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Rosenblatt's Perceptron (1958)
Early attempt to replicate human perception using computers.
Room-sized machine designed to distinguish basic visual patterns (e.g., markings on cards).
Mimicked brain processes in pattern recognition but struggled with complex tasks.
Required 50 trials to complete simple distinctions, revealing perception's complexity.
Initial enthusiasm faded but resurfaced in the 1980s, influencing modern AI research.
Human Perception vs. Modern Computer Vision
Computers struggle with nuanced understanding and context-awareness.
Example: A computer might label a scene as "a plane on a runway" but miss surrounding context, like an airshow.
Humans excel because perception integrates sensory data with years of experiential knowledge.
Despite AI advancements, human perception remains more accurate and adaptable.
Misconceptions About Perception
Early researchers underestimated its complexity, believing it could be replicated quickly.
Perception feels automatic but involves intricate brain mechanisms and sensory processing.
Both the biological and interpretative aspects make it challenging to mimic.
The Study of Perception
Aims to understand how humans and animals sense and process environmental stimuli.
Explores the steps of perception, its practical applications, and methods to measure it.
Advances in perception research contribute to bridging gaps between biological systems and artificial intelligence.
Key Insights
Perception is more than sensory input—it involves interpreting and contextualizing stimuli.
Early models like the Perceptron laid the groundwork for AI but highlight human perception’s complexity.
Ongoing research is essential for deeper understanding and technological advancements.
1.3
Overview of Perception
Perception is a journey from environmental stimuli to behavioral responses (e.g., perceiving, recognizing, and acting).
The process applies to all senses, not just vision.
Simplified as seven steps that represent key events in perception, shown in Figure 1.4.
Steps in the Perceptual Process
Stimulus in the Environment (Step 1):
Begins with an external stimulus (e.g., a tree).
Known as the distal stimulus.
Stimulus on Receptors (Step 2):
Light reflecting from the stimulus creates an image on the retina, called the proximal stimulus.
Receptor Processes (Step 3):
Sensory receptors respond to the stimulus through transduction (conversion of energy forms).
Shaped by receptor properties, influencing perception.
Neural Processing (Step 4):
Electrical signals travel through networks of neurons.
Involves interactions between neurons and processing in different brain areas.
Behavioral Responses (Steps 5–7):
Perception: "I see something."
Recognition: "That’s a tree."
Action: Taking action based on perception, like walking closer to inspect the tree.
Dynamic Nature of the Perceptual Process
Steps don’t always occur sequentially:
Perception and recognition can happen simultaneously or in reverse order.
Action can influence perception and recognition (e.g., a closer look reveals the tree is a maple, not an oak).
Bidirectional arrows represent feedback loops in the process.
Action can modify the stimulus, creating a cycle (e.g., walking toward the tree changes the view of it).
Simplification of the Model
Figure 1.4 is a simplified representation:
Many complex events occur within each "box" (e.g., neural processing involves detailed neuron interactions).
The process is more dynamic and interconnected than the diagram suggests.
Importance of Knowledge in Perception
Knowledge within the brain influences all steps.
Past experiences shape how stimuli are perceived, recognized, and acted upon.
Applications and Measurement
Figure 1.4 serves as a framework to explore perception principles.
The chapter will cover:
Detailed descriptions of each step.
Methods to measure relationships between stimuli and perception.
Notes on Distal and Proximal Stimuli (Steps 1 and 2)
Distal Stimulus:
The external object in the environment (e.g., a tree).
Called "distal" because it is distant from the sensory receptors.
Perception relies on the effects of the stimulus (e.g., light or sound) reaching the receptors, not the stimulus itself.
Proximal Stimulus:
The representation of the distal stimulus on sensory receptors.
Example: Light reflecting from a tree forms an image on the retina.
"Proximal" indicates it is close to the receptors.
Principle of Transformation:
Stimuli and responses change during the journey from the distal stimulus to perception.
Example:
Light reflects from the tree, influenced by environmental conditions (e.g., sunlight, atmospheric clarity).
The reflected light enters the eye, where it is further transformed by the optical system and focused on the retina.
Principle of Representation:
Perception is based on representations of stimuli, not direct contact.
The proximal stimulus (e.g., the retinal image) represents the distal stimulus.
Receptor Processes (Step 3)
Sensory receptors respond to specific types of environmental energy.
Examples:
Visual receptors respond to light.
Auditory receptors respond to air pressure changes.
Touch receptors respond to pressure on the skin.
Smell and taste receptors respond to chemicals entering the nose/mouth.
Receptors perform two functions:
Transform environmental energy into electrical energy (transduction).
Shape perception based on properties of the stimuli.
Transduction allows environmental information to be converted into a form the brain understands.
Sensory receptors act as a bridge between the external world and neural representation.
Neural Processing (Step 4)
After transduction, electrical signals from receptors travel through neurons to the brain.
Neural processing involves:
Transmitting signals to the brain.
Amplifying or reducing signals based on neuron interactions.
Signals are sent to primary receiving areas in the cerebral cortex:
Vision: Occipital lobe.
Hearing: Temporal lobe.
