A Computational Model of the Development of Hemispheric Asymmetry of Face Processing

A Computational Model of the Development of Hemispheric Asymmetry of Face Processing Panqu Wang (pawang@ucsd.edu) Department of Electrical and Computer Engineering, University of California San Diego 9500 Gilman Dr 0407, La Jolla, CA 92093 USA Garrison Cottrell (gary@ucsd.edu) Department of Computer Science and Engineering, University of California San Diego 9500 Gilman Dr 0404, La Jolla, CA 92093 USA Abstract Extensive research effort has been invested in building neuro- computational models for face and object recognition. How- ever, the relationship between the recognition model and the development of the visual system is rarely considered. Re- search on the development of contrast sensitivity shows that human infants can only perceive low spatial frequency infor- mation from visual stimuli, but their acuity improves gradu- ally with age. Also, the right hemisphere (RH) develops earlier than the left hemisphere (LH), and is dominant in infants. Here we show that these constraints, coupled with a desire on the part of the infant to individuate its caretakers and family, leads naturally to the right hemisphere bias for face processing. We propose a developmental model for face and object recognition using a modular neural network based on Dailey and Cottrell (1999). This neural network represents the two hemispheres using two modules, with a competitive relationship between them mediated by a gating mechanism. The strong RH and low spatial frequency bias for face recognition emerges natu- rally in the model from the interaction of the slow development of acuity and the early dominance of the right hemisphere. Re- markably, this strong asymmetry does not appear to hold for the other object categories that we tried. Keywords: face recognition; developmental model; contrast sensitivity; modular neural network. Introduction Computational models have been used extensively to instan- tiate hypotheses regarding face and object perception from a neurocomputational perspective (Dailey & Cottrell, 1999; O’Reilly & Munakata, 2000; Riesenhuber & Poggio, 1999). Due to their social importance and frequency, faces as a stim- ulus class have been studied most extensively. fMRI studies have shown that the fusiform face area (FFA) is activated in response to faces more than other stimuli, and this activation is found to be stronger in the right hemisphere (Kanwisher, McDermott, & Chun, 1997). This right hemisphere lateral- ization is supported by electrophysiological studies, which show a stronger face-specific wave 170 ms after the stimu- lus onset over the right hemisphere (Bentin, Allison, Puce, Perez, & McCarthy, 1996). In addition, according to neu- ropsychological data, the lesioning of the right FFA will re- sult in prosopagnosia, a deficit in face recognition (Bouvier & Engel, 2006). To account for such hemispheric asymme- try, Ivry and Robertson (1998) proposed the Double Filter- ing by Frequency (DFF) model which postulates differential frequency filtering on task-relevant frequency bands in each hemisphere, with a bias for low spatial frequency in the RH. Hsiao, Shieh, and Cottrell (2008) implemented this DFF the- ory using a computational hemispheric model, and showed that the model could account for the left-side bias in face recognition. All neurocomputational models so far investigate the ef- fects of structural constraints, such as the visual field split, the interaction between the two hemispheres, and the localized nature of the FFA. Temporal constraints, such as the develop- ment of the brain through childhood, are rarely considered in these models. By including consideration of the developmen- tal changes, we may generate new hypotheses concerning the lateralization of face processing. We consider two main constraints. First, studies in hu- man infants have shown that the right hemisphere develops its function earlier than the left hemisphere (Chiron et al., 1997). By measuring the regional cerebral blood flow (rCBF) changes at rest using single photon emission computed to- mography (SPECT), Chiron et al. observed a right hemi- sphere predominance of the blood flow between 1 and 3 years of age, with the asymmetry shifting to the left hemisphere af- ter 3 years. The second constraint has to do with the development of visual acuity. Studies have shown that the contrast sensitivity function (CSF), a measure of the ability to distinguish be- tween different levels of luminance in static images, changes radically over time. The CSF of a 3-month-old infant is over 1.0 log units lower than the adult (Peterzell, Werner, & Ka- plan, 1995). By age 4, children’s contrast sensitivity is still reduced by about 0.5 log units when compared to that of adults (Atkinson, Braddick, & Moar, 1995; Gwiazda, Bauer, Thorn, & Held, 1997). The studies regarding the time of con- trast sensitivity maturity have obtained somewhat disparate results. They range from claims of maturity between 6 to 10 years of age (Bradley & Freeman, 1982; Mayer, 1977), to evidence of immaturity in 8-15 year old children (Arundale, 1978). More recent studies suggest that contrast sensitivity attains adult levels in all frequencies by age 7 (Ellemberg, Lewis, Liu, & Maurer, 1999), or 8 (Leat, Yadav, & Irving, Combining these two constraints, we propose a neurocom- putational developmental model for object and face recog- nition. The model is based on a previous study of the de- velopment of hemispheric asymmetry by Dailey and Cottrell