Visual Perception Week 4 Notes
Visual Perception Week 4 Notes PSYC 3124
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This 20 page Class Notes was uploaded by Freddi Marsillo on Thursday February 4, 2016. The Class Notes belongs to PSYC 3124 at George Washington University taught by Dr. John Philbeck in Spring 2016. Since its upload, it has received 78 views. For similar materials see Visual Perception in Psychlogy at George Washington University.
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Date Created: 02/04/16
Visual Perception Week 4 Notes 02/02/2016 16.12.00 The Retinal Projection to the Brain Subcortical targets of the retinal output: Lateral geniculate nucleus (LGN) = in thalamus Superior colliculus LGN to visual cortex Signal splitting at the optic chiasm: Nasal fibers cross over General layout of the retinal projection: Signal splitting at the optic chiasm: o Each optic nerve carries signals from each eye o Each optic tract carries signals from half of the retina of each eye o Each hemisphere processes visual information from the opposite side of the visual field The foveal representation: o It is unlikely that projections from the fovea are partitioned in a perfect midline o It is more likely that projection patterns along entire midline, including the fovea, is fuzzy – some ganglion cells project along ipsilateral pathway and others along contralateral pathway The lateral geniculate nucleus (LGN): Structural and functional properties: o STRUCTURE: Six layers: Bottom two layers contain magnocellular neurons Top four layers contain parvocellular neurons FUNCTION: LGN neurons have receptive fields with concentric circular pattern (ON/OFF and OFF/ON) LGN is a paired neural structure located deep inside the brain Magnified view shows that neurons are largely confined to six major layers Layers are numbered from the bottom up; bottom two layers are distinct from the top four layers Organization of visual signals – retinotopy: o LGN neurons in each layer are monocular o Topographic layout of retina is formed in each layer of LGN o Foveal retina has greater representation in LGN, while peripheral parts of the retina have less Organization of visual signals – functional segregation: o Parasol ganglion cells project to magnocellular layers of LGN, and midget ganglion cells project to parvocellular layers of LGN o Magnocellular neurons are more tuned to light contrast levels o Parvocellular neurons convey information about color contrast Regulation of information flow: o LGN neurons not only send signals to visual cortex, o LGN neurons also receive signals from visual cortex as to which signals to amplify in response to a greater attentional interest The superior colliculus: Resides below LGN Pathway through pulvinar projects to visual cortex: o Can account for blindsight – the ability to respond to visual stimuli without consciously perceiving them – this condition can occur after certain types of brain damage Receives input from visual cortex, as well as somatosensory and auditory systems Major output to motor areas of brainstem to control saccades The Primary Visual Cortex Located in the occipital lobe First cortical area to process visual information Also known as striate cortex, area 17, and area V1 (V1 means the first visual cortical area in the brain) Structure and layout of area V1 – six layers: The retinopic layout of area V1: o Fovea enjoys greater cortical area than peripheral parts of the retina Cortical magnification: refers to the fact that the number of neurons in the visual cortex responsible for processing the visual stimulus of a given size varies as a function of the location of the stimulus in the visual field Properties of area V1 neurons: The emergence of binocularity: o Many area V1 neurons can be activated by light stimulation of either eye or both eyes together o Ocular dominance scale: Individual neurons show different degrees of preference for one particular eye or both eyes Most neurons in area V1 show some degree of binocular influence Orientation selectivity: o ON and OFF subfields in area V1 are rectangular rather than circular Orientation selectivity: An elongated light stimulus will trigger maximum activity only in those neurons with a similar receptive field orientation For a neuron with a vertically elongated receptive field, firing rate will increase as the light bar is more vertical Orientation tuning curve: o Plot neuron’s response profile as a function of the light bar’s orientation Neural response of an orientation-selective neuron depends on relationship of the light bar’s orientation to that of the RF Firing profiles show that vertical bar optimally stimulates a neuron with a vertical RF orientation; minimum firing occurs when bar is horizontal Summary of firing rate as a function of bar orientation is known as the orientation tuning curve Properties of V1 Neurons Directional motion selectivity: Some neurons in area V1 show significantly greater firing to stimulus movement in one direction in comparison to all others However, some neurons are non-directional The Primary Visual Cortex Functional architecture of area V1: Ocular dominance columns: o A vertically oriented collection of neurons spanning the entire thickness of area V1 that shows preference for light stimulation of a particular eye Orientation columns: o Neurons with similar orientation preferences are clustered together in vertically oriented columns o There are discrete shifts in orientation preferences from one column to the next o Series of columns together represents all possible orientations Neural projections for LGN arrive into discrete interdigitated sectors of layer 4C in area V1 Patches alternate in terms of eye preference due to right eye versus left eye projections Anatomical patterns produce vertically oriented columns of eye preference, known as ocular dominance columns (ODCs) The ice cube model proposes that ODC and orientation columns are situated perpendicular to each other Ice cube model: ocular dominance columns run in one direction, whereas the orientation columns are arrayed along a perpendicular axis Hypercolumn: a cortical module that encompasses one pair of ocular dominance columns and a complete series of orientation columns Visualizing architecture of area V1 – functional anatomy: Optical imaging: o Generates maps which display areas of high neural activity in the visual cortex in response to activation by a stimulus o Indicates brain areas that are more active reflecting less light than inactive areas o Can look at how activity changes with different stimuli Ocular dominance columns do not run in straight lines like the ice cube model but have a meandering quality (black and white patches in image below Orientation columns do not run in straight lines but appear as a series of pinwheel