Neural Bases of Categorization: Human fMRI Data in .NET Include Code 128 Code Set B in .NET Neural Bases of Categorization: Human fMRI Data

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12.3.2 Neural Bases of Categorization: Human fMRI Data generate, create code128 none for .net projects PDF417 Neuroimaging barcode code 128 for .NET studies of learning commonly compare BOLD-contrast responses to objects before and after training in particular brain voxels of interest. However, given.

Note that the focus in this chapter is on the perceptual and decision-related neural circuitry set up as a result of feedback-driven learning during training, not on the learning process itself. A number of studies have indicated that a cortical-striatal network is involved during the acquisition of perceptual categories (Ashby and Maddox 2005). The data show that although the basal ganglia appear to play an important role during feedback-driven task learning, little basal ganglia activity is found for well-learned tasks (Boettiger and D Esposito 2005; Raichle et al.

1994), and frontal circuits appear to be more relevant during the execution of learned tasks (Little et al. 2006; Pasupathy and Miller 2005), and show a closer correlation with behavior (Pasupathy and Miller 2005)..

object categorization in man, monkey, and machine that a voxel typically contains hundreds of thousands of neurons and that total activity in the voxel depends on the number of active neurons as well as their selectivity, learning-induced sharpening of neuronal responses, which by itself would lead to a lower population response as each neuron responds to fewer stimuli (Freedman et al. 2006; Rainer and Miller 2000), could lead to either decreases or increases in neuronal activity, depending on how training affects the number of selective neurons (see the discussion in Jiang et al. 2007).

This makes it dif cult to use BOLD-contrast amplitude changes as a measure of training-induced neuronal plasticity. Indeed, previous fMRI studies have found that perceptual and category learning can induce BOLD-contrast signal response increases (Gauthier et al. 1999; Op de Beeck et al.

2006; Pollmann and Maertens 2005), decreases (Reber et al. 1998), or both (Aizenstein et al. 2000; Kourtzi et al.

2005; Little et al. 2004). To more directly probe the changes in neuronal tuning resulting from category acquisition, we trained (Jiang et al.

2007) a group of human participants to categorize stimuli ( cars; see Fig. 12.2A) generated by a morphing system that was capable of nely and parametrically manipulating stimulus shape (Shelton 2000).

This approach allowed us to de ne precisely the categories and dissociate category selectivity, which requires neurons to respond similarly to dissimilar stimuli from the same category as well as respond differently to similar stimuli belonging to different categories, from mere tuning to physical shape differences, in which neuronal responses are a function of physical shape dissimilarity, without the sharp transition at the category boundary that is a hallmark of perceptual categorization. Importantly, unlike earlier studies, we recorded brain activity before and after training using fMRI-RA techniques. We reasoned (see the discussion of fMRI-RA in the previous section) that if categorization training leads to sharpened neuronal selectivity to car images, then the overlap of neuronal activations caused by two sequentially presented car images differing by a xed amount of shape change would decrease following training, resulting in an increase of BOLD-contrast response in the car-selective regions.

We provided direct evidence (Jiang et al. 2007) that training on a perceptual categorization task leads to the sharpening of a car-stimulus representation in lateral occipital cortex (LO), a part of LOC (Fig. 12.

2C). This LO representation showed no explicit category selectivity, seeming to be selective for physical stimulus shape only, as responses to the M3between and M3within stimulus pairs (see Fig. 12.

2B), which were equalized for physical dissimilarity but did or did not belong to different categories, respectively, did not differ signi cantly. In contrast, an area in the right lateral PFC (rLPFC) exhibited category-selective responses (Fig. 12.

2D): When participants were judging the category membership of cars, this area s activity was modulated by explicit changes of category membership, but not by shape differences alone. Note that the ROI was de ned by a very selective contrast, M3between > M3within , that was speci c to categorization without a confound by shape difference. In addition, as predicted by the model, we found that categorization training also improved subject performance on a discrimination task involving the car stimuli, without additional training.

Interestingly, there was no effect of categorical perception (CP) (i.e., increased discriminability of stimulus pairs that fall into different categories vs.

those belonging to the same category), further supporting that the learned car representation just represented object shape with no bias toward the category boundary..
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