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Cognitive Processes PSY 334

Cognitive Processes PSY 334. Chapter 2 – Perception. Object Recognition. Two stages: Early phase – shapes and objects are extracted from background. Later phase – shapes and objects are categorized, recognized, named. Disruptions of Perception.

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Cognitive Processes PSY 334

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  1. Cognitive ProcessesPSY 334 Chapter 2 – Perception

  2. Object Recognition • Two stages: • Early phase – shapes and objects are extracted from background. • Later phase – shapes and objects are categorized, recognized, named.

  3. Disruptions of Perception • Visual agnosias – impairment of ability to recognize objects. • Demonstrate that shape extraction and shape recognition are separate processes. • Apperceptive agnosia (lateral) – problems with early processing (shape extraction). • Associative agnosia(bilateral) – problems with later processing (recognition). • Prosopagnosia – visual agnosia for faces.

  4. Tests for Apperceptive Agnosia Some patients would have trouble drawing this chair due to the missing contours. Some patients would have trouble recognizing a chair from this perspective.

  5. Tests for Associative Agnosia The subject can copy the anchor accurately (as shown) but then cannot tell you what it is.

  6. Early Visual Processing • Parts of the eye • Two kinds of photoreceptors: • Rods respond to motion, light & dark • Cones respond to color, shape, detail • Fovea is the area of the retina with highest resolution – best for seeing detail. • We move our eyes so light hits the fovea.

  7. The Eye

  8. Later Visual Processing • Neural pathways from the eyes to the visual cortex split at the optic chiasm. • Info from the left visual field goes to the right hemisphere. • Info from the right visual field goes to the left hemisphere. • Two pathways from the visual cortex: • “Where” pathway • “What” pathway

  9. Pathways to the Visual Cortex

  10. Pathways Forward

  11. Information Coding • On-off cells in LGN feed into edge and bar detectors in the visual cortex. • Edge detectors – respond positively to light on one side of a line, negatively on the other side of the line. • Bar detectors – responds maximally to a bar of light covering its center.

  12. Edge and Bar Detectors

  13. Edge & Bar Detectors (Cont.)

  14. Computer Edge Detection

  15. Feature Maps • In addition to edges, lines, bars, other information is extracted from the visual signal: • Color • Motion • These aspects, called “features,” are represented in feature maps located in different areas of the brain.

  16. Depth Perception • Our eyes turn a three-dimensional world into a two-dimensional image on the retina. • Our cortex turns that two-dimensional image back into three-dimensions (depth). • Cues are used to infer distance. • Cues must be learned through experience. • Depth cues in art: http://psych.hanover.edu/KRANTZ/art/cues.html

  17. Depth Cues

  18. Optic flow Nearer things move faster, farther things move slower

  19. Size Constancy is Mental

  20. The same photo

  21. The same photo again

  22. Marr • Depth cues (texture gradient, stereopsis, motion parallax) – where are edges in space? • How are visual cues combined to form an image with depth? • 2-1/2 D sketch – identifies where visual features are in relation to observer. • 3-D model – refers to the representation of the objects in a scene.

  23. Pattern Recognition • Classification and recognition of objects occurs through processes of pattern recognition. • Bottom-up processes – feature detection • Top-down processes -- conceptually driven processing

  24. Top-Down Processing Why do we see an H in the first word but an A in the second word?

  25. Gestalt Priniciples • Wertheimer, Koffka, Kohler. • Form perception – segregation of a display into objects and background. • Principles of perceptual organization allow us to see “wholes” (gestalts) formed of parts. • We do not recognize objects by identifying individual features.

  26. Five Principles • Proximity • Similarity • Good continuation • Closure • Common fate • Elements that move together group together. • These will be on the midterm.

  27. Examples (Fig 2-13) proximity similarity good continuation closure

  28. Examples • Gestalt principles of organization • http://psych.hanover.edu/Krantz/sen_tut.html • Illusory contours: http://psych.hanover.edu/JavaTest/Media/Chapter5/MedFig.IllusoryContour.html • Reversible figures • http://www.psy.ritsumei.ac.jp/~akitaoka/reversiblee.html • Apparent motion demos: http://psy.ucsd.edu/~sanstis/SACamov.html http://www.michaelbach.de/ot/mot_biomot/index.html http://www.lifesci.sussex.ac.uk/home/George_Mather/BM_ECVP_2006.htm

  29. Law of Pragnanz • Of all the possible interpretations, we will select the one that yields the simplest or most stable form. • Simple, symmetrical forms are seen more easily. • In compound letters, the larger figure dominates the smaller ones.

