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Trade-off between object selectivity and tolerance in monkey inferotemporal cortex.

Davide Zoccolan1, Minjoon Kouh, Tomaso Poggio

  • 1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|November 9, 2007
PubMed
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Object recognition involves selectivity and tolerance. This study found inferotemporal cortex neurons with high object selectivity typically have low tolerance, revealing a fundamental trade-off in visual processing.

Area of Science:

  • Neuroscience
  • Computer Vision
  • Cognitive Science

Background:

  • Object recognition necessitates both distinguishing between objects (selectivity) and recognizing the same object under varied conditions (tolerance).
  • The inferotemporal cortex (IT) is crucial for object recognition, exhibiting both selectivity and tolerance, yet the relationship between these properties is poorly understood.

Purpose of the Study:

  • To systematically investigate the relationship between object selectivity and tolerance in monkey inferotemporal cortex (IT) neurons.
  • To determine if high object selectivity inherently confers high tolerance to identity-preserving transformations.

Main Methods:

  • Recorded spiking responses from a population of monkey IT neurons.
  • Quantified object selectivity and tolerance to various identity-preserving image transformations (e.g., changes in position, size, illumination, clutter).

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Main Results:

  • A significant trade-off was observed: IT neurons with high object selectivity generally exhibited low tolerance, and vice versa.
  • This inverse relationship held true irrespective of the specific quantification methods for selectivity or the types of transformations examined.

Conclusions:

  • The findings reveal a fundamental trade-off between object selectivity and tolerance in the IT cortex, challenging the assumption that diagnostic features automatically guarantee tolerance.
  • This trade-off is consistent with object recognition models employing AND-like tuning mechanisms for building object selectivity.