What is special about faces? : examining face-categorisation with event-related potential measures

Multiple independent lines of research have suggested that faces are a special class of stimulus. In the last 15 years, neuroimaging studies have shown greater activation to faces than to any other stimulus category in specific areas of cortex, leading to the idea that a portion of the fusiform gyrus, also known as the fusiform face are (FFA), is face-selective (Kanwisher, McDermott, & Chun, 1997). While findings from neuroimaging, behavioural, and lesion studies support the idea of a specialised visual system for faces, it is still debated whether face sensitivity arises from either an inherent face modular network or a general processing network manifesting perceptual expertise. A modular network is an abstract cognitive concept representing functions of the brain that require rapid, automatic cognitive processing. Modules are argued to be domain specific and information encapsulated such that they do not need to interact with other cognitive processes to function. In contrast to face modularity, the expertise account of face processing argues that faces recruit domain general processing mechanisms, which are not unique to faces, but finely tuned from extensive perceptual experience. In other words, the expertise account considers faces as stimuli for which almost everyone is a skilled expert. Attempts to make progress in the debate opposing domain specific (modular) vs. domain general (expertise-based) processing has led to investigations into neurophysiological indices of face processing. Alongside the vast behavioural literature portraying the human face as a unique and special visual stimulus, electrophysiological studies have focused on a 5 negative polarity component from the N1 family and peaking at around 170 rns, the N 170. The N 170 is maximal over parietal-occipital electrode sites, and widely acknowledged as largest in amplitude to faces (Bentin, Allison, Puce, Perez, & McCarthy, 1996). Since the seminal study by Bentin et al. (1996), it has been claimed repeatedly that no visual stimulus other than faces produces negativities as pronounced in the N1 range (Itier & Taylor, 2004a). So robust is this finding that the N170 face effect has been replicated and championed to the point where it is no longer considered a hypothetical effect but rather an established fact (Eimer, 2011; Rossion & Jacques, 2008). Like fMRl, electrophysiology cannot directly elucidate the debate concerning modularity vs. expertise-based processing, since face-selectivity, observable in ERPs as an amplitude increase for faces in the N1 range, can be predicted by both theoretical standpoints. In contrast to the majority of the ERP literature, there are however, instances where face-selectivity in the N170 range was not found, particularly in the case of studies comparing full- front views of objects such as cars and butterflies (Rossion, et al., 2000; Schweinberger, Huddy, & Burton, 2004; Thierry, Martin, Downing, & Pegna, 2007a). For instance, Thierry et al. (2007a) showed that inter-stimulus variability within an object class, a factor mixing physical and perceptual variance, modulates the amplitude of the N170 component. When comparing faces with other categories of object, previous studies have often used faces presented full front, in an upright orientation, whereas contrasting object classes have often been variable in size, background, orientation, viewpoint, etc. This may have lead to imbalanced experimental comparisons artificially increasing the N170 elicited by faces because of the low inter-stimulus G variance usually inbuilt for faces in the design. Thierry et al. (2007a) compared full front views of faces with full front views of cars or butterflies and found no significant mean amplitude differences between conditions in the N 170 range. Furthermore, they reported category-sensitivity unaffected by inter-stimulus variance 70 ms earlier, in the range of the P1 (-100 ms after stimulus onset). It is noteworthy that P1 face-sensitivity has been largely overlooked in previous research, despite some reports, which have highlighted such potential sensitivity (Herrmann, Ehlis, Ellgring, '& Fallgatter, 2005; Herrmann, Ehlis, Muehlberger, & Fallgatter, 2005; Linkenkaer-Hansen, et al., 1998). In sum, Thierry et al. (2007a) questioned the validity of object categorisation experiments, which used stimuli varying in many more ways than object category, particularly in terms of low-level perceptual features. Thierry et al.'s results (2007a) have been staunchly refuted (see (Bentin, et al., 2007; but also Thierry, Martin, Downing, & Pegna, 2007b). In fact, Rossion & Jacques (2008) dedicated a review article to the dismissing of the arguments put forward by Thierry et al. (2007a). In this publication, they present new data, using a design very similar to that of Thierry et al. (2007a) but displaying face-selectivity in the N 170 range. The conflicting findings of Rossion and Jacques, (2008) and Thierry et al. (2007a), and the heavy criticism by Rossion & Jacques (2008) of Thierry et al.'s conclusions, have created some confusion within the field, questioning the established view that N 170 reflects visual object categorisation. This thesis is concerned with the further characterization of stages in face processing as indexed by ERPs. Specifically, I question the point in time 7 at which ERP waveforms can detect the first observable differences between faces and other objects, and whether these differences are indicative of a specialised process dedicated to faces. I present a series of ERP experiments explicitly testing the category sensitivity of early ERP components, namely the P1 and the N170, since their functional significance remains poorly understood. A subtheme of the thesis is to determine whether differences in ERP component amplitude constitute a reliable measure of face (and -more generally- object category) sensitivity, and if so, whether these "differences are attributable to early object categorisation or higher level processes such as individual object recognition / identification. 1 More specifically, in the present work, the aim is to address the following questions: (1) Can Thierry et al. (2007a) be replicated, and does the task involved interact with the commonly accepted N 170 category-selectivity? (2) Does inter-stimulus perceptual variance affect/interact with the N170 face inversion effect? (3) Do any other perceptual parameters affect P1 & N 170 amplitude? For instance, does cropping faces out of heads modulate P1/N170? (4) If one creates face-car hybrid using morphing algorithms, do the P1 and N170 reflect the amount of face information present in the stimulus? (5) Can expertise with complex visual stimuli entirely account for the N 170 inversion effect? At this point, it is important to make a distinction between selectivity or specificity on the one hand and sensitivity on the other. To make a genuine 1 For the purpose of clarity throughout this thesis, I will refer to object categorisation when discussing the distinction between different categories of objects and to recognition or identification when discussing the extraction of higher-level properties such as ethnic origin, emotional expression, intention, age, gender, and even familiarity or identity. 8 - / claim regarding category selectivity/specificity", one would have to test objects from every single existing conceptual category in comparison to faces (in the present case). However, an ERP component can be sensitive to a particular category of objects when its amplitude and/or latency is modulated by categorical changes, without a need for exhaustively testing all existing categories, as if this was humanly possible. 2 Throughout this thesis the terms selective/specific will be used when referring to previous research making claims in support of N170 face selectivity, whereas the term sensitivity will be used to refer to the present results and any conclusions drawn from them.EThOS - Electronic Theses Online ServiceGBUnited Kingdo