Perception and Visual Processing: A Multidisciplinary Approach, Summaries of Psychology

Download Perception and Visual Processing: A Multidisciplinary Approach and more Summaries Psychology in PDF only on Docsity! 1 1 Visual Cognition September 11, 2008 2 Overview of Visual Cognition • Visual system: mission critical • Multivariate inputs, unitary experience • Multiple types of vision means many types of units in a distributed visual network • Segregating vs. parsing • Visual experience reveals nature of underlying representations • Theory >> data 3 Who’s Coming to Dinner? • Gestalt Psychology (interested in basic dimensions of perceptual organization) • Cognitive Psychology (interested primarily in effects of cognitive operations on visual experience) • Cognitive Neuropsychology (interested in effects of selective lesions of visual cortex on visual behavior) • Electrophysiology (interested in single- and multiple-unit activity of cells in visual cortex) • Cognitive science (interested in developing overall theories of visual experience and object recognition) 4 Sensation and Perception • Sensation: Conscious outcome of sense organs and projection regions. (“I detect something”, not necessarily conscious, and not necessarily meaningful) • Perception: means by which information acquired from the environment via the sense organs is transformed (organized) into conscious experiences of objects, events, sounds, tastes, etc. (“I know, recognize, appreciate what I am sensing, and it means something to me”) 5 Three Stages of Visual Processing • Reception: absorption of physical energy by receptors • Transduction: translation of physical energy into electrochemical activity of the nervous system • Coding: how information is encoded and represented; relationship between aspects of the physical stimulus and resultant nervous system activity (e.g., rate, temporal patterning of neuronal activity) – Population coding – Sparse coding 6 Basic Perceptual Phenomena You Already Know About • Size and Shape Constancies: reflect mechanisms whereby perception of a distal object stays the same despite proximal changes in the perception – implies storage of a “structural representation” • Depth Cues: monocular or binocular sources of information that convey information about relative distance of objects from viewer – involve cues about the structure, movement, and proximity of objects 7 8 9 Milner & Goodale: Perception-Action Model • Alternative to the Mishkin-Ungerleider model – Dorsal: “How do I interact with that object”? – Ventral: “What is that object”? 2 • Evidence: distinctions between object recognition and visual control of action; Balint’s syndrome; optic ataxia 10 Perceptual Organization • Two complementary issues: – Organizing coherent objects and events out of segregated sensory/perceptual inputs (“binding”) – “Parsing” the perceptual world; understanding which inputs belong together and which come from separate objects – http://www.michaelbach.de/ot/mot_feet_lin/index.html 11 Perceptual Organization • Gestalt Psychology – Law of Prägnanz: perceptual system organizes to the simplest and most stable shape possible from the array – other laws describe how disparate perceptual elements are grouped – although their “laws” are probably incorrect, the elementary concept of perceptual grouping is critical – Inferring processes from performance 12 13 Figure–Ground Segregation • A type of perceptual organization in which edges are assigned to regions for purposes of shape discrimination 14 15 Is PO learned or hard-wired? • Schematic depiction of two types of displays: (a) homogeneous displays and (b) heterogeneous displays. From Spelke et al. (1993) with permission from Elsevier. 16 17 18 19 Gestalt Theory in the 21st Century • Basic perceptual principles have survived • Contentions that such processes are necessarily very “early” in perception have not – Proximity computation occurs after depth perception – Segmentation is not just “bottom-up” (Vecera &Farah, 1997) 20 Illusions are fun…. But their real value is that they reveal shed light on what is stored about object structure 21 22 5 • Feature theories: pat-terns consist of a set of specific features or attributes • Advantages: – elementary features can combine to form multiple objects • Problems: – context effects in perception – recognition can take place when features are occluded 47 Prototype Theories of Pattern Recognition • Individual instances are not stored; what is stored is an “exemplar” or representative element of a category • Recognition based on “distance” between perceived item and prototype • Nature of computation still relatively unknown 48 49 50 Theories of Object Recognition 51 Object Recognition: Key Questions • When does one object end and another begin? (grouping/segmentation) • Viewpoint independence (perception of objects as objects, regardless of view) • How do we know that that two things belong together, and how do we