Categorical Clustering of the Neural Representation of Color. Brouwer, Heeger. JNeuro 2013.


  1. fMRI study where subjects viewed 12 colors did either a color-naming or distractor task
  2. “A forward model was used to extract lower dimensional neural color spaces from the high-dimensional fMRI responses.”
  3. Vision areas of V4 and V01 showed clustering for color naming task but not for distractor
  4. “Response amplitudes and signal-to-noise ratios were higher in most visual cortical areas for color naming compared to diverted attention. But only in V4v and VO1 did the cortical representation
    of color change to a categorical color space”
  5. We can perceive thousands of colors but have only a handful of descriptive categories for colors, so we can see two different colors but would still potentially call it the same thing
  6. Inferotemporal cortex (IT) is believed to deal with categorization of color
  7. “…performing a categorization task alters the responses of individual color-selective neurons in macaque IT (…).”
  8. Similar colors cause overlapping patterns of neural activity, “… neural representations of color can be characterized by low-dimensional ‘neural color spaces’…”
  9. “Activity in visual cortex depends on task demands (…).”
    1. Use fMRI to study this
  10. “Forward model” is used to reduce fMRI signals to a lower dimensional space of bases
  11. ” Normal color vision was verified by use of the Ishihara plates (Ishihara, 1917) and a computerized version of the Farnsworth–Munsell 100 hue scoring test (Farnsworth, 1957).”
    1. <Need to learn about this>
  12. “The 12 stimulus colors were defined in DKL (Derrington, Krauskopf and Lennie) color space… We chose the DKL space because it represents a logical starting point to investigate the neural representation of color in visual cortex. Although there is evidence for additional higher-order color mechanisms in visual cortex (Krauskopf et al., 1986), the color tuning of neurons in V1 can be approximated by linear weighted sums of the two chromatic axes of DKL color space
    (Lennie et al., 1990).”
  13. Color categorization task was done outside the scanner, involved putting 64 colors into one of 5 categories
  14. When in the fMRI, there were two types of episodes.  In one, subjects had to press one of 5 buttons to categorize the color (R,G,B,Y, or purple).  Distractor task was a 2-back test (is color the same as the color 2 steps ago)
  15. <details on fMRI processing>
  16. Used the forward model from this paper.
  17. ” We characterized the color selectivity of each neuron as a weighted sum of six hypothetical
    channels, each with an idealized color tuning curve (or basis function) such that the transformation from stimulus color to channel outputs was one to one and invertible. Each basis function was a half-wave-rectified and squared sinusoid in DKL color space.”
  18. Assume voxel response is proportional to the number of responding neurons in that voxel
  19. Channel responses C (an n x c matrix where n was number of colors, and c # channels(6)).  Then did PCA on this to “extract neural color spaces from the high-dimensional space of voxel responses(…)”
  20. “According to the model, each color produces a unique pattern of responses in the channels, represented by a point in the six-dimensional channel space.  By fitting voxel responses to the forward model, we projected the voxel responses into this six dimensional subspace.”
    1. PCA <A competing method they previously used to do this analysis> did not work as well – had similar results but more variability because it tries to fit noise where the forward model throws it out
  21. To visualize the forward model, they ran PCA to project the 6D space to 2D (these 2 dimensions accounted for almost all the variance)
  22. “Reanalysis of the current data using PCA to reduce dimensionality directly from the number of voxels to two also yielded two-dimensional neural color spaces that were similar to those published previously. Specifically, the neural color spaces from areas V4v and VO1 were close to circular, whereas the neural color spaces of the remaining areas (including V1) were not circular, replicating our previously published results and supporting the previously published conclusions (Brouwer and Heeger, 2009).”
  23. Used many different clustering methods to see if colors labeled in the same color category had a more similar response than those in other categories
  24. On to results
  25. Subjects were pretty consistent where they put color class boundaries.  Blue and green were the most stable
  26. Subjects weren’t told category labels–basically that they were doing clustering–but still categories were intuitively identifiable and pretty stable
  27. Color clustering was strongest in V01 and V4v, during the color-naming task.  Responses from neighboring area V3 were more smoothly circular and therefore not as good at clustering.
