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One type of representational scheme is provided by models in which the semantic similarity between objects is captured with semantic features (e.g., has legs, made of metal) (Tyler and Moss, 2001; Taylor et al., 2007, 2011; Tyler et al., 2013; Devereux et al., 2013). Before functional scanning, a high-resolution structural MRI image was collected using an MPRAGE sequence with 1 mm isotropic resolution. Here activation patterns in pVTC were associated with categorical similarity models; however, partial correlations showed unique category effects in different regions, object category in left lateral LOC, a category-specific representation of animals in right lateral pVTC and the animal-plant-nonbiological distinctions in bilateral medial VTC. Alex has 6 jobs listed on their profile.

However because this research focuses on similarities and differences between object categories, our understanding of how specific objects (e.g., cat, knife) are differentiated is limited. No eLetters have been published for this article. Spokeo is not a consumer reporting agency as defined by the Fair Credit Reporting Act (FCRA). DOI: The 131 object images used in the current study.

Preprocessing consisted of slice-time correction and the spatial realignment of the functional images only using SPM8 (Wellcome Institute of Cognitive Neurology, London, UK). This differentiation between objects is vital to the task of basic-level naming, as to name an object at the basic-level requires the differentiation between similar items to support lexical and phonological processes involved in naming.

22% of these people are married, and 78% are single. Our findings extend current distributed models by showing coarse dissociations between objects in posterior ventral cortex, and fine-grained distinctions between objects supported by the anterior medial temporal lobes, including the perirhinal cortex, which serve to integrate complex object information.

For example, more confusable objects can be thought of as occupying a dense area of semantic space, and so to individuate a confusable object from similar concepts depends on fine-grained distinctions that rely on the integrative properties of the perirhinal cortex leading to increases in activation. To generate a semantic confusability score for each object we calculated a weighted mean across this row of the matrix, where the weights were defined as the exponential of the ranked similarities along the row of the matrix. Therefore, this analysis determines the effects for each model that cannot be accounted for by any other model. For example, the semantic feature model encompasses both category structure and object-specific semantic individuation and showed effects in both posterior and anterior parts of the ventral stream, whereas categorical models only show effects in the posterior regions.

The similarity maps for each participant were entered into a group-level random-effects analysis (RFX) and permutation-based statistical nonparametric mapping (SnPM; was used to test for significant positive similarities between theoretical and fMRI RDMs, correcting for multiple comparisons across voxels and the number of theoretical model RDMs tested. The object-specific semantic model was based on similarity according to semantic feature information. Voxels outside the perirhinal cortex show increased activation with increasing semantic confusability in the whole-brain analysis (voxelwise p < 0.001, cluster-level p < 0.05 using FWE). Participants were scanned at the MRC Cognition and Brain Sciences Unit, Cambridge, in a Siemens 3-T Tim Trio MRI scanner. Data for each participant were analyzed using the general linear model to show the parametric effects of semantic confusability of objects. The feature norms contain lists of features associated with a large range of objects (e.g., has 4 legs, has stripes and lives in Africa are features of a zebra; Table 1) and were collected by presenting participants with a written concept name and asking them to produce properties of the concept.

Animal-plant-nonbiological distinctions were seen in bilateral medial pVTC and left lateral pVTC which overlapped with previously reported category-specific effects for animals and tools in lateral and medial pVTC respectively (Chao et al., 2002). Our metric of confusability was derived from the semantic-feature similarity model, where an object's semantic confusability was defined as a weighted sum of the similarity an object has with all other objects (Table 3 shows examples of high and low confusable objects for each category). Some previous studies have reported that activation patterns in the posterior ventral stream correlate with measures of semantic similarity either based on behavioral semantic similarity judgments or semantic similarity based on word usage (Connolly et al., 2012; Carlson et al., 2014).

If significant effects were seen for more than one model, the model with the highest partial Spearman's correlation is shown.

Using RSA we revealed object-specific semantic representations coded in the perirhinal cortex, while also showing a gradient of informational specificity along the ventral stream, from image-based visual properties in primary visual cortex to coarse, categorical representations in posterior VTC (Fig. The object-specific semantic feature RDM was based on data from the anglicized McRae feature norms (McRae et al., 2005; Taylor et al., 2012).

SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci. Less confusable objects will have fewer semantic neighbors and are more easily differentiated resulting in less activation. a, Showing regions where activation patterns were significantly related to each model's predicted similarity, thresholded at pFDR < 0.0056 (corrected for 9 model RDMs). Although this suggests object representations in the ventral stream better reflect categorical groupings and representations in the anterior medial temporal lobe additionally reflect object-specific information, we require a more formal evaluation to fully test this.

This is not to claim that the perirhinal cortex represents object-specific semantics per se, but that the region codes the computations necessary for object-specific representations to be formed. Moreover, if a function of the perirhinal is to enable fine-grained differentiation, then it not only needs to code this information, but will also become increasingly activated when recognizing objects that are highly confusable with other objects. 6). Despite a wealth of fMRI research aiming to understand how objects are represented there are few accounts that consider the representation of individual objects (Kriegeskorte et al., 2008b; Mur et al., 2012; Tyler et al., 2013 for other examples of research using a large and diverse set of objects). Variance smoothing of 6 mm FWHM and 10,000 permutations were used in all analyses.

Future work could assess to what extent semantic feature models capture perceived semantic similarity, and whether any divergence provides additional information that accounts for object representations in the ventral stream. To test for categorical and object-specific semantic representations in the ventral stream, participants were scanned with fMRI while they performed a basic-level naming task (e.g., dog, hammer) with 131 different objects from a variety of categories (Fig. The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. However as some of our model RDMs are correlated (Fig. Here, we used representational similarity analysis to determine what different kinds of object information are reflected in fMRI activation patterns and uncover the relationship between categorical and object-specific semantic representations. The unique object-specific semantic effects we see in the perirhinal cortex provide the necessary object differentiation for these processes.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We found distinct categorical and object-specific semantic representations in the brain predicted by the similarity of theoretically motivated measures. RSA was used to determine the kinds of information reflected in spatial fMRI patterns throughout the brain. The site facilitates research and collaboration in academic endeavors. Patterns of activity in early visual areas showed a significant relationship with the EVC model (peak MNI coordinate in each hemisphere; −21, −91, 14 and 15, −94, 2), replicating the known properties of primary visual cortex relating to retinotopy and local edge-orientations that have been previously shown in fMRI activity patterns (Kamitani and Tong, 2005; Kay et al., 2008; Connolly et al., 2012).

However, members of some object categories are more semantically similar to one another (e.g., musical instruments are in general dark blue in the semantic feature RDM in Fig.

Although we initially report semantic feature effects in pVTC, these effects were nonsignificant once we accounted for categorical and visual similarity, both of which may contribute to previously reported effects. RSA searchlight mapping revealed overlapping and distinct representations throughout occipital and ventral temporal cortex. Thank you for sharing this Journal of Neuroscience article.

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