Touch/temperature/pain: Parietal lobe.
Signals then move to other brain areas, like the frontal lobe, for integration across senses.
The brain’s processing changes signal patterns but retains their representation of the stimulus.
Behavioral Responses (Steps 5–7)
Perception (Step 5): Conscious awareness of the stimulus (e.g., recognizing a tree).
Recognition (Step 6): Categorizing the stimulus to give it meaning.
Example: Dr. P., with visual agnosia, could perceive parts of objects but not recognize the whole.
Action (Step 7): Motor responses to stimuli, such as walking toward or interacting with an object.
Perception often leads to action for survival (e.g., navigation, avoiding obstacles).
Perception and action are dynamic processes that continuously change based on new stimuli or perspectives.
Knowledge and the Perceptual Process
Definition of Knowledge: Information the perceiver brings to a situation, based on prior experience or expectations.
Knowledge influences multiple steps of the perceptual process.
Demonstration: Perceiving a Picture
Recently acquired knowledge can affect perception:
Example: "Rat–man demonstration" shows how prior exposure (e.g., seeing a rat-like figure) influences perception.
Long-term knowledge influences categorization (e.g., naming objects like "tree" or "bird").
Bottom-Up vs. Top-Down Processing
Bottom-Up Processing (Data-Based):
Starts with stimuli reaching receptors.
Example: Seeing a moth triggers visual receptors.
Top-Down Processing (Knowledge-Based):
Relies on prior knowledge and experiences.
Example: Identifying the moth as a specific type using learned information.
Key Insight
Perception combines bottom-up and top-down processes.
Past experiences shape our understanding of real-world scenes, often unconsciously.
1.4
1.4 Studying the Perceptual Process
To simplify the seven-step perceptual process, it can be broken into three main components:
Stimulus: Distal and proximal stimuli (Steps 1–2).
Physiology: Receptors and neural processing (Steps 3–4).
Behavior: Perception, recognition, and action (Steps 5–7).
The goal of perceptual research is to understand the relationships between these components, represented as:
Arrow A: Stimulus–Behavior relationship.
Arrow B: Stimulus–Physiology relationship.
Arrow C: Physiology–Behavior relationship.
The Stimulus–Behavior Relationship (Arrow A)
Definition: Examines how stimuli lead to behavioral responses like perception or action.
Example: Psychophysics explores how physical stimuli relate to psychological responses.
Oblique Effect:
People perceive vertical and horizontal lines better than oblique lines.
Grating acuity (smallest detectable line width) shows this effect.
Vertically and horizontally oriented gratings are detected better than oblique ones.
The Stimulus–Physiology Relationship (Arrow B)
Definition: Investigates how stimuli cause physiological responses, like neural activity.
Example: Coppola et al. (1998) used optical brain imaging on ferrets.
Horizontal and vertical orientations triggered stronger brain responses compared to oblique orientations.
The Physiology–Behavior Relationship (Arrow C)
Definition: Links physiological responses to behavioral outcomes.
Example: Furmanski and Engel (2000) studied participants' brain activity and sensitivity to gratings.
Behavioral sensitivity was higher for vertical and horizontal orientations.
Functional MRI (fMRI) showed greater brain activity for these orientations.
Key Insights
Interconnected Relationships: Each relationship—stimulus to behavior, stimulus to physiology, and physiology to behavior—provides unique insights.
Combined Analysis: Understanding perception requires studying both behavioral and physiological responses together.
Influence of Knowledge: Perception is shaped by prior experiences, memories, and expectations, as demonstrated by phenomena like the rat–man experiment.
A comprehensive understanding of perception comes from analyzing all three relationships while considering how knowledge-based processing affects perception.
1.5
Absolute Threshold
The smallest detectable stimulus level (e.g., faintest sound heard, weakest salt taste in stew).
Difference Threshold
The smallest detectable difference between two stimuli (e.g., differentiating saltiness between two stews).
Fechner’s Contributions to Psychophysics
Gustav Fechner introduced psychophysics, linking physical stimulation (body) to perceptual experiences (mind).
Proposed methods for measuring thresholds, enabling the scientific study of perception.
Fechner’s Classical Methods for Determining Absolute Threshold
Method of Limits: Stimuli presented in ascending or descending order, with the threshold calculated as the average crossover point where the response changes from "yes" to "no."
Method of Constant Stimuli: Stimuli of varying intensities presented in random order, with the threshold determined as the intensity detected in 50% of trials. This is the most accurate method but is time-consuming.
Method of Adjustment: Participants adjust the stimulus intensity themselves until it is barely detectable. This method is faster but less precise.
Applications of Threshold Measurements
Historical Significance: Fechner’s methods laid the foundation for scientific psychology.
Practical Use: These methods are still used today across various sensory systems and conditions.
Beyond Absolute Thresholds
Researchers study experiences above threshold levels to fully understand perception.
Example: Dark adaptation, where the ability to see in the dark improves over time as thresholds for light detection decrease.
Key Takeaways
Thresholds quantify the limits of sensory systems and allow researchers to study perceptual mechanisms scientifically.
Fechner’s innovations established the study of perception as a measurable science, integrating the physical and psychological domains.
