Perception

• Recap from Year 1 - Main Visual Pathway: Geniculostriate Pathway

  • Retina -(optic nerve)-> optic chiasm -(optic tract)-> lateral geniculate nucleus -(optic radiations)-> striate cortex (V1)
  • Ganglion cells in the retina collects info to pass onto the optic nerve
  • Optic chiasm - left and right visual fields in both eyes -> split to corresponding hemispheres
  • LGN (subcortical) - cells that have similar receptive field properties as ganglion cells
  • V1 - first cortical visual area
• Recap from Year 1 - Geniculostriate Pathway: Retinal and LGN receptive fields

  • On-centre/off-surround retinal ganglion cell vs off-centre/on-surround retinal ganglion cell
  • Part of the visual array in which stimulus within it will activate it -> excitatory or inhibitory effect on ganglion cell
  • Optimal stimulus -> light that matches the size of the retinal ganglion cell on area - light that covers both areas will lead to a null response
• Recap from Year 1 - Geniculostriate Pathway: V1 receptive fields (Hubel and Wiesel, 1960s)

  • Simple cells -> receptive fields emerge from adjacent LGN field inputs
  • Most effective stimulus was a oriented bar -> orientation selectivity in striate cortex; some can be on/off-centre
  • Complex cells -> do not have spatially fixed inhibitory/excitatory regions
  • More dynamic, still shows orientation selectivity but across the visual field; receptive fields emerge from adjacent simple cell field inputs
  • Orientation selectivity -> preference: normal distribution of neural response based on orientation
• Recap from Year 1 - Geniculostriate Pathway: Columnar arrangement in V1

  • Systematic arrangement of orientation preference across the cortical surface
  • Ocular dominance (left eye, right eye, ..) columns arranged perpendicular to orientation columns
  • One set of ocular dominance and one set of orientation columns form one 'hypercolumn'
  • Hypercolumn - a cortical processing module for a stimulus that falls within a particular retinal area
• Low-Level Visual Processes - Feature Detection

  • Hierarchical model: increasing complexity from simple to complex cells -> small receptive field in V1 (simple, edges/lines), to large receptive field in V4 and IT (complex, objects/faces)
  • Hubel and Wisel (1979) -> doubts there is a single cell that recognises faces
  • What a single cell 'detects' -> high vs low contrast
  • Spikes per second and differing orientation - normal dist curve: high contrast = 2 crossings at 10 spikes; low contrast = 1 crossing at 10 spikes -> many variables impact way cell responds -> output of cell is ambiguous
• Low-Level Visual Processes - Fourier Analysis

  • Early cells as part of independent channels -> each channel conveys info contained in the image at specific spatial scale and orientation: high spatial frequencies (low scale) for visual detail; low spatial frequencies (high scale) for broad structure
  • Visual system deconstructs image into discrete channels before recombining
  • Removing high spatial frequency content -> blurry image, coarse luminance, large-scale structure
  • Removing low spatial frequency content -> lose general image -> fine luminance, small-scale detail
• Low-Level Visual Processes - Fourier Analysis

  • Deconstructing and Reconstructing images: complex signals can be constructed from simpler sinusoidal functions
  • Vision: can add together sinusoidal functions to create visual images -> decompose images into spatial frequency at differing orientation i.e., 2D Fourier Transformation, computer can reconstruct image
• Low-Level Visual Processes - Fourier Analysis: Evidence in Humans - Spatial Frequency Channels

  • Measuring contrast sensitivity function - should be able to plot CSF based on contrast and spatial frequency -> n-shaped curve
  • Contrast sensitivity varies as a function of spatial frequency
  • Sensitivity max ~2-5cpd -> higher image contrast needed to detect high and low spatial frequencies
• Low-level Visual Processes - Fourier Analysis: Spatial Frequency Channels

  • Blakemore and Campbell (1969) -> contrast threshold, adaptation
  • Ps moved contrast until just visible -> spatial frequency changed after 60s -> drop in contrast threshold - neurons habituated, less sensitive -> higher contrast needed
  • Threshold is increased (sensitivity decreased) for spatial frequencies similar to the adapting frequencies - implies existence of multiple, overlapping spatial frequency channels
  • DeValois (1982) -> contrast sensitivity of V1 cells in macaques
• Low-Level Visual Processes - Summary

  • Feature Detection - single cells convey spatially local information, maps on well with functional organisation of V1
  • Fourier Analysis - distributed coding across many cells in different channels, maps on well with responses of some V1 cells
  • Both theories don't explain high-level perception i.e., recognising objects or faces
• Low-Level Vision: David Marr's Computational Approach

