Perception: Pattern or object recognition. Chapter 3

Transcription

1 Perception: Pattern or object recognition Chapter 3

2 Perception Sensation vs. perception What are the mechanisms responsible? What is the process? Q: How do we interpret lines and patterns as objects? Q: How do we program a computer to perceive objects and scenes? Start simple: How do we recognize these letters as A s?

3 Template approach Stimulus is compared to stored pattern Examples? Bar code, bank check, scantron, etc. Problems: There are an infinite number of templates to remember Have to learn a template first Any change in stimuli will not be recognized

4 Bottom-up processing Cell s responses Stimulus Receptors in retina -> optic nerve -> occipital lobe (visual cortex) Specialized receptors in visual cortex Simple cells: feature detectors e.g. Orientation specific Complex cells Combination of 2 simple features Perception due to pattern of neural firing (neural code)

5 McClelland & Rummelhart (1981) Interactive Activation Model

6 Pandemonium (Selfridge, 1959)

7 Visual perception by neurons Respond to things that occur most often in environment e.g orientation: horizontal and vertical lines vs. oblique Experience-dependent plasticity Animal reared in certain environment brain changes to more strongly respond to those cues (Blakemore & Cooper, 1970) Gauthier et al. (1999): Greebles study Measure FFA (fusiform area) IV: experience with Greebles

8 Recognition by components Biederman s RBC (recognition by component) theory 36 geons (3D) Basic building blocks Emphasis on intersections Recognition with missing information possible

9 Geons: Identify objects Principle of componential recovery Resistance to visual noise View invariant properties Discriminability

10 Biederman s Geons Intersections are important to recognition

11 Beyond bottom-up processing

12 Pattern or object recognition Bottom-up processing Information from sensory receptors Processing driven by stimulus Data-driven Top-down processing Information from knowledge and expectations Processing driven by higher level knowledge Conceptually-driven Problems with pure bottom-up theories: How does brain pull all the feature information together? How do theories deal with complex objects?

13 Tox-Doxn Pxocxssxng To xllxstxatx, I cxn rxplxce xvexy txirx lextex of x sextexce xitx an x, anx yox stxll xan xanxge xo rxad xt ix wixh sxme xifxicxltx Context and knowledge fills in the rest! The redundancy of stimuli provide more features than required

14 Oliva & Torralba (2007) Q: Does perception depend on more than just stimulation of receptors? Method: Use same blob in multiple contexts Result: Perceived as different objects due to top-down processing Conclusion: Signal from object Signal from context Feedback signal: influence of knowledge

15 Theory of perception Bottom-up AND top-down Bi-directional or connectionist model Depth perception Relative size Size constancy Odor intensity Controlled sniff intensity Perception of language Speech segmentation

16 Treisman & Schmidt (1982) 1 3 Q: Does knowledge change perception? Method Flash display of #s & objects 200 ms Ask Ss to report #s then objects IV: Give description of objects ( carrot, lake, tire ) or not Results Info significantly improved accuracy Conclusion Top-down knowledge changes perception Able to bind features (group information) together more rapidly Orange & triangle = carrot

17 Hollingworth (2005) Question How does knowledge of what objects belong in a scene influence perception? Semantic regularities (knowledge of function of objects) Method Study scene 20s IV: w/ or w/o target object Test: Place target object in scene Result By memory or expectation Accurate position in both conditions Prediction based on experience

18 Palmer (1975) Method Present scene Ss ID flashed pics (a) or (b) or (c) IV: type of picture DV: accuracy Results Appropriate pictures: 83% Inappropriate pictures: 50% Misleading pictures 40% Conclusion Bottom-up perception interacts with prior knowledge (top-down) to influence response