy regardless of their orientation. Recent studies have proposed that orientation invariance in face recognition involves an intermediate representation where neural responses are similar for mirrorsymmetric views. Here, we used fMRI, multivariate pattern analysis, and computational modeling to investigate the neural encoding of faces and vehicles at different rotational angles. Corroborating previous studies, we demonstrate a representation of face orientation in the fusiform face-selective area (FFA). We go beyond these studies by showing that this representation is category-selective and tolerant to retinal translation. Critically, by controlling for low-level confounds, we found the representation of orientation in FFA to be compatible with a linear angle code. Aspects of mirror-symmetric coding cannot be ruled out when FFA mean activity levels are considered as a dimension of coding. Finally, we used a parametric family of computational models, involving a biased sampling of view-tuned neuronal clusters, to compare different face angle encoding models. The best fitting model exhibited a predominance of neuronal clusters tuned to frontal views of faces. In sum, our findings suggest a category-selective and monotonic code of face orientation in the human FFA, in line with primate electrophysiology studies that observed mirror-symmetric tuning of neural responses at higher stages of the visual system, beyond the putative homolog of human FFA.

Space reconstruction by primary visual cortex activity: a parallel, non-computational mechanism of object representation

“…Research in homologous areas in the human visual cortex is consistent with single-cell data from the monkey IT cortex [22][23][24][25]. Experimentalists invariably identify the temporal cortex as the site of object representation and recognition [15][16][17][20][21][22][23][24][25]. Presumably, responses of orientation-selective V1 cells give rise to perception of short line segments [26] while perception of more complex objects such as eyes or faces is predicated on the responses of cells in areas that are higher on the hierarchical ladder such as V4 or the IT cortex [27].…”

“…The hierarchical transformations leading to face-selective cells are paralleled by an increase in spatial integration from cells integrating over a few minutes of arc in V1 to cells at the pinnacle of the hierarchy responding within a large portion of the visual field (VF). Research in homologous areas in the human visual cortex is consistent with single-cell data from the monkey IT cortex [22][23][24][25]. Experimentalists invariably identify the temporal cortex as the site of object representation and recognition [15][16][17][20][21][22][23][24][25].…”

A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

“…Also, in an extreme case, a single neuron might contain substantial information that is drowned out by other neurons only contributing noise (e.g., Etzel et al, 2013). The tuning of a single voxel thus depends on the sampling of neurons in a complex way that can only be unraveled by direct invasive measurement of population signals in combination with computational modeling Kriegeskorte, 2011;Nevado et al, 2004;Ramírez et al, 2014). Interpreting Accuracies: Overestimating Information There are several ways in which an observed accuracy with fMRI might overestimate the information that is computationally available at the neural level.…”

The neural basis of visual symmetry and its role in mid‐ and high‐level visual processing

“…Ventral regions of the human visual system compute object representations that are robust to transformations, like depth rotations, which preserve solid shape structure . Recent computational models have shown that neurons that learn according to a broad class of Hebb‐like rules have responses invariant to viewpoint …”