“ we have often seen claims to have decoded the mind which are essentially based on identifying simple correspondences between scan results and an item of mental activity. The subject is shown a picture of, say, John Malkovich and scan results obtained: then from scan results the researchers succeed in telling with statistically significant rates of success the occasions when the subject is looking at the same picture of John Malkovich and not one of John Cusack. Voila! The secret language of the brain is cracked! It is not shown that similar scan patterns can be obtained from other subjects looking at the picture of John Malkovich, or from the same subject looking at other pictures of John Malkovich, or thinking about John Malkovich, or even from the same subject looking at the same picture the next day. No general encoding of mental activity is revealed, no general encoding of visual activity, in fact we don’t even get for sure a general encoding of that particular picture of John Malkovich in that particular subject on that particular day. The only truth securely revealed is that if you have an experience and then soon afterwards another one just like it, you probably use quite a few of the same neurons in responding to it. ”
http://www.consciousentities.com/?p=1073
20120201 @ 1249
February 1st
December 7th
I just hate the term “apparent motion,” whose existence in the jargon of its field does nothing but force me to write awkward doubletalk to avoid that collocation. As t turns out I am very concerned with the sensation of the appearance of visual motion, which ought to be called apparent motion — the original meaning of the phrase — to distinguish the sensation from the stimulus that might elicit it. Instead, “apparent motion” has ossified into a jargon, taken to refer to stimuli composed of discrete momentary images, lacking a continuous physical translation of the light-generating object. It is a phrase named after a sensation but understood as a type of stimulus. This is bonkers.
December 2nd
There’s some peripheral drift illusion going on with this color scheme, but not in a coherent direction. Can we recolor or rearrange to make it stronger?
Goodman-Strauss 7-fold rhomb
“ A criticism of both the above dichotomies [i.e., short range vs. long range motion, and first vs. second order motion] is that they reflect differences in the choice of stimuli, rather than qualitative differences in the underlying motion detection mechanisms. For example, much of the evidence for the existence of first-order and second-order mechanisms comes from comparing results from two types of stimuli: first, stimuli where the motion signal is carried by a spatio-temporal luminance correlation, and second, stimuli where motion is perceived in the absence of a luminance correlation, such as motion in a dynamic kinematogram or an amplitude modulated grating. Similarly, there is a wealth of information and data thought to characterize the short-range process, where the stimuli are spatially dense and movement is across small spatial displacements, whereas the long-range process has been investigated by using movement of figural stimuli across large displacements. ”
J. C. Boulton and C. L. Baker, Jr. Different parameters control motion perception above and below a critical density. Vision Res, 33(13):1803–11, Sep 1993. Pubmed
20111202 @ 0100
November 18th
“ Crowding has usually been characterized by just one
number, “critical spacing”, i.e., spacing threshold, the spacing
required to achieve a criterion level of performance. That
single number seems to be enough to characterize crowding
when the flanker is similar to the target, but may not
adequately describe the weaker crowding produced by
dissimilar flankers. Disentangling the amplitude and extent of
crowding demands a two-number description. The complete
‘psychometric function’, plotting proportion correct as a
function of spacing, tells us little more than the critical
spacing. Proportion correct has a small dynamic range
bounded by the floor at chance, when spacing is below
critical, and by the ceiling at 100%, when spacing is above
critical. To get the whole story, we must replace proportion
correct by a better dependent measure: threshold. To measure
threshold, one varies a physical parameter of the stimulus to
achieve a particular level of performance. Thus, threshold is
measured on a physical scale with a wide dynamic range. For
example, several studies have measured orientation
discrimination thresholds as a function of spacing. These plots
show that the weaker crowding produced by less-similar
flankers has much less amplitude (maximum threshold
elevation) but practically the same spatial extent. ” Pelli D and Tillman K (2008) The uncrowded window of object recognition. Nature Neuroscience 11(10):1129-1135
20111118 @ 1359
number, “critical spacing”, i.e., spacing threshold, the spacing
required to achieve a criterion level of performance. That
single number seems to be enough to characterize crowding
when the flanker is similar to the target, but may not
adequately describe the weaker crowding produced by
dissimilar flankers. Disentangling the amplitude and extent of
crowding demands a two-number description. The complete
‘psychometric function’, plotting proportion correct as a
function of spacing, tells us little more than the critical
spacing. Proportion correct has a small dynamic range
bounded by the floor at chance, when spacing is below
critical, and by the ceiling at 100%, when spacing is above
critical. To get the whole story, we must replace proportion
correct by a better dependent measure: threshold. To measure
threshold, one varies a physical parameter of the stimulus to
achieve a particular level of performance. Thus, threshold is
measured on a physical scale with a wide dynamic range. For
example, several studies have measured orientation
discrimination thresholds as a function of spacing. These plots
show that the weaker crowding produced by less-similar
flankers has much less amplitude (maximum threshold
elevation) but practically the same spatial extent. ” Pelli D and Tillman K (2008) The uncrowded window of object recognition. Nature Neuroscience 11(10):1129-1135
July 22nd
May 11th
May 7th
![T. Lennert and J. Martinez-Trujillo. Strength of response suppression to distracter stimuli determines
attentional-filtering performance in primate prefrontal neurons. Neuron, 70(1):141–52, Apr 2011. [pubmed]
[for less ranty, try here.]
Dear everyone who uses colormaps: The ‘jet’ colormap, the default colormap in MATLAB, is a perceptual disaster. STOP USING THAT SHIT. I mean, just what is happening in the lower right sector of these plots?