  1. Computational Level -> goal of the system, purpose, problem it solves
  2. Algorithmic Level -> rules and representations that achieve this goal
  3. implementational Level -> biological mechanisms that bring about algorithm

  • Computational Vision: vision serves multiple goals (where objects are, their shapes, how to interact), each of these goals relies on numerous algorithmic steps i.e., edge detection involved in identifying object boundaries and structure
• Low-Level Vision: Edge Detection

  • Marr's Model of Object Perception: grey-level representation (info at level of photoreceptors, like pixels) -> primal sketch (object boundaries) -> 2.5D sketch (element of depth perception) -> 3D model
  • Marr and Hildreth (1980) - Edge Detection: algorithm transforms image to highlight edges, assuming edges coincide with gradients in luminance
• Low-Level Vision: Marr and Hildreth (1980)'s Edge Detection Steps:

  • Initial measurement -> luminance gradient on graph can implicitly separate edge
  • f'(x) -> peaks/valleys of intensity gradient - rate of change; difficult, have to decide intensity of peaks/valley you are interested in, susceptive to noise
  • f''(x) -> zero crossings where there is a luminance gradient, edge shown from change from positive to negative; negatively affected by high frequency noise i.e., zero crossing where no meaningful gradient exists
  • Proposed a smoothing process
• Low-Level Vision: Edge Detection - Smoothing Process

  • Equivalent to first blurring the image
  • Equivalent to removing high frequency content (i.e., analysis at coarse spatial scale)
  • Expressed as convolving image with Gaussian operator G -> each pixel is blurred with its neighbours; the sigma of G determines the blurring - greater level of sigma = greater level of blurring
  • However, this sequential process (luminance, one drop -> smoothed -> first derivative, normal dist -> second derivative, zero crossing) can be done in one, parallel step
• Low-Level Vision - Edge Detection: Laplacian of Gaussian (LoG) filter

  • Convolving image with LoG filter achieves the same steps in one operation -> provides a zero crossing
  • 2D LoG looks like an on-centre/off-surround retinal and LGN field - application of algorithm to biological mechanism
  • Filters should span a range of sizes for full range of scales and spatial frequencies - trade-off between noise removal (coaser scales, need larger filter) and edge enhancement (finer scales, smaller filter)
  • Spatial Coincidence Rule - zero crossing may just be an intensity change -> combining information
• Low-Level Vision - Edge Detection

  • Retinal and LGN receptive fields can be considered as spatial filters that compute the second derivative of an image
  • Location of zero crossing in the output can be represented by simple cells in V1 -> presence of luminance gradient in terms of orientation preference
• Low-Level Vision - Rapid Edge Detection

  • Paradiso and Nakayama (1991) - temporal mask paradigm; Ps view luminance target followed by annular mask -> Ps report a composite image -> what they perceive; presence of mask disrupts "filling in" process
• Low-Level Vision - Edge Detection: Conclusion

  • Low-level visual processing can be characterised in the wider context of vision's computational goals
  • Computational Level - detecting an object's boundary and perceiving its structure
  • Algorithmic Level - Marr and Hildreth's model: convolving the image with a set of LoG filters and detecting the zero crossings
  • Implementational Level - retinal and LGN cells perform the filtering, and simple cells in V1 detect zero crossings
  • But, more complex edges exist -> occurrence of luminance-based edges
• Low-Level Vision - First- and Second-Order Edge Perception

  • First-order edges -> defined by luminance gradient
  • Second-order edges -> no luminance difference, edges invisible to Marr-Hildreth model, defined by 'texture'
  • Julesz (1981) and 'texture segmentation' -> model based on local conspicuous image features -> textons
  • Influenced by feature detection and pre-attentive visual search -> individual neurons responding to specific areas within the visual field
  • Conspicuous features: oriented lines; line terminations; junctions (T and X)
• Low-Level Vision - First- and Second-Order Edge Perception

  • Bergen and Julesz (1983): effortless segmentation - differences in conspicuous element; difficult segmentation - no difference
  • Nothdurft (1985) -> similarities between luminance segmentation (first-order) and texture segmentation (second-order): luminance segmentation - more difficult as element spacing increases; texture segmentation - more difficult with spacing and shortened length - not consistent with texton model
  • Suggests a mechanism more similar to an edge detection mechanism sensitive to spatial scale and orientation
• Low-Level Vision - First- and Second-Order Edge Perception: Texture Gradient