  30. Law of Pragnanz People are more likely to see (b) and (c) not (d) or (e) in figure (a)

  31. Visual Illusions • Depend on experience. • Influenced by culture. • Illustrate normal perceptual processes. • These are not errors but rather failures of perception in unusual situations. • Try some yourself: • http://www.michaelbach.de/ot/

  32. Visual Pattern Recognition • Bottom-up approaches: • Template-matching • Feature analysis • Recognition by components

  33. Template-Matching • A retinal image of an object is compared directly to stored patterns (templates). • The object is recognized as the template that gives the best match. • Used by computers to recognize patterns. • Evidence shows human recognition is more flexible than template-matching: • Size, place, orientation, shape, blurred or broken (ambiguous or degraded items easily recognized by people.

  34. Example from the Internet

  35. Feature Analysis • Stimuli are combinations of elemental features. • Features are recognized and combined. • Features are like output of edge detectors. • Features are simpler, so problems of orientation, size, etc., can be solved. • Relationships among features are specified to define the pattern.

  36. Features of Letters

  37. Evidence for Feature Analysis • Confusions – people make more errors when letters presented at brief intervals contain similar features: • G misclassified: as C (21), as O (6), as B (1), as 9 (1) • When a retinal image is held constant, the parts of the object disappear: • Whole features disappear. • The remaining parts form new patterns.

  38. Object Recognition • Biederman’s recognition-by-components: • Parts of the larger object are recognized as subobjects. • Subobjects are categorized into types of geons – geometric ions. • The larger object is recognized as a pattern formed by combining geons. • Only edges are needed to recognize geons.

  39. Sample Geons

  40. Biederman’s Stimuli

  41. Tests of Biederman’s Theory • Object recognition should be mediated by recognition of object components. • Two types of degraded figures presented for brief intervals: • Components (geons) missing • Line segments missing • At fast intervals (65-100 ms) subjects could not recognize components when segments were missing.

  42. Biederman’s Results

  43. Face Recognition • Prosopagnosia – inability to recognize familiar faces. • Are faces special? • Thatcher effect • Damage to fusiform gyrus causes prosopagnosia. • The area may also be used for fine-grained distinctions needed to recognize faces but also other objects. • Bird, car & greeble experts all use it.

  44. Identification of Faces and Members of CategoriesProsopagnosia The Fusiform Face Area: http://www.psy.vanderbilt.edu/faculty/gauthier/picts/mona_lisa.jpg

  45. Thatcher Illusion (without Thatcher)

  46. Thatcher Illusion (Cont.) Why did it look more normal when viewed upside down?

  47. Greebles & Faces Figure 4.24 (a) Greeble stimuli used by Gauthier. Participants were trained to name each different Greeble. (b) Brain responses to Greebles and faces before and after Greeble training. (a: From Figure 1a, p. 569, from Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P. L., & Gore, J. C. (1999). Activation of the middle fusiform “face area” increases with experience in recognizing novel objects. Nature Neuroscience, 2, 568-573.)

  48. Speech Recognition • The physical speech signal is not broken up into parts that correspond to recognizable units of speech. • Undiminished sound energy at word boundaries – gaps are illusory. • Cessation of speech energy in the middle of words. • Word boundaries cannot be heard in an unfamiliar language.

  49. Phoneme Perception • No one-to-one letter-to-sound correspondence. • Speech is continuous – phonemes are not discrete (separate) but run together. • Speakers vary in how they produce the same phoneme. • Coarticulation – phonemes overlap. • The sound produced depends on the sound immediately preceding it.

  50. Feature Analysis of Speech • Features of phonemes appear to be: • Consonantal feature (consonant vs vowel). • Voicing – do vocal cords vibrate or not. • Place of articulation – where the vocal track is constricted (where is tongue placed). • The phoneme heard by listeners changes as you vary these features. • Sounds with similar features are confused.

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