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Cognitive Neuroscience

Cognitive Neuroscience. Recognizing Objects: The Computational Problems. The problem:. The visual system needs to be general enough so as to allow us to recognize objects under variable conditions—achieve object constancy

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Cognitive Neuroscience

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  1. Cognitive Neuroscience Recognizing Objects: The Computational Problems

  2. The problem: • The visual system needs to be general enough so as to allow us to recognize objects under variable conditions—achieve object constancy • Yet specific enough to allow us to detect differences between objects, and exemplars of objects

  3. Object constancy We can recognize objects across changes in: • Illumination • Size • Occlusion • Viewing position These changes create very different signals at the retina, the brain must be able to determine their constancy despite these differences

  4. How do we do this? Evidence from cognitive psychology (1) Independence of visual feature dimensions (2) Vision is “non-veridical”: not simply stimulus-driven but also knowledge- driven

  5. 1-Independence of color and shape: Illusory conjunctions

  6. 5 TS N 8

  7. 1-Independence of color and form: Feature and conjunction search (Treisman and colleagues)

  8. Features? • Visual search methods can be used to reveal the features that are independently processed by the visual system

  9. 2-Knowledge-driven perception • Data (stimulus) driven (bottom–up) + • Knowledge driven (top-down)

  10. 2-Knowledge-driven perception: Illusory contours

  11. 2-Knowledge driven perception: Grouping (Gestalt) principles

  12. 2-Knowledge driven perception: Grouping (Gestalt) principles

  13. Features + principles grouping of features Illusory conjunctions within vs. across groups:

  14. Features + principles grouping of features Illusory conjunctions within vs. across groups: 22% 15%

  15. Structural descriptions How/what is information stored that will support general and specific object recognition across a wide range of viewing conditions? • Templates? • Structural descriptions? Extremely difficult problem, receiving considerable research attention.

  16. Predictions for breakdown?

  17. AgnosiaJGE (Leek & Rapp, 199*) • 74 year-old male • Master’s degree, high-school business teacher • Left CVA affecting left occipito-parietal region and a small right occipital infarct • Auditory comprehension and spoken production excellent

  18. rack of trays mule bowl cabinet

  19. Picture Naming Accuracy: 75% (349/464) correct Errors: Visual+semantic: 45% (sheep -> cow) Semantic 26% (shoe -> sweater) Visual 17% (baseball bat -> cigarette) Circumlocutions 10% (helicopter -> flies about, flying ambulance) Don’t Know 1%

  20. Where is the breakdown? Naming?

  21. A naming problem? Naming from other modalities of input: Tactile: -50 objects to name from tactile presentation with eyes closed (including 21 items previously named incorrectly with visual input) -results: 96% correct (100% correct on subset) To definition: -asked to provide a definition of 42 objects misnamed from vision -results: 98% correct

  22. Where is the breakdown? Knowledge of The visual attributes of objects?

  23. Knowledge of visual attributes? Drawing from memory: -36 objects he had previously misnamed -results: 92% correct (inclusion of object parts and spatial configuration) Verbal definitions: -asked to define 42 objects previously misnamed -results: 100% correct, including info about shape and appearance

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