networks and faces Flashcards
deep neural network (DNN)
‘machine learning’ in ai; computer programmed to learn something (e.g. object recognition)
levels of object recognition
entry, subordinate (specific), superordinate (general)
4 challenges for DNN in replicating human object recognition
- fooled by image manipulations that humans aren’t
- can’t recognize objects in multiple ways (levels of object recognition)
- subordinate categories may come first for unusual category members
- entry level shifts down for experts
structural description (geons) is for which category of object recognition?
entry level
viewpoint representation is important for which category of object representation?
subordinate level
example for an unusual category member that might be recognized at subordinate level first?
ostrich
example for when entry level shifts down for experts?
recognizing specific bird rather than just ‘bird’
face inversion effect
recognition is more difficult for inverted faces; distortions go unnoticed
holistic processing
analysis of entire object/scene; upright faces but not inverted faces or other objects
composite face effect
when faces mixed up: slower, less accurate id; not occur for misaligned or inverted faces
visual agnosia
cannot interpret visual sensory information; impairment in perception/recognition w/ relatively intact low-level vision
prosopagnosia (definition & name 2 types)
selective inability to perceive/recognize faces; usually associative; apperceptive
associative prosopagnosia is what and involves what part(s) of brain?
selective inability to recognize faces; R or bilateral anterior temporal lobe
apperceptive prosopagnosia is what and involves what part(s) of brain?
selective inability to perceive/recognize faces; R or bilateral occipitotemporal/fusiform gyrus
what prosopagnosia did the man who mistook his wife for a hat have?
apperceptive prosopagnosia