Expertise Effects in Face-Selective Areas are Robust to Clutter and Diverted Attention, but not to Competition

Abstract

Expertise effects for nonface objects in face-selective brain areas may reflect stable aspects of neuronal selectivity that determine how observers perceive objects. However, bottom-up (e.g., clutter from irrelevant objects) and top-down manipulations (e.g., attentional selection) can influence activity, affecting the link between category selectivity and individual performance. We test the prediction that individual differences expressed as neural expertise effects for cars in face-selective areas are sufficiently stable to survive clutter and manipulations of attention. Additionally, behavioral work and work using event related potentials suggest that expertise effects may not survive competition; we investigate this using functional magnetic resonance imaging. Subjects varying in expertise with cars made 1-back decisions about cars, faces, and objects in displays containing one or 2 objects, with only one category attended. Univariate analyses suggest car expertise effects are robust to clutter, dampened by reducing attention to cars, but nonetheless more robust to manipulations of attention than competition. While univariate expertise effects are severely abolished by competition between cars and faces, multivariate analyses reveal new information related to car expertise. These results demonstrate that signals in face-selective areas predict expertise effects for nonface objects in a variety of conditions, although individual differences may be expressed in different dependent measures depending on task and instructions.

Domain-specific reports of visual imagery vividness are not related to perceptual expertise

“…Conversely, to foreshadow our results, we did not find that these two measures relate, suggesting that performance on a car recognition test has little to no bearing on self-reported imagery vividness. The VET (and similar measures) has been used to define expertise in a large number of published studies and has produced measures with high internal consistencies (Bowles et al, 2009;Dennett et al, 2012;Duchaine & Nakayama, 2006;Gauthier et al, 2014;Germine, Duchaine, & Nakayama, 2011;McGugin, Van Gulick, Tamber-Rosenau, Ross, & Gauthier, 2015;Richler et al, 2011;Woolley, Gerbasi, Chabris, Kosslyn, & Hackman, 2008). Other than perceptual tests, the main alternative way to define expertise is self-report, but research suggests that self-reported expertise is a relatively poor predictor of performance on the VET and other perceptual tasks (Dennett et al, 2012;McGugin, Richler, et al, 2012).…”

A Neurocognitive Approach to Expertise in Visual Object Recognition

“…This would explain why experimentally reducing the engagement level of the expert reduces the selective cortical activity underlying the visual expertise. Subsequent fMRI studies with car experts supported this conjecture, both in terms of the extent of activation, and its modulation by attentional engagement [24,40,41].…”

“…Visual experience with cars was found to predict neural activity not only in the functionally-defined FFA, but also medial fusiform gyrus, lingual gyrus, and precuneus [24], as well as in early visual cortex, parahippocampal gyrus, hippocampus, middle and superior temporal gyrus, intraparietal sulcus, inferior frontal gyrus, and cingulate gyrus [40]. Such extensive activation as reported in [40] clearly indicates that even when experts are engaged with a very simple visual task, a host of cognitive processes are at play, and many of them, are arguably non-visual. This further demonstrates how visual expertise is intrinsically linked to multiple top-down factors, which interact and modulate "pure" visual processing of the stimulus.…”

“…This further demonstrates how visual expertise is intrinsically linked to multiple top-down factors, which interact and modulate "pure" visual processing of the stimulus. Importantly, the attentional modulation by engagement was also addressed in [40]. Visual selective attention was manipulated by including conditions in which cars were always presented in a pair with another object.…”

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration

“…Among all object classes, because of their social importance, faces have been studied most extensively, especially since the fusiform face area (FFA) was discovered (Kanwisher, McDermott, & Chun, 1997;Sergent, Ohta, & MacDonald, 1992). Some research suggests that the FFA is a domainspecific "module" processing only faces (Grill-Spector, Knouf, & Kanwisher, 2004;Kanwisher et al, 1997;McCarthy, Puce, Gore, & Allison, 1997); however, the FFA responds to nonface object categories of expertise, including birds, cars (McGugin, Van Gulick, Tamber-Rosenau, Ross, & Gauthier, 2014;Xu, 2005;Gauthier, Skudlarski, Gore, & Anderson, 2000), chessboards (Bilalić, Langner, Ulrich, & Grodd, 2011), and even artificial objects when participants are sufficiently trained in the laboratory (Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999). High-resolution fMRI in the FFA and neurophysiology in macaque's brain reveal the existence of highly selective face areas within the FFA or its likely homologue in monkeys, but no reliable selectivity for nonface objects (Grill-Spector, Sayres, & Ress, 2006;Tsao, Freiwald, Tootell, & Livingstone, 2006).…”