The dynamics of invariant object recognition in the human visual system.
By Leyla Isik, Ethan M. Meyers, Joel Z. Leibo, Tomaso A. Poggio. 2014
267 citations recorded by the source corpus.
The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and position-invariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feed-forward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream.
Subfields: Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Fabio Anselmi
- Qianli Liao
- Joel Z. Leibo
- Tomaso A. Poggio
- Thomas A. Carlson
- Susan G. Wardle
- Tijl Grootswagers
- Thomas Serre
Citations in the corpus
- View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016, neutral): “This is too fast of a timescale for feedback to play a large role [23, 50, 27].”
- The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex (2014, background): “(2) The ventral stream’s feedforward, hierarchical operating mode is sufficient for recognition [115, 116, 117].”
- Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “Another popular alternative is the permutation test, which entails repeatedly shuffling the data and recomputing classifier performance on the shuffled data to obtain a null distribution, which is then compared against observed classifier performance on the original set to assess statistical signifi”