The Processing Of Environ Mental Stimuli Psychology Essay
Critically, an MVPA classifier initially trained to differ- entiate the neural signatures of the two line orientations when each was presented alone was also able to decode which of the two line orientations was being attended when the stim- uli were concurrently displayed. Distributed information about the attended orientation was present even at the earliest cortical level of vi- sual processing (V1). Thus, despite equivalent bottom-up input, attentional signals served to bias neural patterns in favor of the task-relevant stimulus/feature.
Subsequent studies have documented the attentional modulation of distributed cortical patterns across a variety of low-level and high- level stimulus materials, ranging from simulta- neously presented motion fields (Kamitani & Tong 2006, Liu et al. 2011) to simultaneously presented visual objects (Macevoy & Epstein 2009, Reddy & Kanwisher 2006).Moreover, it is not only possible to decode which of mul- tiple stimuli is currently being attended, but also what aspect of a given stimulus is being at- tended.For instance, distributed fMRI patterns across face-selective voxels in the fusiform and occipital cortices can be used to decode whether participants are preferentially attending to the race or the gender of a face (Chiu et al. 2011). Likewise, the cat- egory of imagined objects can be decoded from the same VTC voxel patterns that are engaged during the perception of stimuli from these categories (Cichy et al. 2011b, Reddy et al. 2010), and MVPA techniques can even reconstruct a coarse visual representation of what a participant is currently imagining based on fMRI activity patterns in retinotopic cortex (Thirion et al. 2006).
Information about an individual’s current attentional priorities can be extracted from fMRI activity patternswithin dorsal regions of the frontal and parietal lobes. The role of these frontoparietal struc- tures may also extend to the specification and maintenance of more abstract task sets, such as representing which stimulus-response mapping scheme (Bode & Haynes 2009, Woolgar et al. 2011), perceptual categorization rule (Li et al. 2007), or mathematical operation (Haynes et al. 2007) should be applied at a given moment in time.
Although Diana et al. were unable to decode the viewing of complex scenes relative to other visual categories from hr-fMRI ac- tivity patterns in human hippocampus, recent hr-fMRI data indicate that it is possible to decode which of two complex scenes is being viewed based on distributed BOLD signals in the hippocampus (as well as in ERC and PHC) (Bonnici et al. 2011).
(Rissman & Wagner, 2012)
Although columns containing cells that respond selectively to faces or sim- ilar features tend to cluster together, these studies have not revealed any consistent larg- er scale organization for object representa- tion. Numerous computational models for ob- ject recognition have been developed (4), but the correspondence between these models and the neural architecture of the ventral object vision pathway is uncertain.
According to this model, which we have named “object form topography,” ventral temporal cortex has a topographically organized representation of attributes of form that underlie face and ob- ject recognition. The representation of a face or object is reflected by a distinct pattern of response across a wide expanse of cortex in which both large- and small-amplitude re- sponses carry information about object ap- pearance. Unlike the other models, object form topography predicts how all categories might evoke distinct patterns of response in ventral temporal cortex and, thereby, pro- vides an explicit account for how this cortex can produce unique representations for a vir- tually unlimited number of categories.
(Haxby et al., 2001)
. To date, only a few object categories—namely faces, bodies, and letter strings—have been shown to have focal cortical regions that show strong category selectivity (Cohen et al., 2000; Downing et al., 2001; Kanwisher et al., 1997; McCarthy et al., 1997). Most other object categories such as shoes and cars do not have a clear spatially clustered region of selective cortex but instead activate a large swath of occipitotemporal cortex with distinct and reliable patterns (Carlson et al., 2003; Cox and Savoy, 2003; Haxby et al., 2001; Norman et al., 2006; O’Toole et al., 2005).
In the current study, we compared the cortical response to big
and small real-world objects. We specifically focused on the representations of everyday inanimate objects, excluding faces, bodies, animals, and classically defined tools. These everyday objects often get grouped together as ‘‘other objects’’ (e.g., see Hasson et al., 2003;Opde Beeck et al., 2008) and are known to have a distributed activation pattern across a large swath of ventral-temporal cortex. Here, we examined whether voxels along this cortex showed a preference for objects of big or small real-world sizes.
(Konkle & Oliva, 2012)
Human inferior temporal (hIT) cortex has been shown to con- tain category-selective regions that respond more strongly to ob- ject images of one specific category than to images belonging to other categories. The two most well known category-selective regions are the FFA, which responds selectively to faces (Puce et al., 1995; Kanwisher et al., 1997), and the PPA, which responds selectively to places (Epstein and Kanwisher, 1998). The category selectivity of these regions has been shown for a wide range of stimuli (Kanwisher et al., 1999; Downing et al., 2006). However, previous studies grouped stimuli into predefined natural catego- ries and assessed only category-average activation. To investigate responses to individual stimuli, each stimulus needs to be treated as a separate condition (single-image design). Despite common use of single-image designs inmonkeyelectrophysiology (Vogels, 1999; Fo
¨ldia´k et al., 2004; Tsao et al., 2006; Kiani et al., 2007) and occasional use of item-specific designs in human studies in other domains (Bedny et al., 2007), single-image responses in human visual cortex have not been thoroughly investigated in object- vision functional magnetic resonance imaging (fMRI).
For PPA and right FFA, no inver- sions were detected consistent with the analysis shown in Figure 4B, where acti- vation profiles were averaged across sub- jects with equal weights. However, for left FFA (defined at 55 or 128 voxels), we found evidence for replicated inverted pairs. In sum, our findings are consistent with the idea that right FFA will prefer any face over any nonface (in terms of its regional-average activation), and that left and right PPA will similarly prefer any place over any nonplace. Only for left FFA was there some evidence for preference inversions for particular images.
(Mur et al., 2012)
Z- Normalization by adnan
For each voxel calculate the mean subtract it from the voxel value and then divde vy the std of the voxel. This should be done every time a new sample arrives. So iterative approach should be used.
Multiclass classification The objective of the multiclass classification experiment was to
test whether the online classifier is able to classify three discrete emotions, namely, happy, sad and disgust. The multiclass problem was formulated as a problem of finding the best classification among three trained binary classifiers (i.e., classifier 1: happy vs. disgust, classifier 2: happy vs. sad, and classifier 3: disgust vs. sad) based on the selected voxels from an Effect map of each combination as described above.
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