Quantifying the Internal Structure of Categories Using a Neural Typicality Measure. Davis, Poldrack. Cerebral Cortex 2014


  1. Deals with the internal structure/representation of category information
  2. <Seems like assumption is there is something of an exemplar representation>
  3. “Internal structure refers to how the natural variability between-category members is coded so that we are able to determine which members are more typical or better examples of their category. Psychological categorization models offer tools for predicting internal structure and suggest that perceptions of typicality arise from similarities between the representations of category members in a psychological space.”
  4. Based on these models, develop a “neural typicality measure” that checks if a category member has a pattern of activation similar to other members of its group, as well as what is central to a neural space.
  5. Use an artificial categorization task, find a connection between stimulus and response
    1. “find that neural typicality in occipital and temporal regions is significantly correlated with subjects’ perceptions of typicality.”
  6. “The prefrontal cortex (PFC) is thought to represent behaviorally relevant aspects of categories such as
    rules associated with category membership (…). Motor and premotor regions may represent habitual responses associated with specific categories (…). The medial temporal lobe (MTL) and subregions of the striatum are thought to bind together aspects of category representations from these other systems.”
  7. Different areas and different neurons and patterns of activation in an area can “reliably discriminate
    between many real world object categories”
  8. Consider examples of category data as having some sort of “internal structure” or feature representation specific to that class.
    1. These features can say things like how typical a concrete example is, and is related to how quickly and accurately classification occurs
  9. “Depending on the specific model, a category representation may be a set of points associated with a given category (exemplar models; …), a summary statistic ( prototype models; …), or a set of statistics (clustering models; …) computed over points associated with a category.”
  10. Items closer to other examples in the class, or to the prototype are considered to be most typical or likely
  11. But they don’t propose that an accurate model is exactly the same thing a computer does, as there are examples of where nonintuitive things happen.
    1. Ex/ culture can influence how things are categorized, as can a current task or other context
  12. “Here, our goal is to develop a method for measuring the internal structure of neural category representations and test how it relates to physical and psychological measures of internal structure.”
  13. The neural typicality measure is related to nonparametric kernel density estimators, but “A key difference between our measure and related psychological and statistical models is that instead of using psychological or
    physical exemplar representations, our measure of neural typicality is computed over neural activation patterns…”
  14. Use a well studied research paradigm of categorizing simple bird illustrations into 4 categories based on neck angle and leg length.  Previous results show people reconstruct classes based on average item for each category
  15. “Our primary hypothesis is that psychological and neural measures of internal structure will be linked, without regard to where in the brain this might occur.”
    1. Also expect that some categorization will happen in visual cortex, and higher level temporal and medial-temporal regions, which “…. are theorized to bind together features from early visual regions into flexible conjunctive category representations (…).”
    2. There are other parts relevant to categorization, but not particularly this form of visual categorization, and other parts may be sensitive to things like entropy
  16. “To foreshadow the results, we find that neural typicality significantly correlates with subjects’ perceptions of typicality in early visual regions as well as regions of the temporal and medial temporal cortex. These results suggest that neural and psychological representational spaces are linked and validate the neural typicality measure as a useful tool for uncovering the aspects of category representations coded by specific brain regions.”
  17. “For analysis of behavioral responses, response time, and typicality ratings, a distance-to-the-bound variable was constructed that gave each stimulus’ overall distance from the boundaries that separate the categories in the stimulus space. Distance-to-the-bound is a useful measure of idealization: items that are distant from the bound are more idealized than items close to the bound (…).”
  18. “For the psychological typicality measure, a value for each of the Test Phase stimuli was generated by interpolating, on an individual subjects basis, a predicted typicality rating from the subjects’ observed typicality ratings…”
  19. Also did a physical typicality measure, which is pretty simple to understand (just neck angle, leg length measurements)
  20. Then a neural typicality <too much details to list here>
    1. “Our neural typicality measure is based on similarities between multivariate patterns of activation elicited for
      stimuli in the task. Stimuli that elicit activation patterns that are like other members of their category are more neurally typical than those that elicit dissimilar patterns of activation.”