Summary of Measuring Perception Above Threshold
This section explores methods used to measure sensory experiences when stimuli are above threshold levels, focusing on five key questions about the perceptual world and techniques used to answer them.
Question 1: What Is the Perceptual Magnitude of a Stimulus?
Technique: Magnitude Estimation
Participants compare stimuli to a standard (e.g., loudness, brightness).
They assign proportional numerical values to the perceived magnitude of stimuli.
Example: A sound perceived as twice as loud as the standard receives a value of 20 if the standard is rated as 10.
This technique is versatile, applicable across senses like hearing, vision, and touch.
Question 2: What Is the Identity of the Stimulus?
Technique: Recognition Testing
Involves identifying or naming objects or stimuli.
Useful for studying brain-damaged individuals and understanding recognition processes.
Recognizing objects is critical for survival, emphasizing the transition from perception ("What do you see?") to recognition ("What is that called?").
Question 3: How Quickly Can I React to It?
Technique: Reaction Time
Measures the time between stimulus presentation and response.
Reaction time is faster when attention is directed toward the stimulus location.
Relevant for tasks like driving, where attention affects reaction speed.
Question 4: How Can I Describe What Is Out There?
Technique: Phenomenological Report
Participants describe what they see, such as objects, colors, spatial arrangements, or patterns.
Used to identify perceptual phenomena, such as figure-ground relationships (e.g., vase-face illusion).
Provides a foundation for further research using other methods.
Question 5: How Can I Interact With It?
Technique: Physical Tasks and Judgments
Focuses on actions resulting from perception, such as reaching, navigating, or driving.
Explores how perception enables interaction with the environment.
Includes tasks where judgments about actions (e.g., distance estimation) are assessed.
The Difference Between Physical and Perceptual
Physical stimuli (e.g., light intensity) do not directly equate to perceptual experiences (e.g., brightness).
Perceptual responses often exhibit non-linear relationships with physical stimuli.
Example: Doubling the physical intensity of light does not double perceived brightness.
The electromagnetic spectrum highlights the limits of human perception, as we can only perceive visible light within a narrow range.
Key Takeaways
Behavioral and physiological methods combine to deepen our understanding of perception.
Studying perception above thresholds provides insights into how we interpret and interact with the sensory world.
The way electrical signals travel through the nervous system is compared to two cars on different routes. While Car A takes a direct express highway, Car B takes a scenic route, stopping at various points that influence the journey. This analogy illustrates how electrical signals in the nervous system don't follow a straight, uninterrupted path but instead travel through a complex network of interconnected signals.
Key Points:
Car A (Expressway vs. Car B (Scenic Route))
Car A’s journey is a direct path with minimal stops, like a simple signal traveling from receptor to brain.
Car B’s journey is slower, with stops and detours, representing a more complex process of neural signals interacting with others along the way.
Why a Complex, Indirect Route?
A direct signal from receptors to the brain would simply tell the brain that a receptor has been activated. However, the brain needs more detailed information for perception.
Signals take indirect paths, interacting with other neurons along the way, leading to richer, more complex information by the time it reaches the brain.
Neural Processing:
The nervous system does not simply transmit information in a straight line; it involves complex processing where neurons interact.
This interaction allows the brain to process specific stimuli, such as slanted lines, faces, or movement in a particular direction.
This neural processing is crucial for creating our perceptual experiences.
Importance of Understanding Neural Responses:
Understanding how individual neurons respond and how large networks of neurons process signals is key to comprehending how we perceive the world.
Neural processing creates richer, more detailed perceptual experiences, rather than just basic signals.
2.1
Electrical Signals in Neurons
Neurons, such as those in the figure, consist of:
Cell body: Contains mechanisms to keep the cell alive.
Dendrites: Receive electrical signals from other neurons.
Axon: Conducts electrical signals and is filled with fluid.
Variations exist in neuron structure, with some having long axons, others with short axons or none at all.
Sensory receptors: Neurons specialized to respond to environmental stimuli, such as touch.
Neurons do not exist in isolation; they are connected to many other neurons.
The activity of individual neurons contributes to perception.
Recording Electrical Signals in Neurons
Electrical signals are recorded using small electrodes placed in the axon.
The setup typically includes:
Recording electrode: Placed inside the neuron.
Reference electrode: Placed at a distance from the neuron.
These electrodes are connected to a meter to record the difference in charge.
At rest, the axon’s electrical potential is -70 millivolts (mV), indicating the inside is 70 mV more negative than the outside.
When the neuron’s receptor is stimulated, an electrical signal travels down the axon, causing a rise to +40 mV and eventually returning to the resting potential.
Basic Properties of Action Potentials
The action potential is a propagated response, traveling down the axon without decreasing in size.
The action potential’s size remains constant regardless of the stimulus intensity.
Increasing stimulus intensity increases the rate of firing, but not the size of the action potential.
There is a limit to how quickly a neuron can fire due to the refractory period, which is typically 1 millisecond.
Spontaneous activity allows action potentials to occur even without environmental stimuli.
Neurons can have baseline activity that increases or decreases based on stimulation.
