Cognitive Processes PSY 334 Chapter 2 – Perception

Marr  Depth cues (texture gradient, stereopsis) – 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 occurs through processes of pattern recognition.  Bottom-up processes – feature detection  Top-down processes -- conceptually driven processing

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.

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 Gestalt principles of organization Reversible figures Stuart Anstis demos:

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.

Visual Illusions  Depend on experience. Influenced by culture.  Illustrate normal perceptual processes. These are not errors but rather failures of perception in unusual situations.

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.

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.

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.

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 ( ms) subjects could not recognize components when segments were missing.

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.

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 same items are the same despite differences.  Category boundaries can be shifted by fatiguing the feature detectors.