  • Nothdurft drew similarities between luminance and texture segmentation -> may be achieved by evaluation of a gradient
  • For a given textural property (orientation), the determinant of segmentation performance is not simple the difference in that property from foreground to background, but the spatial gradient of the property across the texture boundary
• Low-Level Vision - First- and Second-Order Edge Perception: Texture Segmentation

  • Bergen and Adelson (1988) - can be impaired/enhanced by changing the sizes of the elements -> suggests that simpler filtering processes can account for segmentation
  • Computational models of texture segmentation: spatial differences in orientation and spatial frequency statistics
  • Neurophysiology (Lamme, 1999) -> single cell recording in V1 of macaques: response enhancement at texture figure and edge, develops after initial response peak (beyond V1) -> supports edge-based segmentation process -> filling in
• High-Level Vision - Visual Agnosia

  • Agnosia - a lack of knowledge; perception -> can see features i.e., orientation, colour, but cannot identify object
  • Associative - complete perception with inability to link object to memory
  • Apperceptive - disorder to perception, specific visual impairment, can recognise objects through touch
  • Integrative - not as simple as dissociable associative and apperceptive
• High-Level Vision - Visual Agnosia

  • Lissauer (1889) - first to identify visually agnostic patient; distinguished between two stages of recognition: apperceptive and associative -> impairments to visual perception rather than impairments of intellect
  • Associative agnosia - 'normal percept stripped of its meaning' (Teuber, 1968) -> patient can copy model; visual perception is intact
  • Apperceptive agnosia - impairment in conscious visual representations -> cannot make a copy of a model
• High-Level Vision - Integrative Agnosia

  • Riddoch and Humpherys (1987) - patient HJA passes apperceptive agnosia tests (copying) but shows higher order perceptual impairments
  • Reaction time for overlapping objects impaired
  • Impaired at discriminating real vs unreal objects
  • Performs better with fewer details i.e., drawings vs silhouettes
  • Potentially due to integration information across space
  • Indicates something more complex than a simple apperceptive/associative dissociation
• High-Level Vision - Integrative Agnosia

  • Describes a high-level perceptual impairment in integrating the form and features of an object
  • Birmingham Object Recognition Battery - series of tests to identify level of processing at which recognition is impaired
  • Low-level features - size, length, orientation
  • Figure/ground formation - integrating info about overlapping features
  • Viewpoint invariant representation - identify object from other angle
  • Stored knowledge - real/unreal, describe/draw written work
  • Knowledge of function and between-object representation - semantically similar objects
• High-Level Vision - Associative Agnosia

  • Associative agnosia may be explained by subtle, low-level sensory impairments
  • Ettlinger (1956) -> cerebral lesions either with or without visual field impairments and/or agnosia: those with just visual field defect impaired performance, those with both perform at a similar level
  • Impairments in visual sensory abilities associated with visual field defects - but tests used didn't fully account for functional organisation
  • Dissociable visual 'features': lightness, colour, movement, texture, shape, etc
• High-Level Vision - Associative Agnosia

  • DeHaan (1995) repeated Ettlinger (1956) - focus on agnosia and taking visual abilities into consideration, using more appropriate tests: shape, location, colour, texture, and lightness discrimination, shape from motion, and line orientation
  • Group A - Agnosia -> some impairment, no marked difference to controls, some patients impaired in only one domain
  • Group B - No Agnosia -> most significant impairments
  • No evidence that these visual functions are necessary/sufficient enough to cause agnosia -> not dependent on low-level visuosensory impairments
• High-Level Vision - Form Agnosia

  • Is apperceptive agnosia dependent on low-level visuosensory impairments - one example of apperceptive agnosia is visual form agnosia
  • Mr S (Benson and Greenberg, 1989) - failed basic apperceptive agnosia tests (copying, matching objects) -> impairment seemed specific to visual form perception -> could make discriminations based on overall luminance, colour
  • Term agnosia is questionable - not a lack of knowledge
• High-Level Vision - Form Agnosia

  • Specific and selective impairment
  • Marr's Model (1982) -> impairment in primal sketch (edges, contours), which leads to a lack of object perception
  • Low-level? Performance generally intact with common small impairments
  • Campion and Latto (1985) - contrasts sensitivity in an agnosic (RC) - abnormal thresholds indicating a low-level sensory deficit (spatial frequency and orientation)
  • Peppery fields defects - losses of very small parts of the visual field
• High-Level Vision - Form Agnosia