Chemical Basis of Action Potentials
Action potentials generate electricity within the body’s liquid environment.
The key to understanding action potentials is the movement of ions like sodium (Na+) and potassium (K+).
At rest, the neuron is filled with potassium inside and sodium outside.
Depolarization: Sodium ions rush into the axon, causing the inside to become positive (+40 mV).
Hyperpolarization: Potassium ions exit the axon, making the inside negative again.
The sodium-potassium pump maintains the ion distribution, preventing the buildup of sodium inside and potassium outside.
Transmitting Information Across a Gap
When the action potential reaches the end of the axon, neurotransmitters are released into the synapse.
These neurotransmitters cross the synapse and bind to receptors on the next neuron.
Depending on the neurotransmitter, the next neuron may:
Excitatory: Causes depolarization and increases the likelihood of firing.
Inhibitory: Causes hyperpolarization and reduces the likelihood of firing.
The balance between excitatory and inhibitory signals determines the neuron’s firing rate and influences the overall response to stimuli.
2.2
Sensory coding refers to how neurons represent characteristics of the environment, such as the taste of salt. The question arises whether specific neurons represent individual sensory experiences (specificity coding), or if multiple neurons and patterns of firing are involved (sparse coding and population coding).
Specificity Coding:
Neurons can represent specific sensory information, such as the taste of salt, through a specialized neuron.
This concept dates back to the 1960s and suggests that there could be a neuron for each concept, such as a “grandmother cell” for a person’s grandmother.
Neurons might be so specific that they fire in response to a concept, such as a famous person’s face, regardless of how that face is presented.
Evidence from studies of patients with epilepsy undergoing brain surgery suggests that some neurons do indeed respond to a specific concept, such as a famous person like Steve Carell or Halle Berry.
However, this does not prove the existence of “grandmother cells” and is not widely accepted in neuroscience due to biological implausibility and lack of confirmatory evidence.
Sparse Coding:
Proposed as an alternative to specificity coding, sparse coding involves a small group of neurons firing to represent a stimulus, with the majority of neurons remaining inactive.
For example, a few neurons might represent a particular face (Bill’s face) by firing in a pattern, while another group of neurons represents a different face (Mary’s face), and so on.
Sparse coding has been observed in visual, auditory, and olfactory systems, suggesting it may underlie how sensory information is represented in the brain.
Population Coding:
A more complex idea is population coding, where the pattern of firing across a large number of neurons represents sensory information.
Population coding allows for the representation of a wide range of stimuli because it utilizes large groups of neurons to create a diverse set of firing patterns.
This concept is supported by evidence in various sensory systems and cognitive functions.
Summary:
Perception is represented by the firing patterns of groups of neurons, with small groups (sparse coding) or large groups (population coding) involved in representing experiences such as the aroma of food or visual perceptions.
2.3
Representation in the Brain:
Perception involves more than just individual neurons or small groups of neurons. It also includes the roles of different brain areas and their connections.
Mapping Function to Structure:
The idea that different perceptual functions map to specific brain areas dates back to the 18th century, with Franz Joseph Gall and Johann Spurzheim.
Gall's phrenology theory suggested that the shape of the skull correlates with mental faculties, such as love or musical ability. While phrenology has been debunked, it introduced the concept of modularity—specific brain areas for specific functions.
Early evidence for modularity came from case studies of patients with brain damage, such as Pierre Paul Broca's discovery of Broca’s area (speech production) and Carl Wernicke’s discovery of Wernicke’s area (speech comprehension).
Brain Imaging Methods:
MRI and fMRI: MRI scans reveal brain structure, while fMRI detects brain activity by measuring blood flow. Blood flow increases in areas of the brain that are active.
fMRI works by detecting changes in the magnetic properties of hemoglobin, which carries oxygen in the blood. Active brain areas consume more oxygen, making hemoglobin more magnetic.
Voxel-based Analysis: fMRI records activity in small cube-shaped brain units called voxels, which contain many neurons. Each voxel acts like a pixel on a screen but in 3D.
Brain activity is compared to baseline activity (resting state or a different task), and the results are shown with color-coded images indicating areas of higher or lower activity.
Mapping Functions to Specific Areas:
fMRI studies have mapped certain functions to specific areas, such as the "voice area" in the superior temporal sulcus (STS), which responds more strongly to vocal sounds than non-vocal sounds.
This research supports the modular view, suggesting specific brain areas are specialized for certain functions, like speech perception.
Limitations of Modularity:
Though specific areas may be specialized, perception often involves networks of brain regions distributed across the cortex. The brain's representation can be more complex than simple modularity.
Distributed Representation:
Brain represents information in patterns spread across multiple areas, rather than in one single area.
Introduced by Geoffrey Hinton, James McClelland, and David Rumelhart in the 1980s.
Focuses on activity and connections across various brain regions.
Examples of Distributed Representation:
Pain Perception: Involves sensory, emotional, and motor components, activating multiple brain areas.
Object Recognition: Different objects (e.g., houses, faces, chairs) activate distinct but overlapping brain regions.
Connections Between Brain Areas:
Structural Connectivity: Physical pathways connecting different brain areas.
