Task-Specific Codes for Face Recognition: How they Shape the Neural Representation of Features for Detection and Individuation

Abstract

BackgroundThe variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing.Methodology/Principal FindingsOur study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the sensitivity of the human visual system to these different face descriptions independently of one another. Both behavior and functional magnetic resonance imaging reveal effects elicited by objective task-specific levels of information. Behaviorally, recognition performance with image fragments improves with increasing task-specific information carried by different face fragments. Neurally, this sensitivity to the two tasks manifests as differential localization of neural responses across the ventral visual pathway. Fragments diagnostic for detection evoke larger neural responses than non-diagnostic ones in the right posterior fusiform gyrus and bilaterally in the inferior occipital gyrus. In contrast, fragments diagnostic for individuation evoke larger responses than non-diagnostic ones in the anterior inferior temporal gyrus. Finally, for individuation only, pattern analysis reveals sensitivity to task-specific information within the right “fusiform face area”.Conclusions/SignificanceOur results demonstrate: 1) information diagnostic for face detection and individuation is roughly separable; 2) the human visual system is independently sensitive to both types of information; 3) neural responses differ according to the type of task-relevant information considered. More generally, these findings provide evidence for the computational utility and the neural validity of fragment-based visual representation and recognition.

Many Faces of Expertise: Fusiform Face Area in Chess Experts and Novices

“…While it seems the identity of faces is encoded and retrieved in FFA (Haxby et al, 2000;Lehmann et al, 2004;Winston et al, 2004;Calder and Young, 2005;Loffler et al, 2005; but see Rotshtein et al, 2005;Kriegeskorte et al, 2007;Nestor et al, 2008), we have recently shown that the utilization of chess knowledge structures-a process similar to the identification of faces-is related to the middle part of the collateral sulcus (Bilalić et al, 2010). The discrepancy and similarities in the specialization of the ventral visual stream for distinct stimuli such as faces and chess positions invites conjectures as to the role of FFA in visual expertise.…”

Unraveling the distributed neural code of facial identity through spatiotemporal pattern analysis

“…A central theme of this research emphasizes the distribution of face processing across a network of spatially segregated areas (4-10). However, there remains considerable disagreement about how information is represented and processed within this network to support tasks such as individuation, expression analysis, or high-level semantic processing.One influential view proposes an architecture that maps different tasks to distinct, unique cortical regions (6) and, as such, draws attention to the specificity of this mapping (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). As a case in point, face individuation (e.g., differentiating Steve Jobs from Bill Gates across changes in expression) is commonly mapped onto the fusiform face area (FFA) (6, 21).…”

“…One influential view proposes an architecture that maps different tasks to distinct, unique cortical regions (6) and, as such, draws attention to the specificity of this mapping (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). As a case in point, face individuation (e.g., differentiating Steve Jobs from Bill Gates across changes in expression) is commonly mapped onto the fusiform face area (FFA) (6,21).…”

“…As a case in point, face individuation (e.g., differentiating Steve Jobs from Bill Gates across changes in expression) is commonly mapped onto the fusiform face area (FFA) (6,21). Although recent studies have questioned this role of the FFA (14,15), overall they agree with this task-based architecture as they single out other areas supporting individuation.…”

Face detection differs from categorization: Evidence from visual search in natural scenes

“…Some research has employed paradigms in which faces are cropped from extraneous background and presented in the center of visual displays (e.g., Liu, Harris, & Kanwisher, 2002;Nestor, Vettel, & Tarr, 2008). Other studies have used more complex displays, such as visual search arrays (e.g., Hershler & Hochstein, 2005) and natural scenes (e.g., ).…”