know that dogs are dogs and not cats? (categorization) 52 More Key Issues • The “binding problem”: if perception of a given object is distributed throughout visual areas, how does the brain combine features to produce unitary percept? • Conscious vs. unconscious perception: perception of some object or object qualities may proceed pre-consciously or without effort (a general principle) – Example: Threat 53 54 55 The Building Blocks of Object Recognition: Marr’s Representations • Primal Sketch: 2-D representation of light intensity changes, information about edges, contours, and blobs – Raw: pure light intensity changes – Full: uses information to identify shapes • 2 1/2-D Sketch: depth and orientation of visible surfaces, shading, texture, motion, binocular disparity; observer-centered • 3-D Sketch: three dimensional description of objects independent of view 56 Marr-Hildreth Algorithm • Attempts to account for development of primal sketch • Idea of “blurred repres-entations” • “Zero-crossings” identify edges within a visual image • Only ‘reliable’ zero-cross-ings are kept • Four types of tokens: edge-segments, bars, terminations, and blobs 57 Marr & Nishihara (1978) • Development of 3-D sketch based on processing of more elementary shape primitives (basic primitive is a cylinder with a major axis) • Hierarchical organization of primitives 6 • Concavities important in segmenting parts • This is a completely computational, not empirical, model 58 59 Biederman’s RBC Theory • Objects built from primitives called “geons” (n = 36) • Viewer must decide appropriate way a visual object is to be segmented into geons • Two key components of decision: – locating concavity – deciding which edge information remains invariant across different viewing angles (invariant properties like curvature, parallelism, etc.) 60 Biederman’s Recognition-by-Components Theory • Adapted from Biederman (1987) 61 Recognition-by-Components Theory • Biederman (1987): five invariant properties of edges – Curvature: points on a curve – Parallelism: sets of points in parallel – Cotermination: edges terminating at a common point – Symmetry: versus asymmetry – Collinearity: points sharing a common line 62 Biederman (1987). Participants were presented with degraded line drawings of objects. Recognition was much harder to achieve when parts of the contour containing concavity information were omitted than when other parts of the contour were deleted. This confirms the assumption that information about concavities is important for object recognition. Figures adapted from Biederman (1987). Biederman’s view is “viewpoint invariant” 63 64 65 Viewpoint-dependent and Viewpoint-invariant Theories • Biederman (1987) – The ease of object recognition is not affected by the observer’s viewpoint • Tarr (1995), Tarr and Bülthoff (1995, 1998) – Changes in viewpoint reduce the speed and/or accuracy of object recognition • Milner and Goodale (1995) – Dorsal pathway makes use of viewpoint-dependent information – Ventral pathway makes use of viewpoint-invariant information 66 Common Elements in Object Recognition Theories • Edge coding • Grouping or encoding into higher-order features • Matching to a stored “structural representation” • Access to semantic knowledge 7 67 Face Recognition • Two general theories: – Neural substrate model (module) – Specialized processing model • Face recognition can be selectively impaired (prosopagnosia), or can it? • Global (configurational) vs. local (feature-based) processing 68 69 70 71 72 73 74 Examples of “Greebles”. In the top row, four different “families” are represented. For each family, two members of different “genders” are shown (e.g., Ribu is one gender and Pila is the other). The bottom row shows a new set of Greeble figures constructed on the same logic but asymmetrical in structure. Images provided courtesy of Michael J. Tarr (Brown University, Providence, RI). 75 Speed of Greeble matching as a function of stage training and difference in orientation between successive Greeble stimuli. Based on data in Gauthier and Tarr (2002). Figure shows that, although speed increases with training, Greeble identification is still viewpoint dependent 76 Face Recognition • Probably involves specialized processing/visual expertise • Dedicated neural substrates aren’t the only answer • Hybrid model: neural substrates dedicated to configural processing 77 Disorders of Object Recognition – Visual Agnosia • Apperceptive agnosia – Object recognition is impaired because of deficits in perceptual processing • Associative agnosia – Perceptual processes are essentially intact, but object recognition is impaired partly or mainly because of difficulties in accessing relevant knowledge about objects from memory 78 Riddoch and Humphreys (2001) A hierarchical model of object recognition and naming, specifying different component processes which, when impaired, can produce varieties of apperceptive and associative agnosia.