  • Peppery Field Defects: fine grained perimetry - rating brightness of single dots -> islands of visual loss shown
  • Agnosia explained peppered field defects (scotoma) -> loss of conscious experience in small parts of visual field -> not form-specific impairment
  • Simulated visual field losses in neurotypicals using mask -> contrast thresholds similar to agnosia
• High-Level Vision - Functional Organisation: Ventral and Dorsal Stream

  • Ungerleider and Mishkin (1982) - lesioned monkeys: temporal - poor object recognition; parietal - poor spatial judgement
  • Original framework:
  • Dorsal - 'where', spatial location of objects and movement
  • Ventral - 'what', recognising objects, properties, and complex objects i.e., faces
  • Milner and Goodale (1991) - different dissociation: DF profound visual form agnosia accompanied by other deficits (brightness, motion, depth), but largely intact low-level vision - damage in occiptal-temporal cortex (ventral stream)
• High-Level Vision - Functional Organisation: Ventral and Dorsal Stream

  • Milner and Goodale (1991) - DF did not have conscious access to visual object information but could use it to support different tasks: matching - could not match card to orientation of postbox; posting - little difficulty in action -> conscious visual access to matching vs completing action
  • Dorsal - visuomotor interaction, egocentric, no access to memory, unconscious
  • Ventral - object recognition, access to memory, conscious, allocentric
  • Visual agnosia not explained by lower level deficit
• High-Level Vision - Functional Organisation: Dissociation in Neurotypicals

  • Malach (1995) - fMRI in response to images of objects or textures: response in lateral occipital cortex in response to objects, response does not distinguish familiarity, dissociation between object- and non-object-recognition
  • Culham (2003) - fMRI in visually guided grasping: response in anterior intraparietal sulcus, not associated with object recognition - intact in DF even though they do not have conscious access
  • Evidence of neural dissociation in neurotypicals
• High-Level Vision - Functional Organisation: Dissociation in Neurotypicals

  • Aglioti (& Goodale) (1995) - used visual illusions to make central circles appear larger or smaller, measured maximum grip aperture during visually guided grasp
  • Maximum grip aperture scales with physical size, not perceived size -> can change the perceptual experience (ventral) but this does not change the interaction with visually guided experience
  • Some interaction between ventral and dorsal to guide visually guided actions -> DF had issues in posting complex shapes i.e., T
• High-Level Vision - Modular Processing

  • Faces are processed in a unique, holistic way -> only for upright faces and not for any non-face objects (Thompson, 1980)
  • Damage to the ventral system can result in prosopagnosia - can be unimpaired in recognising other objects
  • Kanwisher (1997) - modular: ventral stream is organised to recognise faces - activity in Fusiform Face Area, not explained by differences in low-level features and attention effects
• High-Level Vision - Modular Processing

  • Recognising exemplars/expertise effect (Geuthier (2000) -> expects in other object recognition (i.e., birds, cars) get activation in FFA -> discrimination of differences
  • Expertise effects not restricted to FFA and can be confounded with attention (Harel et al., 2010)
  • Can subdivide sections of the ventral system to recognise different objects i.e., faces, places, limbs -> maps on well to specific impairments/case studies
• Low-Level Hearing - Sound Waves

  • Auditory Perception begins with sound waves
  • Sound waves are variations in air pressure
  • Sound waves are longitudinal waves
  • Wave form graph - increases and decreases in presuure
  • The physical properties of the sound wave determine perceptual qualities
  • Amplitude - loudness; quieter/softer when there is less pressure
  • Frequency - pitch
  • The relationship between physical and perceptual qualities is not entirely straightforward
• Low-Level Hearing - Fourier Analysis

  • Everyday waveforms are not sinusoidal -> any complex sound waveform can be created using a finite number of sinusoids -> infer that the early auditory system breaks down the sound waves into several sinusoids
  • Wave forms often plotted as frequency spectra, showing the level of each frequency present in a sound
  • Lowest frequency present is called the fundamental frequency, f(0), e.g., 200Hz
  • Harmonics are integer multiples of f(0) i.e., 400Hz, 600Hz
  • Spectrograms show frequency spectrum over time - shifting frequency up -> perception of increased pitch
• Low-Level Hearing - Auditory Pathway

  1. Delivering the sound stimulus to the receptor
  2. Converting the physical stimulus into an electrical signal
  3. Inferring perceptual qualities (e.g., loudness, pitch) from electrical signals