APCV2014 Note

Winawer

fMRI spatial summation is sublinear and pRF nonlinearity has a compressive nonlinearity.

Second order contrast (Kay et al. 2013, PLOS Computational Biology): V1 front end -> second order contrast (RMS contrast) -> decompose to low and high 2nd order contrast component -> predicted response.  The same model fits fMRI and eCog data.

Kreigeskorte

Cichy et al (2014, Nature neuroscience) showed MEG representation dynamics in which the correlation between the MEG response to different types of objects emerges at different time points.

Current computational models for vision cannot explain IT. They are all less categorical than IT.  He proposed a forward multiple convolution model to explain it.

Ziemba

V2 model: Nature image=> V1 model<==> iterative synthesis with filtered noise -> V2 model (correlation across position, orientation an scale <==> iterative synthesis with white  noise.

The measured naturalness of random noise pattern from different texture family. They found that V2 response is affected by naturalness of the texture  (between 150-250ms after stimulus onset). fMRI also showed V2 and V3 response correlated strongly  with naturalness. There is no correlation in V1.  V2 also decode different texture types better.

  

Zaidi suggested that unique colors have no psychophysical color or physiological uniqueness.  Instead, it may related to language. For instance, if one ask an observer to identify a color that is half way between unique colors A and B and between C and D, and find another color that is half way between the two just picked. The match is not the same as picking and C and B and D first. That is (A+B) + (C + D) ~= (A+C) + (B+D). The law of distribution holds only if the observer is instructed to use opponent colors, rather than unique colors.   

Koida et al. demonstrated a glaze illusion in which a white disk appears to be self-illuminated when it is surround by a luminance gradient, but not when it is surrounded by uniform gray. See below

[I am wondering how the effect will hold if the disk and the surround are isoluminance colors].

Isherwood, Schira & Spehar showed that BOLD activation in V1 is a U-shape function of 1/f slope of the (noise) image spectrum. There is an activation in the medial frontal cortex that is correlated with the aesthetic preference to the slope.

Schira constructed a software that can simulate and model BOLD response from an image.

[Schira's model has a good performance to small stimuli. Hence, it may resolve some problems we encountered in the lab on BOLD activation for small stimulus difference]

Beer & Greenlee studied the network of multiple sensory network by combining probabilistic fiber tracing and functional MRI. They seeded a voxel by fMRI, traced fibers from that voxel, and observed the terminations of the fibers. The termination was compared with the fMRI activation and showed a high correlation between the two measurements.