Functional Connectivity: Measures the coordination of neural activity between areas, indicating how they work together.
Resting-State fMRI:
Used to measure functional connectivity in the brain when not performing a task.
Can predict behaviors like detecting sounds or perceiving pain.
Mind-Body Problem:
Question: How do physical processes (e.g., nerve impulses) translate into subjective experiences?
Current research shows correlations between neural activity and perception, but doesn’t answer how neural processes cause conscious experience.
Larry Hester's Story:
Background:
Retired tire salesman diagnosed with retinitis pigmentosa in his 30s, leading to total blindness for 33 years.
Bionic Eye:
Implanted electrodes connected to glasses-mounted camera.
Restores partial vision by distinguishing contrasts (e.g., light vs. dark).
Allowed Larry to perceive crosswalk lines, edges of objects, and faces—transformative despite limited functionality.
Importance of Vision:
Demonstrates the role of light, eyes, and retinal cells in visual perception.
Vision involves complex processes starting in the eye and continuing in the brain.
The Visual Process:
Step 1: Distal Stimulus
Object in the environment (e.g., a tree).
Step 2: Proximal Stimulus
Light reflected from the object enters the eye, creating an image on the retina.
Step 3: Receptor Transformation
Photoreceptors (rods and cones) convert light into electrical signals.
Step 4: Neuronal Processing
Signals are processed as they travel through retinal neurons and into the brain.
Focus of Chapter:
How physical events influence seeing in focus, dim light, and fine details.
Light – The Stimulus for Vision:
Visible Light:
A band within the electromagnetic spectrum (400–700 nm).
Short wavelengths: Blue.
Medium wavelengths: Green.
Long wavelengths: Yellow to red.
The Eye:
Structure:
Light enters through the pupil, focused by the cornea and lens onto the retina.
Photoreceptors: Rods and cones in the retina convert light into signals.
Photoreceptors:
Rods: Function in dim light; concentrated in the peripheral retina.
Cones: Detect color and fine details; concentrated in the fovea.
Photoreceptor Distribution:
Fovea:
Small central area with only cones; provides detailed central vision.
Peripheral Retina:
Contains both rods and cones, with rods outnumbering cones (120 million rods vs. 6 million cones).
Visual Disorders:
Macular Degeneration:
Common in older adults.
Destroys the fovea, creating a blind spot in central vision.
Key Takeaways:
Vision relies on light interacting with the eyes and retinal cells.
The process begins with light entering the eye and ends with the brain interpreting visual signals.
The distribution of rods and cones determines how we perceive light, color, and detail.
3.1
Light: The Basis of Vision
Visible Light: A portion of the electromagnetic spectrum with wavelengths ranging from 400 to 700 nanometers (nm).
Short wavelengths (~400 nm): Blue.
Middle wavelengths (~500-600 nm): Green.
Long wavelengths (~700 nm): Red and orange.
Electromagnetic Spectrum: A continuum of energy, where visible light represents the range humans can perceive.
Other forms include gamma rays, X-rays, and radio waves, which are outside the visible range.
The Eye: Structure and Function
Overview:
Light enters the eye through the pupil and is focused by the cornea and lens onto the retina.
The retina contains photoreceptors (rods and cones) that convert light into electrical signals.
Photoreceptors:
Rods:
Specialized for vision in dim light.
Found in the peripheral retina.
Total: ~120 million.
Cones:
Specialized for color vision and fine detail.
Found in the fovea and peripheral retina.
Total: ~6 million.
Visual Pigments: Light-sensitive chemicals in photoreceptors that react to light and trigger electrical signals.
Rod and Cone Distribution
Fovea:
Small area at the center of the retina.
Contains only cones (~50,000 in number).
Critical for sharp, central vision.
Peripheral Retina:
Contains both rods and cones but predominantly rods.
Retinal Conditions
Macular Degeneration:
Affects the fovea and surrounding area, leading to a blind spot in central vision.
Common in older adults.
Retinitis Pigmentosa:
Degeneration of peripheral rod receptors, leading to loss of peripheral vision (tunnel vision).
In severe cases, can also affect foveal cone receptors, causing total blindness.
Blind Spot
Cause:
Area in the retina where the optic nerve exits the eye.
Lacks photoreceptors, resulting in a natural blind spot.
Why It’s Unnoticed:
Located in the peripheral visual field, where focus is less sharp.
The brain "fills in" the blind spot with surrounding visual patterns, creating a seamless perception.
Demonstrations:
Blind Spot Awareness: Using a cross and circle, the circle disappears when its image falls on the blind spot.
Filling-In Process: The brain replaces the blind spot with matching surrounding patterns (e.g., the spokes of a wheel).
Key Takeaways
Vision begins with light entering the eye and interacting with photoreceptors in the retina.
Photoreceptors are distributed differently, with rods specialized for low-light vision and cones for detailed, color vision.
The brain compensates for natural visual limitations, like the blind spot, to create a coherent perception.
3.2
Cornea and Lens Focusing Light:
Show light being focused by a lens onto a surface.
Use a magnifying glass focusing sunlight on paper.
