Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

By Nikolaus Kriegeskorte, Marieke Mur, D. Ruff, Roozbeh Kiani, Jerzy Bodurka, Hossein Esteky, Keiji Tanaka, Peter A. Bandettini. 2008

1,389 citations recorded by the source corpus.

SUMMARY Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT’s role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…DiCarlo, Zoccolan, & Rust, 2012; Felleman & Van Essen, 1991; Hubel & Wiesel, 1968; Hung, Kreiman, Poggio, & DiCarlo, 2005; Kobatake & Tanaka, 1994; Kriegeskorte et al., 2008; Lennie & Movshon, 2005; Logothetis, Pauls, & Poggio, 1995; Perrett, Hietanen, Oram, Benson, & Rolls, 1992; Perrett, Rolls,…”
Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “In contrast, we expect most informative semantic features to be shared across multiple classes: indeed, cognitive research suggests that in humans’ early visual cortex, representations of different objects occupy a common feature space (Kriegeskorte et al., 2008).”
Similarity of Neural Network Representations Revisited (2019, methodology): “In neuroscience, such matrices representing the similarities between examples are called representational similarity matrices (Kriegeskorte et al., 2008a).”
How does the brain solve visual object recognition? (2012, neutral): “For example, a comparison of monkey IT and human ‘‘IT’’ (LOC) shows strong commonality in the population representation of object categories (Kriegeskorte et al., 2008).”
A mathematical theory of semantic development in deep neural networks (2018, methodology): “Strikingly, studies have found remarkable constancy between neural similarity structures across human subjects, and even between humans and monkeys [27, 28].”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “This method was chosen because it did not require the data being tested to meet distributional assumptions (Kriegeskorte et al., 2008).”
Comparison of multivariate classifiers and response normalizations for pattern-information fMRI (2010, positive): “More details on the experiment are in Kriegeskorte et al. (2008a).”
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation (2014, methodology): “Several studies suggested that the IT representation is not purely visual but also semantic [7,14,22,81].”
Performance-optimized hierarchical models predict neural responses in higher visual cortex (2014, methodology): “Although first principles-based models of higher ventral cortex have been proposed (15–20), these models fail to match important features of the higher ventral visual neural representation in both humans and macaques (4, 21).”
Recurrence is required to capture the representational dynamics of the human visual system (2019, neutral): “This stimulus set was used across multiple studies and laboratories to collect human fMRI (20) and MEG data (16, 45), human perceptual similarity judgments (46), and macaque single-cell data (27), and was used in previous investigations of DNN models (25, 47).”
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision (2016, result): “5d), in line with previous findings (Kriegeskorte et al. 2008; Naselaris et al. 2012).”
A common, high-dimensional model of the representational space in human ventral temporal cortex (2011, background): “…Universitá degli studi di Trento, Rovereto 38068, Italy 3Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA 4Dipartimento di Psicologia, Universitá di Bologna, Bologna 40127, Italy *Correspondence: james.v.haxby@dartmouth.edu DOI 10.1016/j.neuron.2011.08.026”