  28. Screen Shot 2015-01-06 at 12.38.08 PM
  29. “The categorical clustering indices were significantly larger for color naming than diverted attention in all but one (V2) visual area (p 0.001, nonparametric randomization test), but the
    difference between color naming and diverted attention was significantly greater in VO1 relative to the other visual areas (p 0.01, nonparametric randomization test). One possibility is that all visual areas exhibited clustering of within-category colors, but that the categorical clustering indices were low in visual areas with fewer color-selective neurons, i.e., due to a lack of statistical power”
  30. “… no visual area exhibited categorical clustering significantly greater than baseline for the diverted attention task.”
  31. Manual clustering done by subjects matched that done from the neural data, aside from the fact that neurall turqoise/cyan matched with blues, whereas people matched it with greens
  32. “Hierarchical clustering in areas V4v and VO1 resembled the perceptual hierarchy of color categories”
    1. In V01 when doing color naming.
    2. The dendogram resulting from the distractor task looks pretty much like garbage
  33. <Shame on the editor.  Use SNR without defining the abbreviation – I assume its signal to noise ratio?>
  34. “Decoding accuracies from the current data set were similar; forward-model decoding
    and maximum-likelihood decoding and were nearly indistinguishable.”
  35. <Between this and the similarity of the result of PCA, what does their forward model buy you?  Is it good because it matches results and is *less* general?>
  36. “… we propose that some visual areas (e.g., V4v and VO1) implement an additional color-specific change in gain, such that the gain of each neuron changes as a function of its selectivity relative to the centers of the color categories (Fig. 8C). Specifically, neurons tuned to a color near the center of a color category are subjected to larger gain increases than neurons tuned to intermediate colors”
    1. <It is only shown that doing this in simulation helps clustering, which is in the neural data, but they don’t show that the neural data specifically supports this over other approaches>
  37. “Task-dependent modulations of activity are readily observed throughout visual cortex, associated with spatial attention, feature-based attention, perceptual decision making, and task structure (Kastner and Ungerleider, 2000; Treue, 2001; Corbetta and Shulman, 2002; Reynolds and Chelazzi, 2004; Jack et al., 2006; Maunsell and Treue, 2006; Reynolds and Heeger, 2009). These task-dependent modulations have been characterized as shifting baseline responses, amplifying gain and increasing SNR of stimulus-evoked responses, and/or narrowing tuning widths. The focus in the current study, however, was to characterize task-dependent changes in distributed neural representations, i.e., the joint encoding of a stimulus by activity in populations of neurons.”
  38. <Need to read all references in section “Categorical specificity of areas V4v and VO1”>
  39. Lots of results that show V4 and nearby areas respond to chromatic stimuli.  They have a previous paper (their one from 2009) that V4v and V01 better match perceptual experience of color than other regions, but there aren’t many results dealing with “… the neural representation of color categories, the representation of the unique hues, or the effect of task demands on these representations”
  40. Previous EEG studies show that the differences in EEG when looking at one color and then another “…  appear to be lateralized, providing support for the influence of language on color  categorization, the principle of linguistic relativity, or Whorfianism (Hill and Mannheim, 1992; Liu et al., 2009; Mo et al., 2011). Indeed, language-specific terminology influences preattentive color perception. The existence in Greek of two additional color terms, distinguishing light and dark blue, leads to faster perceptual discrimination of these colors and an increased visual mismatch negativity of the visually evoked potential in native speakers of Greek, compared to native speakers of English (Thierry et al., 2009).”
    1. Here however, no evidence of lateralized categorical clustering from fMRI
  41. Neural research on Macaques and color, but there are differences in brain structure and sensitivities in photoreceptors between them and us so we need to keep that in mind when examining the results from animal experiments on color
  42. “We proposed a model that explains the clustering of the neural color spaces from V4v and VO1, as well as the changes in response amplitudes (gain) and SNR observed in all visual areas. In this model, the categorical clustering observed in V4v and VO1 is attributed to a color-specific gain change, such that the gain of each neuron changes as a function of its selectivity relative to the centers of the color categories.”
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