Caption: "Cornea and lens bend light to focus on the retina."
Accommodation Adjustment:
Film a close-up of eye adjusting focus on objects at different distances.
Use a pencil or object for focus changes.
Caption: "Accommodation adjusts the lens for clear vision at various distances."
Demonstrating Accommodation:
Film shifting focus between near (pencil) and far (distant object).
Caption: "Accommodation lets us focus on nearby or distant objects, but not both at once."
Refractive Errors (Myopia & Hyperopia):
For myopia, blur a distant object (e.g., sign, landscape).
For hyperopia, blur a close object.
Caption: "Myopia blurs distance; hyperopia blurs near. Corrective lenses help focus."
Presbyopia (Aging Vision):
Show an older person reading with glasses.
Caption: "Presbyopia makes it hard to focus on near objects due to lens hardening."
Corrective Lenses:
Demonstrate blurry vision without lens and sharp focus with lens.
Caption: "Corrective lenses refocus light for clear vision."
Focusing Process Visualization:
Capture an image going from blurry to sharp focus (camera lens analogy).
Caption: "The eye’s lens focuses light, beginning the visual process."
Retina and Focus:
Show an eye model or diagram with focused light on the retina.
Caption: "Focused light on the retina starts vision before the brain processes it."
3.3
Transduction:
Definition: The process of converting light energy into electrical signals.
Visual Pigments: Composed of opsin (a long protein) and retinal (a light-sensitive component).
Process:
When light hits the retina, retinal changes shape (bent to straight), initiating isomerization.
Isomerization triggers a chain reaction that activates molecules, creating electrical signals.
This amplification process leads to full activation of the photoreceptor, completing transduction.
Dark Adaptation:
Definition: The process of becoming more sensitive to light after moving from a bright to a dark environment.
Dark Adaptation Curve: Tracks changes in light sensitivity over time after light exposure ceases.
Initial Phase: Sensitivity increases rapidly, driven by cone adaptation.
Later Phase: Sensitivity increases more slowly, influenced by rod adaptation.
Methods:
Dark Adaptation Experiment:
Participant looks at a fixation point while a test light falls on the peripheral retina.
Light-adapted sensitivity is measured first.
After extinguishing the light, sensitivity is tracked as it increases in the dark.
The result is a two-phase curve, reflecting both cone and rod adaptation.
Conclusion:
Cone vs. Rod Adaptation:
Cone adaptation: Occurs in the initial phase (when the test light is focused on the fovea).
Rod adaptation: Occurs later and is responsible for the second phase of the curve.
Measuring Cone Adaptation:
The two phases of the red curve in Figure 3.13 represent the dark adaptation of both rods and cones, as the test light was placed on the peripheral retina, which contains both.
To measure cone-only dark adaptation, the test light must be focused on the fovea, where only cones reside, and must be small enough to stimulate only the fovea. This gives the green curve in Figure 3.13, which corresponds to the initial phase of the full adaptation curve but lacks the second phase.
Measuring Rod Adaptation:
The green curve only represents cone adaptation, so to study rod adaptation, researchers use people who are rod monochromats, individuals without cones due to a genetic defect. This allows measurement of rod sensitivity without interference from the cones.
The purple curve shows how rod adaptation proceeds, reaching full sensitivity after about 25 minutes. This corresponds to the second part of the dark adaptation curve.
Rods take longer to adapt because of slower visual pigment regeneration compared to cones.
Visual Pigment Regeneration:
Light causes the retinal part of visual pigment molecules to change shape and separate from opsin, a process called visual pigment bleaching.
Regeneration occurs when retinal reattaches to opsin, returning the visual pigment to its functional state. Cone pigments regenerate faster than rod pigments.
The dark adaptation process corresponds to the regeneration of visual pigments, as shown in William Rushton's research.
Spectral Sensitivity:
The eye’s sensitivity to light varies across the visible spectrum. Rods are more sensitive to short-wavelength light, peaking at 500 nm, while cones are most sensitive at 560 nm.
The Purkinje shift occurs during dark adaptation, where vision becomes more sensitive to short-wavelength light, such as blue and green, compared to long-wavelength light like red. This shift explains why green foliage stands out more near dusk.
Rod and Cone Pigment Absorption Spectra:
The absorption spectra of rods and cones show how each pigment absorbs light at different wavelengths.
Rod pigment absorbs best at 500 nm, while the three types of cone pigments absorb at different wavelengths: short-wavelength cones at 419 nm, medium-wavelength cones at 531 nm, and long-wavelength cones at 558 nm.
3.4
Neural Convergence: Multiple neurons synapse onto a single neuron.
Rods: 120 rods converge onto one ganglion cell.
Cones: About 6 cones converge onto one ganglion cell.
Impact on Perception:
Sensitivity:
Rods are more sensitive due to greater convergence.
In dim light, rods detect faint stimuli better (e.g., stars visible in peripheral vision).
Acuity (Detail Vision):
Cones have better acuity because they have less convergence.
Foveal cones often have "private lines" to ganglion cells, enabling precise vision.
