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1.  What do hands know about hills? Interpreting Taylor-Covill and Eves (2013) in context☆☆ 
Acta psychologica  2013;144(2):10.1016/j.actpsy.2013.07.014.
Hills appear much steeper than they are. Although near surface slant is also exaggerated, near surfaces appear much shallower than equivalently slanted hills. Taylor-Covill and Eves (2013) propose a new type of palm orientation measuring device that provides outputs that accurately reflect the physical slants of stairs and hills from 19 to 30° and also seems to accurately reflect the slants of near surfaces (25–30°). They question the validity of the observations of Durgin, Hajnal, Li, Tonge & Stigliani (2010), who observed that palm boards grossly underestimated near surfaces. Here I review our recent work on the visual and haptic perception of near surface orientation in order to place Taylor-Covill and Eves' arguments in context. I note in particular that free hand measures of real surfaces in near space show excellent calibration, but free hand measures show gross exaggeration for hills. This leads to the question of the grounds for preferring a mechanical device to a freely wielded hand. In addition I report an investigative replication of the crucial observations that led to our concerns about the value of palm boards as measures of perception and note the specific methodological details that we have accounted for in our procedures. Finally, I propose some testable hypotheses regarding how better-than-expected haptic matches to hills may arise.
PMCID: PMC3870886  PMID: 23938050
Haptic perception; Slant; Space perception; Measurement; Orientation perception
2.  Task effects, performance levels, features, configurations, and holistic face processing: A reply to Rossion 
Acta psychologica  2009;132(3):286-292.
A recent article in Acta Psychologica (“Picture-plane inversion leads to qualitative changes of face perception” by B. Rossion, 2008) criticized several aspects of an earlier paper of ours (Riesenhuber et al., “Face processing in humans is compatible with a simple shape-based model of vision”, Proc Biol Sci, 2004). We here address Rossion’s criticisms and correct some misunderstandings. To frame the discussion, we first review our previously presented computational model of face recognition in cortex (Jiang et al., “Evaluation of a shape-based model of human face discrimination using fMRI and behavioral techniques”, Neuron, 2006) that provides a concrete biologically plausible computational substrate for holistic coding, namely a neural representation learned for upright faces, in the spirit of the original simple-to-complex hierarchical model of vision by Hubel and Wiesel. We show that Rossion’s and others’ data support the model, and that there is actually a convergence of views on the mechanisms underlying face recognition, in particular regarding holistic processing.
PMCID: PMC2788156  PMID: 19665104

Results 1-2 (2)