Okay, more background. The scale is a raster of a modulation index (area under ROC, to be particular) for a group of neurons, plotted over the time from stimulus onset. The big salient feature of each plot is a yellow band. You see the yellow band because each raster plot sorts its neurons according to the “latency,” that is, the earliest time that the modulation passed some arbitrary threshold. Because that arbitrary threshold coincides with yellow, the highest-luminance value on the colormap, effectively the authors have chosen a computational and graphical procedure that says “Hey, sort my data that it makes a nice yellow stripe down the middle, NO MATTER WHAT THE DATA ACTUALLY CONTAINS.”
And what is happening to the right of that stripe? Well, because your spatial resolution for chroma differences (and especially for blue) is much worse than that for luminance differences (which is why sane colormaps, that are not “jet”, always have a monotonic luminance component), you have to get your nose right up to the screen to decide that under the stripe is a pretty good mixup of blue and red and yellow — all over the scale. In other words, some of these cells are well modulated past where they pass the arbitrary threshold, but a lot of cells stop being modulated at all after they dinged the threshold. Which is conveniently difficult to discern due to the colormap — and kind of raises the question of how reasonable that threshold setting is, or the cell inclusion criteria are.
We’ll just note in passing that the sorting is done separately for EACH subplot, so that row-by-row comparisons of the cells can’t be done, and the “neuron number” scale on the left is pretty much meaningless. Which also, by the way, fully undermines the claim that modulation for larger ordinal differences(*) happens faster.
I hope you noticed after you got close up to the screen, that the dark red and dark blue values are a lot harder to distinguish than, say, the yellow-to-cyan colors that makes up the middle of the scale. You know, we should be easily able to see when things are at opposite ends of the scale, right? I mean, if we’re plotting our data, I mean.
What kind of insane colormap has the property that values spanning the extreme ends of the scale stand out less, and can’t be distinguished as easily as values in the noisy middle? Why, it’s MATLAB’s default colormap, of course!
You know, ggplot uses a much better preset for its color maps, as well as better alternatives. Just sayin’.
(*) You want to know the ironic punchline? This catastrophe is figure 4 of a paper ABOUT NEURAL PROCESSING OF ORDINAL COLOR SCALES. Seriously!
I’m not sure whether this belongs on the ranting-about-neuroscience blog, or the ranting-about-MATLAB blog. so it goes on both places.](http://25.media.tumblr.com/tumblr_lkuifzATgo1qzg2k6o1_r2_1280.png)
T. Lennert and J. Martinez-Trujillo. Strength of response suppression to distracter stimuli determines
attentional-filtering performance in primate prefrontal neurons. Neuron, 70(1):141–52, Apr 2011. [pubmed]
[for less ranty, try here.]
Dear everyone who uses colormaps: The ‘jet’ colormap, the default colormap in MATLAB, is a perceptual disaster. STOP USING THAT SHIT. I mean, just what is happening in the lower right sector of these plots?
Okay, more background. The scale is a raster of a modulation index (area under ROC, to be particular) for a group of neurons, plotted over the time from stimulus onset. The big salient feature of each plot is a yellow band. You see the yellow band because each raster plot sorts its neurons according to the “latency,” that is, the earliest time that the modulation passed some arbitrary threshold. Because that arbitrary threshold coincides with yellow, the highest-luminance value on the colormap, effectively the authors have chosen a computational and graphical procedure that says “Hey, sort my data that it makes a nice yellow stripe down the middle, NO MATTER WHAT THE DATA ACTUALLY CONTAINS.”
And what is happening to the right of that stripe? Well, because your spatial resolution for chroma differences (and especially for blue) is much worse than that for luminance differences (which is why sane colormaps, that are not “jet”, always have a monotonic luminance component), you have to get your nose right up to the screen to decide that under the stripe is a pretty good mixup of blue and red and yellow — all over the scale. In other words, some of these cells are well modulated past where they pass the arbitrary threshold, but a lot of cells stop being modulated at all after they dinged the threshold. Which is conveniently difficult to discern due to the colormap — and kind of raises the question of how reasonable that threshold setting is, or the cell inclusion criteria are.
We’ll just note in passing that the sorting is done separately for EACH subplot, so that row-by-row comparisons of the cells can’t be done, and the “neuron number” scale on the left is pretty much meaningless. Which also, by the way, fully undermines the claim that modulation for larger ordinal differences(*) happens faster.
I hope you noticed after you got close up to the screen, that the dark red and dark blue values are a lot harder to distinguish than, say, the yellow-to-cyan colors that makes up the middle of the scale. You know, we should be easily able to see when things are at opposite ends of the scale, right? I mean, if we’re plotting our data, I mean.
What kind of insane colormap has the property that values spanning the extreme ends of the scale stand out less, and can’t be distinguished as easily as values in the noisy middle? Why, it’s MATLAB’s default colormap, of course!
You know, ggplot uses a much better preset for its color maps, as well as better alternatives. Just sayin’.
(*) You want to know the ironic punchline? This catastrophe is figure 4 of a paper ABOUT NEURAL PROCESSING OF ORDINAL COLOR SCALES. Seriously!
I’m not sure whether this belongs on the ranting-about-neuroscience blog, or the ranting-about-MATLAB blog. so it goes on both places.
May 6th
March 21st
“ Turing’s contribution to this discussion was to advocate the use of gin, which he said contained alcohol and water in just the right proportions to give a zero temperature coefficient of propagation velocity at room temperature. ”
Wilkes, Maurice V. Computers Then and Now. 1967 ACM Turing lecture. Journal of the Association for Computing Machinery, Vol. 18, No. 1, January 1968 [acm.org]
20110321 @ 1616