Illustration of Sensitivity:
Rods: 5 rods converge onto one ganglion cell. Low light intensity can stimulate the ganglion cell.
Cones: 5 cones each connect to individual ganglion cells. Higher intensity required to stimulate ganglion cells.
Conclusion:
Rods are better at detecting light (higher sensitivity) but offer less detail.
Cones excel in detecting fine details (better acuity) but require more light to respond.
Cones Have Better Acuity Than Rods:
Rods: More convergence, resulting in greater sensitivity but poorer acuity.
Cones: Less convergence, resulting in better acuity (ability to see details).
Acuity:
Cone Vision: Best in the fovea, where cones are densely packed.
Peripheral Vision: Less sharp due to fewer cones in the periphery.
Demonstration: Letters seen near the fovea are easier to identify than those further off to the side, which are perceived by peripheral retina.
Dark Adaptation:
Cone Vision: Provides sharp, colorful details in light.
Rod Vision: Poor detail in darkness, vision becomes blurry and colorless as rods take over.
Neural Wiring of Rods vs. Cones:
Rod Circuits: Multiple rods synapse onto one ganglion cell, causing poor acuity.
Cone Circuits: Each cone has its own ganglion cell, enabling better detail perception.
Convergence:
High Convergence: Results in high sensitivity (rods), but poor acuity.
Low Convergence: Results in low sensitivity (cones), but high acuity.
Receptive Fields:
Ganglion Cells: Each ganglion cell has a receptive field, an area on the retina that must be illuminated for the cell to fire.
Hartline’s Discovery: Receptive fields are larger than individual photoreceptors and overlap between ganglion cells.
Football Field Analogy: Each ganglion cell is like a spectator watching a small part of the field, but many ganglion cells together monitor the whole retina.
Kuffler’s Discovery of Center-Surround Receptive Fields:
Center-Surround Organization: Ganglion cells have receptive fields organized like concentric circles, with the "center" and "surround" responding differently to light.
Excitatory-Center, Inhibitory-Surround: Light in the center increases firing (excitatory), while light in the surround decreases firing (inhibitory).
Inhibitory-Center, Excitatory-Surround: The reverse, with inhibition in the center and excitation in the surround.
Receptive Field Definition:
Modified Definition: A receptive field is "the retinal region over which a cell in the visual system can be influenced (excited or inhibited) by light."
Center-Surround Antagonism:
Best Response: Ganglion cells respond best to a spot of light matching the size of the excitatory center of their receptive field.
Increased Spot Size: As the spot of light grows to cover the surround, the inhibitory response counteracts the excitatory center's response, reducing firing.
Lateral Inhibition:
Underpinning Center-Surround Antagonism: Lateral inhibition occurs when neighboring receptors inhibit the firing of a receptor.
Hartline’s Experiment: Showed that stimulating nearby receptors can reduce the response of a target receptor (lateral inhibition).
Limulus Experiment:
Horseshoe Crab Study: Hartline found that illuminating neighboring receptors inhibited the response of a central receptor in the Limulus eye.
Lateral Inhibition in Mammals:
Neural Circuitry: Horizontal and amacrine cells in the retina of mammals (including humans) transmit lateral inhibitory signals, crucial for center-surround antagonism.
Neural Circuit Example:
Receptive Field Creation: A circuit of seven photoreceptors creates an excitatory-center, inhibitory-surround receptive field through both excitatory and inhibitory synaptic connections.
Stimulation Effects: Stimulating the center increases firing; stimulating the surround decreases firing due to lateral inhibition.
Summary of Mechanisms:
Excitation and Lateral Inhibition: The interplay of excitation in the center and lateral inhibition from the surround creates center-surround receptive fields. This mechanism is fundamental for how ganglion cells process visual information.
Center-Surround Receptive Fields and Edge Enhancement:
Interactions between excitatory center and inhibitory surround shape neuron responses.
Contribute to edge enhancement by increasing perceived contrast at borders.
Help make edges more distinct and easier to perceive.
Chevreul Illusion:
Illusory light and dark bands appear at the border between light and dark regions.
Perception of lightness changes, despite constant physical light intensity.
The border looks sharper and more distinct due to the enhancement effect.
Mach Bands:
Similar light and dark bands appear at fuzzy shadow borders (compared to sharp edges in the Chevreul illusion).
Caused by lateral inhibition and the center-surround receptive field interactions.
Explanation via Receptive Fields:
Cells with more illumination in their inhibitory surround generate more inhibition, leading to a reduced response.
Cells with less illumination in the surround generate less inhibition, increasing their response.
This contrast creates enhanced edge perception.
Neural Processing and Perception:
Perception results from neural processing in the retina and higher levels of the visual system.
Receptive fields change at higher processing stages, responding to complex stimuli like objects.
Early Events in Perception:
Early distortions, like the Hubble Space Telescope lens issue, highlight the importance of initial input in shaping perception.
Degraded sensory input (e.g., poor eye focusing) affects perception, even with complex neural processing.
Visual Pigments Limiting Perception:
Visual pigments act as filters, limiting the wavelengths available for vision.
Rods are sensitive to 420–580 nm wavelengths, cones to longer wavelengths.