patterns Optical imaging can be used to reveal both ocular dominance and orientation columns (left panel) Enlarged view of orientation columns show that they appear as a pinwheel pattern on top of cortex where multiple columns converge into a central core Pinwheel segments extend down through the thickness of the cortex to make up a series of vertically oriented orientation columns Visual information processing beyond the occipital lobe splits into two major pathways: Dorsal stream: the “where” pathway in the parietal lobe Ventral stream: the “what” pathway in the temporal lobe Higher Cortical Functions and Object Perception Dorsal cortical stream: Signal output from area MT to the parietal lobe Involved in the visual coordination of body and eye movements and in encoding spatial relationships The “where” pathway Ventral cortical stream: Signal output from area V4 to the temporal lobe Involved in processing object detail and identity The “what” pathway Visual areas in the temporal lobe depicted below: Object perception: Agnosia: a disorder in which people have difficulty recognizing objects due to selective damage to the ventral visual pathways o Patients with agnosia are unable to compare and match different structures o Another type of agnosia spares perceptual function but produces an inability to name the structure Structuralism: the psychological theory that mental experiences result from the assembly of elemental structural units that can be deduced through careful introspection Gestalt theory of object perception: object perception has the intrinsic quality that was based on the wholeness of structure that could not be reduced to its constituent parts Law of similarity: similar items appear to be grouped together because they share common features Closure: a single closed pattern can obscure its components According to Gestalt law of similarity, items that are similar in nature appear to be grouped together to create form Interacting items can create a closed pattern, according to Gestalt principle of closure Law of proximity = near items appear to be grouped together Law of simplicity = items are organized into figures in the simplest way possible Law of good continuation = the tendency to perceive clusters of individual elements as forming a single contour Law of common fate = items moving in the same direction are grouped together Kanizsa figures: shows importance of holistic mechanisms; sensory analysis at the structural level cannot account for certain features of the figures Kanizsa figures show structure when it is not explicitly defined Neither the white triangle nor the cube actually exists in either image, but they are instead created by the mind Figure-ground segregation: a salient figure or foreground impression stands out and is distinguishable from background stimulation o Vase-face image has two possible interpretations Relationship between figure and ground can alternate in ambiguous situations Ruben’s vase figure (left picture) alternates between a white vase and two opposing black faces Past experiences also affect perceptual organization Vase more salient if figure is inverted (when faces are upside down) o Issue of familiarity – we are not used to seeing faces upside down, they are not as familiar to us, so the vase stands out more Dalmatian instantly perceived once you have seen it Modern structural theories Gestalt principles are regarded not as laws but as heuristics: the most plausible solution to a particular problem given the circumstances; “rules of thumb” Feature integration theory (FIT) is based on the assembly of low- level features into a complex visual object 1) Pre-Attentive Stage: Basic characteristics of the features in the pre- attentive map can be identified with the pop-out phenomena: some features immediately pop out regardless of the number of other distracters Differences in elementary features such as orientation or contrast immediately pop out and can be quickly detected regardless of figure density More complex forms, such as characters or numerals, require an attention-driven search; the greater the number of distracters, the longer the search time 2) Attentional Stage: at this stage, feature integration is believed to take place The binding problem = how elementary tokens are assembled into a visual object (binding requires attention and takes time) Once the features are bound together, the resulting object is compared to memory – a positive match then leads to identification Modern structural theories Recognition-by-components theory (RBC) says that visual objects are initially parsed into simple geometric volumes that are later assembled to create a 3D representation Basic features are volumetric primitives called geons These are examples of geons, along with some common objects that can be assembled by various combinations of these particular geons Analytical approaches – David Marr’s computational algorithm: Early stages of vision: Edge detection algorithm creates spatial primitives composed of edges, lines, blobs, and terminations o Fits nicely with contrast-detecting functions of early visual neurons Next stage: Links primitive features into larger ones and groups similar elements together: o Produces a representation of an object’s surface and layout, then is transformed into a 3D representation Edge detection algorithms can be applied at different spatial scales Broad changes in intensity provide course resolution (top right pictures), intermediate changes (bottom left), and abrupt intensity changes identified at fine resolution (bottom right) The three spatial levels of analysis allow an algorithm to pick out sharp borders, as well as broad intensity changes (e.g., shadows and highlights) Face perception Neural processing of faces: Prosopagnosia = inability to recognize faces caused by impairment in sensory processing of high-level visual functions in the temporal lobe Single neurons in the monkey temporal lobe are responsive to face stimuli in a highly specific manner fMRI studies with humans show increased activity in the fusiform face area (FFA) o = Area of brain; functional activity in human brain in response to faces shows a focus of activation in the fusiform gyrus Perceptual aspects of face processing: Mental representations of faces are fundamentally holistic in nature (supports Gestalt theory) Faces and objects may be treated differently by our visual system (familiarity) Inverted faces are hard to recognize Holistic nature of face perception can be demonstrated with overlapped face images Even though features of the two faces blend into each other, each face is distinctly visible as a whole – the same is not true with non- face objects, such as overlapped houses 02/02/2016 16.12.00 02/02/2016 16.12.00
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