Visual Expertise Depends on How You Slice the Space
Visual Expertise Depends on How You Slice the Space Brian A. Tran (b3tran@ucsd.edu) UCSD Department of Computer Science and Engineering, 9500 Gilman Drive La Jolla, CA 92093-0114 USA Carrie A. Joyce (cjoyce@cs.ucsd.edu) UCSD Department of Computer Science and Engineering, 9500 Gilman Drive La Jolla, CA 92093-0114 USA Garrison W. Cottrell (gary@cs.ucsd.edu) UCSD Department of Computer Science and Engineering, 9500 Gilman Drive La Jolla, CA 92093-0114 USA Abstract Previous studies using fMRI have found that the Fusiform Face Area (FFA) responds selectively to face stimuli. More recently however, studies have shown that FFA activation is not face-specific, but can also occur for other objects if the level of experience with the objects is controlled. Our neurocomputational models of visual expertise suggest that the FFA may perform fine-level discrimination by amplifying small differences in visually homogeneous categories. This is reflected in a large spread of the stimuli in the high- dimensional representational space. This view of the FFA as a general, fine-level discriminator has been disputed on a number of counts. It has been argued that the objects used in human and network expertise studies (e.g. cars, birds, Greebles) are too “face-like” to conclude that the FFA is a general-purpose processor. Further, in our previous models, novice networks had fewer output possibilities than expert networks, leaving open the possibility that learning more discriminations, rather than learning fine-level discriminations, may be responsible for the results. To challenge these criticisms, we trained networks to perform fine-level discrimination on fonts, an obviously non-face category, and showed that these font networks learn a new task faster than networks trained to identify letters. In addition, all networks had the same number of output options, illustrating that visual expertise does not rely on number of discriminations, but rather on how the representational space is partitioned. Introduction The Fusiform Face Area (FFA) in the ventral temporal lobe has recently received much attention. Initial work appeared to show that this area was selective for processing faces. Several fMRI studies showed high activation in the FFA only to face stimuli and not other objects (Kanwisher et al., 1997; Kanwisher, 2000). Further, studies involving patients with associative prosopagnosia, the inability to identify individual faces (Farah et al., 1995), and visual object agnosia, the inability to recognize non-face objects (Moscovitch et al., 1997), seemed to indicate a clear double dissociation between face and object processing. Prosopagnosic patients had lesions that encompassed either right hemisphere or bilateral FFA, while object agnosic patients’ lesions did not (De Renzi et al., 1994). Gauthier and colleagues have challenged the notion of the face specificity of the FFA by pointing out that the earlier studies failed to equate the level of experience subjects had with non-face objects, to the level of experience they had with faces (Gauthier et al., 1997; Gauthier et al., 1999a; Gauthier et al., 1999b). She showed that the FFA was activated when bird and dog experts were shown pictures of the animals in their area of expertise. Further, she illustrated that, if properly trained, individuals can develop expertise on novel, non-face objects (e.g. Greebles), and subsequently show increased FFA activation to them (Gauthier et al., 1999a). Expertise in these studies was operationally defined as the point in training when a subject’s default response level (i.e. entry level) “shifts” from basic to the individual level. This is indexed by the subject’s reaction time for verifying individual names becoming as fast as the time to verify category membership. Neurocomputational models done first by Sugimoto and Cottrell (2001) and later extended by Joyce and Cottrell (2004) began to address the question of how and why the FFA gets recruited for these other tasks (Sugimoto & Cottrell, 2001, Joyce & Cottrell, 2004). Using four different types of stimulus classes (books, cans, cups and faces), Sugimoto and Cottrell found that the amount of expert-level experience on a previous task correlates with faster subordinate level learning relative to a system that processes the same stimuli, but not to a subordinate level. Thus, an area that is used for one expertise task will learn a second expertise task faster than an area used only for basic level discriminations. Joyce and Cottrell (2004) further found that an expert network’s ability to separate individuals is reflected in highly variable responses at the representational layer (the hidden layer). This response variability extended to novel categories, permitting faster learning of these