Cognitive processes psy 334
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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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Cognitive ProcessesPSY 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

  • 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.


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.


Tests for Associative Agnosia

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


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.


The Eye


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


Pathways to the Visual Cortex


Pathways Forward


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.


Edge and Bar Detectors


Edge & Bar Detectors (Cont.)


Computer Edge Detection


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.


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


Depth Cues


Optic flow

Nearer things move faster, farther things move slower


Size Constancy is Mental


The same photo


The same photo again


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.


Pattern Recognition

  • Classification and recognition of objects occurs through processes of pattern recognition.

  • Bottom-up processes – feature detection

  • Top-down processes -- conceptually driven processing


Top-Down Processing

Why do we see an H in the first word but an A in the second word?


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.


Five Principles

  • Proximity

  • Similarity

  • Good continuation

  • Closure

  • Common fate

    • Elements that move together group together.

  • These will be on the midterm.


Examples (Fig 2-13)

proximity

similarity

good continuation

closure


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


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.


Law of Pragnanz

People are more likely to see (b) and (c) not (d) or (e) in figure (a)


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/


Visual Pattern Recognition

  • Bottom-up approaches:

    • Template-matching

    • Feature analysis

    • Recognition by components


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.


Example from the Internet


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.


Features of Letters


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.


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.


Sample Geons


Biederman’s Stimuli


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.


Biederman’s Results


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.


Identification of Faces and Members of CategoriesProsopagnosia

The Fusiform Face Area:

http://www.psy.vanderbilt.edu/faculty/gauthier/picts/mona_lisa.jpg


Thatcher Illusion (without Thatcher)


Thatcher Illusion (Cont.)

Why did it look more normal when viewed upside down?


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.)


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.


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.


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.


Categorical Perception

  • For speech, perception does not change continuously but abruptly at a category boundary.

  • Categorical perception – failure to perceive gradations among stimuli within a category.

    • Pairs of [b]’s or [p]’s sound alike despite differing in voice-onset times.


Perception of /b/ versus /p/


/b/ versus /p/


Phoneme Perception Results

Notice the abrupt shift from /b/ to /p/


Two Views of Categorical Perception

  • Weak view – stimuli are grouped into recognizable categories.

  • Strong view – we cannot discriminate among items within such a category.

  • Massaro – people can discriminate within category but have a bias to say items are the same despite differences.

  • Category boundaries can be shifted by fatiguing the feature detectors.


Top Down Processing


Top Down Processing

  • General knowledge (context, high-level thinking) combines with interpretation of low-level perceptual units (features).

  • Context limits the possibilities so fewer features must be processed:

    • Word superiority effect – D or K vs WORD or WORK – words do 10% better.

    • To xllxstxatx, I cxn rxplxce xvexy txirx lextex of x sextexce xitx an x, anx yox stxll xan xanxge xo rxad xt wixh sxme xifxicxltx.


Word Superiority Effect

K

D

K

Subjects seeing the letter K in the context of a word did 10% better than the other conditions.

WORK

OWRK


Context and Speech

  • Phoneme restoration effect:

    • It was found that the *eel was on the axle.

    • It was found that the *eel was on the shoe.

    • It was found that the *eel was on the orange.

    • It was found that the *eel was on the table.

  • The identification of the missing word depends on what happens after it.


Models of Object Perception

  • Two competing models explain how context and feature information are combined:

    • Massaro’s FLMP (fuzzy logic model of perception) -- Context and detail are two independent sources of information.

    • McClelland & Rumelhart’s PDP model – connectionist model in which both sources of information interact.


Testing the FLMP Model

  • Four kinds of stimuli:

    • Only an e can make a real word.

    • Only a c can make a real word.

    • Both letters can make a word.

    • Neither letter can make a word.

  • Within each group, stimuli go from e to c.

  • Subjects saw each stimulus word briefly and had to identify the letter, e or c.


Testing the Fuzzy Logic Model


FLMP Results

  • Observed frequencies for naming a letter e increase as it has more e features, but also as the context demands an e.

  • Baye’s theorem gives a formula for combining the independent contributions of two sources of information.

  • Massaro’s results conform to predictions of Baye’s theorem, suggesting that the information sources must be independent of each other.


Testing the PDP Model

  • Activation spreads from features to excite letters and from letters to excite words (bottom up processing).

  • Activation also spreads from words to the component letters (top-down processing).

  • The more activation, the more likely the correct letter will be identified:

    • TRAP vs TRIP


Faces and Scenes

  • When parts are presented in isolation, more feature information is needed to recognize them.

    • Face parts are recognized with less detail when in the context of a face.

    • Subjects are better able to identify objects when they are part of coherent novel scenes rather than jumbled scenes.


Jumbled Scenes

The same details are in both stimuli but people identify more objects when the overall scene makes sense.


Change Blindness

  • People cannot keep track of all of the information in a complex scene.

  • If change occurs during a scene-cut or eye movement and it fits the context, it may be missed.

    • Large changes can be overlooked.

    • 7 of 15 participants noticed that the person changed entirely while giving directions

    • Demo: http://viscog.beckman.illinois.edu/djs_lab/demos.html


Change Blindness


Marr

  • Depth cues (texture gradient, stereopsis) – where are edges in space?

  • How are visual cues combined to form an image with depth?

    • Primal sketch – extracts features.

    • 2-1/2 D sketch – identifies where visual features are in relation to observer (depth).

    • 3-D model – refers to the representation of the objects in a scene, combines context.


Stages in Marr’s Model


Putting it All Together

  • The output of these stages is a representation of an object and its location.

  • This output is used as input to higher-level cognitive processes.

  • Conscious awareness (a higher-level process) involves the recognition stage, but lots of processing occurs first.


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