Functional architecture

Associations of pairs of base-level concepts were analyzed by spatiotemporal correlations. Base-level concepts belonging to the same superordinate category showed a higher degree of association than concepts that do not belong to identical categories. Previous neuroimaging and

electrophysiological studies have shown dissociations between superordinate categories, and this study is the first showing direct association between base-level concepts within a superordinate category. These associations (as indexed by correlations computed for different regions of analysis) were highest in posterior regions and along the ventral visual stream, whereas the latter one was more pronounced over the left hemisphere. There is considerable evidence that the occipito-temporal cortex responds to meaningful visual stimuli and that regions within the occipito-temporal cortex have been shown to be activated differentially by different objects (Ishai et al. 2000). If semantic

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A.4.3 : Discussion - Functional architecture

categories are represented on a cortical level in distinct regions, then members belonging to the same superordinate category should share cortical resources and which may result in higher within-category correlations. However, given the almost infinite number of objects, it is rather unlikely that objects are represented in separable cortical regions. Moreover, categories do not always have sharp definitions (Armstrong et al., 1983), making it difficult to explain how categories could be

organized in separable cortical modules. In that sense, the higher correlations within categories are hard to explain by assuming separable brain regions for each of the superordinate categories used in this study.

Malach and colleagues (2002; Levy et al., 2001) have suggested an alternative model of temporal cortical organization. According to their view, the center-periphery organization found in the early visual areas of primates and humans extends into higher order visual areas. They suggested that this principle also applies to the cortical representation of objects. For example, as the recognition of faces vs. buildings depends on different ‘cues’ within the visual field (central vs. peripheral), this center-periphery bias is also reflected in the organization of higher visual areas. In this study, all stimuli were presented at fixation and it can be assumed that all categories have activated central and peripheral retinal cells to a similar degree. Therefore, the ‘center-periphery’ model can not account for the findings in this study.

Another view claims that the human extrastriate cortex contains areas which are specialized in different types of processing. Processing of a stimulus on an individual level (expert knowledge acquired by experience), as in the case for faces, or processing of spatial information (contained in pictures of houses) can appear as ‘category-specific’ activation in functional imaging studies.

However it is unlikely that any of the subjects in this study were experts in processing of any of the superordinate categories. This makes it unlikely that expertise in any category contributed to the finding of the higher association measures of concepts belonging to the same category.

Nevertheless, it can not be ruled out that the categorization of a specific category might have

A.4.3 : Discussion - Functional architecture

required a particular cognitive or perceptual process, which could result in higher correlations between these concepts. For example, animals might share common visual features (e.g., having fur) and the processing of all animals might require activation of brain regions specialized in visual discrimination. Indeed, this view is consistent with another model suggesting that objects are represented in a distributed neural network including several cortical areas that store information about different object features and attributes, including color, form, and motion (Martin et al., 2000). Depending on the category different parts of this distributed network are activated. Haxby et al. (2000) have pointed out that the exact nature of these attributes is not yet completely understood.

However, as the ventral extrastriate visual cortex is activated strongly, it is likely that these attributes are at least partially visual. Basic level concepts of a specific category share more

attributes and features with each other than do concepts from different superordinate categories. For example, in this study all animals had in common the ability to move, are covered in fur, have four legs and typically live in the forest. On the other hand, all pieces of furniture consisted of rather straight lines, sharp edges, usually can be found within houses etc. The combined activity of the neural representation of these features leads to an object-specific neural code. The neuromagnetic responses and the associated spatio-temporal correlations between objects is most likely a

manifestation of the activation of these object-specific neural codes. In that sense, each object has its own neural code within in a distributed network. The topographical distribution of the

correlation coefficients with foci corresponding to the temporal cortex further manifests this view.

Results from hierarchical clustering additionally support this view. The algorithm clustered similar vectors, i.e. the neuromagnetic ‘signature’ of each concept, and reconstructed the a priori categories (for the left hemisphere in the time range from 210-450 ms). For the less perfect cases, during the initial passes only concepts of the same categories were clustered.

However, the self-organizing maps did not reveal any clear categorical organization in the data.

This failure to reconstruct the a priori categories might be related to limitations of the algorithm 50

A.4.3 : Discussion - Functional architecture

applied. The original data vectors were mapped on a two-dimensional grid and it can not be ruled that similarities 'hidden' in a three- or higher dimensional solution remained undetected. A second problem is that each SOM detects different similarities between the data vectors and therefore yields different solutions. Also, similar samples are not necessarily near each other and a cluster can get split into two groups. It is possible that a larger number of iterations would have resulted in 'better' maps.