Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual cortex. What remains unclear is how strongly experimental choices, such as network architecture, training, and fitting to brain data, contribute to the observed similarities. Here, we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 object images in human inferior temporal cortex (hIT), as measured with fMRI. We compare untrained networks to their task-trained counterparts and assess the effect of cross-validated fitting to hIT, by taking a weighted combination of the principal components of features within each layer and, subsequently, a weighted combination of layers. For each combination of training and fitting, we test all models for their correlation with the hIT representational dissimilarity matrix, using independent images and subjects. Trained models outperform untrained models (accounting for 57% more of the explainable variance), suggesting that structured visual features are important for explaining hIT. Model fitting further improves the alignment of DNN and hIT representations (by 124%), suggesting that the relative prevalence of different features in hIT does not readily emerge from the Imagenet object-recognition task used to train the networks. The same models can also explain the disparate representations in primary visual cortex (V1), where stronger weights are given to earlier layers. In each region, all architectures achieved equivalently high performance once trained and fitted. The models' shared properties—deep feedforward hierarchies of spatially restricted nonlinear filters—seem more important than their differences, when modeling human visual representations.

In the ventral visual pathway, early visual areas encode light patterns on the retina in terms of image properties, for example, edges and color, whereas higher areas encode visual information in terms of objects and categories. At what point does semantic knowledge, as instantiated in human language, emerge? We examined this question by studying whether semantic similarity in language relates to the brain's organization of object representations in inferior temporal cortex (ITC), an area of the brain at the crux of several proposals describing how the brain might represent conceptual knowledge. Semantic relationships among words can be viewed as a geometrical structure with some pairs of words close in their meaning (e.g., man and boy) and other pairs more distant (e.g., man and tomato). ITC's representation of objects similarly can be viewed as a complex structure with some pairs of stimuli evoking similar patterns of activation (e.g., man and boy) and other pairs evoking very different patterns (e.g., man and tomato). In this study, we examined whether the geometry of visual object representations in ITC bears a correspondence to the geometry of semantic relationships between word labels used to describe the objects. We compared ITC's representation to semantic structure, evaluated by explicit ratings of semantic similarity and by five computational measures of semantic similarity. We show that the representational geometry of ITC—but not of earlier visual areas (V1)—is reflected both in explicit behavioral ratings of semantic similarity and also in measures of semantic similarity derived from word usage patterns in natural language. Our findings show that patterns of brain activity in ITC not only reflect the organization of visual information into objects but also represent objects in a format compatible with conceptual thought and language.

How does the brain translate an internal representation of an object into a decision about the object's category? Recent studies have uncovered the structure of object representations in inferior temporal cortex (IT) using multivariate pattern analysis methods. These studies have shown that representations of individual object exemplars in IT occupy distinct locations in a high-dimensional activation space, with object exemplar representations clustering into distinguishable regions based on category (e.g., animate vs. inanimate objects). In this study, we hypothesized that a representational boundary between category representations in this activation space also constitutes a decision boundary for categorization. We show that behavioral RTs for categorizing objects are well described by our activation space hypothesis. Interpreted in terms of classical and contemporary models of decision-making, our results suggest that the process of settling on an internal representation of a stimulus is itself partially constitutive of decision-making for object categorization.