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.