  21. Subjects’ behavioral responses were predicted by SVM
  22. Typicality ratings were highly correlated with distance-to-the-bound
    1. Reveals that most typical items, and not the average item are the one that is used for category representation.  There are a few other results that show this is the case through other methodology
  23. Neural typicality is linked to psychological typicality
  24. Found activity in visual cortex and MTL that have been found to be linked to categorization
  25. “These results suggest that, in the present task, the internal structure of neural category representations in temporal and occipital regions are linked to subjects’ psychological category representations such that objects that are idealized or physical caricatures of their category elicit patterns of activation that are most (mathematically) similar to other members of their category.”
  26. “… in the present task, physical similarity is not a significant contributor to the internal structure of neural category representations, at least not at a level that is amenable to detection using fMRI.”
  27. Also did MDS for classification on the neural data, <results don’t seem amazing, but only ok>
  28. SVM for classification “The SVMs are given no information about the underlying stimulus space, and unlike
    the MDS analysis, do not make any assumptions about how the dimensions that separate the categories will be organized. Thus, the SVMs can be sensitive to regions that code rulebased or behavioral differences between categories, regions that encode information about their perceptual differences, or regions that code some combination of behavioral and perceptual information.”
  29. “Although there is strong overlap in the visual and MTL regions that discriminate between categories and represent
    internal structure, the motor/premotor, insula, and frontal regions were only identified in the between-category analysis. These results are consistent with the hypothesis that PFC and motor/premotor regions are more sensitive to behavioral aspects of categories (…). However, because behavioral responses are strongly associated with the perceptual characteristics of each category, the SVM results are also consistent with the hypothesis that these regions contain some perceptual information about the categories.”
  30. “The present research adds to the growing consensus that categorization depends on interactions between a number of
    different brain regions… An important point that this observation highlights is that there may not be any brain region that can be thought of representing all aspects of categories, and thus it might be most accurate to think of brain regions in terms of the aspects of category representations that they code.”
  31. “…in the present context, the deactivation of regions of the striatum with increasing typicality likely indicates an uncertainty signal, as opposed to category representation…”
  32. “Because our neural typicality measure is not based on mean activation-level differences between stimuli, it may be
    more directly interpretable and less susceptible to adjacency effects in studies of longer term internal category structure.”

    1. <Hm, should read their methodology more carefully on another read-through>
  33. They don’t have results that indicate suppression of adjacent stimulus
  34. Says their methodology should be tested in real-world, and more artificial settings
  35. Evidence of “dimensional selective attention” where not all features are attended to for classificaiton
    1. “Attentional mechanisms in the PFC that instantiate rule-based strategies (…) may contribute to selective attention effects by influencing neural representations in a top-down manner.”
    2. Although: “In the present context, dimensional selective attention is insufficient for explaining the idealization effect because dimensional selective attention affects an entire dimension uniformally… additional mechanisms are required.”
  36. “Attention has been found to create a spotlight around salient regions of visual space such that the processing of stimuli
    close to this location in space is enhanced (not just differences along a specific dimension of visual space; …). It is conceptually straightforward to predict that the same or similar spotlight mechanisms may affect the topography of stored neural stimulus representations, such that regions of a category space that contain highly idealized category members are enhanced and contribute more to categorization and typicality judgments than exemplars in ambiguous regions of category space.”
  37. Another model is one that specifically tries to “… to reduce prediction error and confusion between categories (…). In these models, category members are simultaneously pulled toward representations/members of their own categories and repelled by members of opposing categories.”
    1. But this doesn’t seem to be a possible explanation here because “… the neural effects as actual neuronal changes in regions of early visual cortex happen on a much longer scale than our task.”
  38. This study only tried to find correlation between “psychological” and “neurological” responses, but more in-depth exploration of their relationship is a good idea and left for future work
  39. “Our task involves learning to distinguish multiple categories, akin to A/B tasks, and so our finding that early visual cortex is involved with representing category structure may be at odds with theories emphasizing the role of task demands (as opposed to featural qualities) in determining which perceptual regions will be recruited to represent categories.”
    1. Although these distinctions may be an artifact of the type of analysis used
Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: