Neural mechanisms of rapid natural scene categorization in human visual cortex

By Marius V. Peelen, Li Fei-Fei, Sabine Kastner. Nature, 2009

306 citations recorded by the source corpus.

The visual system has an extraordinary capability to extract categorical information from complex natural scenes. For example, subjects are able to rapidly detect the presence of object categories such as animals or vehicles in new scenes that are presented very briefly. This is even true when subjects do not pay attention to the scenes and simultaneously perform an unrelated attentionally demanding task, a stark contrast to the capacity limitations predicted by most theories of visual attention. Here we show a neural basis for rapid natural scene categorization in the visual cortex, using functional magnetic resonance imaging and an object categorization task in which subjects detected the presence of people or cars in briefly presented natural scenes. The multi-voxel pattern of neural activity in the object-selective cortex evoked by the natural scenes contained information about the presence of the target category, even when the scenes were task-irrelevant and presented outside the focus of spatial attention. These findings indicate that the rapid detection of categorical information in natural scenes is mediated by a category-specific biasing mechanism in object-selective cortex that operates in parallel across the visual field, and biases information processing in favour of objects belonging to the target object category.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b88340dc989ef314a455566e51c264de2513e774

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In Cognitive Neuroscience

Citations in the corpus

A neural basis for real-world visual search in human occipitotemporal cortex (2011, methodology): “Data were analyzed using multivoxel pattern analysis, a technique that has been successfully used to reveal object category coding in extrastriate visual cortex (13, 14).”
Attention During Natural Vision Warps Semantic Representation Across the Human Brain (2013, unknown): “These categories were used because they are quite distinct from one another, they occur commonly in real-world scenes, and they are common targets of visual search 12,13 .”
THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images (2019, background): “[79, 80]), and objects appearing in dynamic movies (e.”
Intuition: A Challenge for Psychological Research on Decision Making (2010, background): “For example, there is evidence that the visual system can extract categorical information—such as the presence of cars or animals—in scenes after very brief exposure and even when performing unrelated tasks that demand attention ( Peelen, Fei-Fei, & Kastner, 2010 ).”
Neural mechanisms of object-based attention. (2015, methodology): “…that featurebased attention leads to reliable modulations in early visual areas (Kamitani and Tong 2005; Jehee et al. 2011), and such methods have been successfully applied to investigate attentional modulation of high-level object areas (Peelen et al. 2009; Reddy et al. 2009; Cukur et al. 2013).”
The cortical dynamics of speaking: present shortcomings and future avenues (2016, unknown): “…& Kastner, 2009) and even anticipate specific objects for recognition (e.g. Chelazzi, Miller, Duncan, & Desimone, 1993; O’Craven & Kanwisher, 2000; Peelen et al., 2009; Ullman, 2007) (for reviews see: e.g. Corbetta & Shulman, 2002; Gilbert & Li, 2013; Kanwisher & Wojciulik, 2000; Kastner &…”
Encoding and decoding in fMRI (2010, background): “An incomplete tally of some current work includes studies of vision (Brouwer and Heeger, 2009; Carlson et al., 2003; Cox and Savoy, 2003; Eger et al., 2008; Haxby et al., 2001; MacEvoy and Epstein, 2009; Peelen et al., 2009; Walther et al., 2009), = ? (1) Collect data and”
Deconstructing multivariate decoding for the study of brain function (2017, background): “, 2008), visual search templates in object-selective cortex (Peelen et al., 2009), and reward value representations in parietal cortex (Kahnt et al.”
Confounds in multivariate pattern analysis: Theory and rule representation case study (2013, unknown): “The second is " within-minus-across pattern similarity, " or the difference between within-condition and across-condition pattern correlations (e.g., Haxby et al., 2001; Peelen et al., 2009).”