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. 2008 Feb;48(5):703-15.
doi: 10.1016/j.visres.2007.11.025. Epub 2008 Jan 28.

The roles of visual expertise and visual input in the face inversion effect: behavioral and neurocomputational evidence

Joseph P McCleery  1 , Lingyun Zhang, Liezhong Ge, Zhe Wang, Eric M Christiansen, Kang Lee, Garrison W Cottrell
Affiliations

The roles of visual expertise and visual input in the face inversion effect: behavioral and neurocomputational evidence

Joseph P McCleery et al. Vision Res. 2008 Feb.

Abstract

Research has shown that inverting faces significantly disrupts the processing of configural information, leading to a face inversion effect. We recently used a contextual priming technique to show that the presence or absence of the face inversion effect can be determined via the top-down activation of face versus non-face processing systems [Ge, L., Wang, Z., McCleery, J., & Lee, K. (2006). Activation of face expertise and the inversion effect. Psychological Science, 17(1), 12-16]. In the current study, we replicate these findings using the same technique but under different conditions. We then extend these findings through the application of a neural network model of face and Chinese character expertise systems. Results provide support for the hypothesis that a specialized face expertise system develops through extensive training of the visual system with upright faces, and that top-down mechanisms are capable of influencing when this face expertise system is engaged.

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Figures

Figure 1
Figure 1
Design of the Face and Character Conditions in Experiments 1a and 1b.
Figure 2
Figure 2
Mean Correct Response Latencies and standard errors for the Ambiguous Figures in the Face and Character Conditions of Experiment 1a (*: p<.05).
Figure 3
Figure 3
Mean accuracy (%) and standard errors of the mean for the Ambiguous Figures in Experiment 1b (*: p<.05).
Figure 4
Figure 4
Design of the Character Condition in Experiment 2.
Figure 5
Figure 5
Visual representation of the structure of the neurocomputational model.
Figure 6
Figure 6
Example training images (left) and test images (right) used in the neurocomputational modeling experiment.
Figure 7
Figure 7
Mean discriminability and standard errors of the mean for the Ambiguous igures in the Face and Character Conditions of neurocomputational modeling xperiment (*: p<.05)
Figure 8
Figure 8
Discriminability of upright and inverted Ambiguous Figures stimuli at the different stages of the neurocomputational model in the face and character conditions.
See this image and copyright information in PMC

References

    1. Bishop CM. Neural networks for pattern recognition. Oxford University Press; 1995.
    1. Bukach CM, Gauthier I, Tarr MJ. Beyond faces and modularity: The power of an expertise framework. Trends in Cognitive Science. 2006;10(4):159–166. - PubMed
    1. Chen Y, Fu S, Iversen SD, Smith SM, Matthews PM. Testing for dual brain processing routes in reading: a direct contrast of Chinese character and Pinyin reading using fMRI. Journal of Cognitive Neuroscience. 2002;14:1088–1098. - PubMed
    1. Cottrell GW, Branson KM, Calder AJ. Proc 24th Ann Cog Sci Soc Conf. Mahwah, New Jersey: Cognitive Science Society; 2002. Do expression and identity need separate representations?
    1. Cottrell GW, Munro P, Zipser D. Learning internal representations from gray-scale images: An example of extensional programming. Proceedings of the Ninth Annual Cognitive Science Society Conference; Seattle, WA. 1987. pp. 461–473.

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