Honeybees perceive ultraviolet light due to specialized visual pigments.
Developmental Dimension - Infant Visual Acuity:
Measuring infant visual acuity is difficult due to lack of verbal feedback.
The preferential looking (PL) technique helps measure visual acuity by observing infants’ gaze preferences.
O
Chap 1
Challenges of studying perception
Why study perception?
To understand yourself
Practical reasons:
Design prosthetic devices
Repair damaged sense organs
Build artificial realities ETC
You can only do these helpful things well if you understand how perception works
Studying perception is complicated
Much of our brain is devoted to the complex processing that underlies perception
When scientists try ti program computers to analyze visual scenes & recognize objects, the complications become very evident
Methods for studying perception
Involves many sciences
Physics
To understand the kinds of external energy we can sense
Anatomy & physiology
To understand the operations of the brain and nervous system
Psychology
To understand cognitive functions like attention, memory, etc
Info science
To examine how signals are extracted from noise
Computer science
To construct models & test hypotheses by building systems
3 main methods of study
Physiological ("hardware" levels of analysis")
Cognitive ("software" level of analysis)
Psychophysical (connects objective measurements to reports of subjective experience)
Physiological method
Ask: how are properties of objects in environment represented by activity in nervous system?
It examines relationships between stimuli & physiological responses
Characterizes activity in brain
Neurons
Neurons = units of processing
Relationship between nerve impulses & specific perceptions
Signal is transmitted down the receptor cells axon to the next neuron
Specialized receptor gets info from outside world (pressure on skin)
The second neuron doesn’t have specialized receptors. It has dendrites that that can receive any nerve impulse
The neuron
Neuron = our unit of processing
Everything in this course is based on
The activity (firings, nerve impulses, action potentials),
The interconnections, of these units
How one neuron communicates with the next neuron
Neurons don’t actually touch each other
Synapse = gap between 2 neurons
Pre-synaptic neuron (before the gap) communicates wit the….
Post synaptic neuron (after the gap)…
By sending drugs (neurotransmitters)
Neuron firing rates
Not a 1-1 relationship between stimulus & action potential
Its lots and lots of firing happening
Each neuron needs to collect lots of evidence to decide whether they should fire more or less
Firing rate, not strength (magnitude) of each action potential carries info
Theres a spontaneous activity level = "resting rate" or "base rate" of firing (without stimulation)
If a neurons inputs encourage activation, the firing rate goes UP, above its base rate
If they encourage inhibition, the firing rate goes down, below its base rate
Refractory period
How a message is sent
One neuron communicates with the next neuron (can affect the activity of that neuron)
Overall use
Localization of function
Where in brain particular info is processed
Sensory coding
How features of environment = represented
Specific neurons code for certain features
Pattern of firing (distributed over many neurons) codes for a features?
Physiological investigative techniques
Lesion studies
If a certain part of brain is damaged and particular ability disappears, then that part of the brain is normally involved in this particular ability
Single-cell recoding technique
Insert a delicate probe that can detect electric activity
Present specific stimuli
Measure the neurons response
This gives precise info about both timing & location of activity, but only in a narrow brain region
Neuroimaging techniques
Specifies functions for broader areas of brain
Activity corresponds to different perceptual tasks
Functional magnetic resonance imaging (fMRI) measures the changes in blood flow that support increased neural activity
Note: blood flow to neurons increases shortly after the moment when neurons get more active (like muscles)
fMRI tells us precisely where brain activity occurs across a broad area, but is somewhat imprecise about timing
Electro-encephalography (EEG)
Measures electrical acticity through the scalp/skull
Very precise info about the timing of neural activation across a broad area
Less precise about location of activity in brain
Cognitive Method
Neisser
Thinking: info processing flowchart
2 directions of info processing
Bottom-up: constructs a perception by analyzing info falling on receptors
Top-down: starts with analysis of high-level info (knowledge, goals…)
Psychophysical method
Q: How we use info from environment (stimulus) to create perceptions (experience)
Identify exactly what aspect of stimulus underlies some perceptual experience
Limitations of our perception
Do we perceive "reality" directly?
To survive, we must acquire useful info about the world
Perception constructs a "reality" for the perceiver
There must be a systematic relation between it & the external world, or else we wouldn’t survive
We don’t perceive reality directly
Interpretation
Psychophysics
Intro
Psychophysics: determining quantitative relationships between
Physical stimulus (external)
Perception (internal psychological)
Big picture: we cant study perception if we cant even say whether an observer perceives a stimulus or not
Gustav Fechner epiphany
Quantitative relationship between
Mind (mental sensations)
Body (material/physical stimulus)
Psychometric function
Experimenter presents stimuli of different insensities
Task = detection (did you see a light or not)
Absolute threshold
Absolute threshold = smallest amount of stimulus energy necessary for the observer to detect a stimulus
Problem : THRESHOLDS ARE NOT ALL OR NOTHING
Difference threshold
Difference threshold
Or JND (just-noticeable difference)
Smallest difference between 2 stimuli a person can detect
Signal detection theory
Absolute threshold: what intensity of stimulus produces 50% "yes I detect it" responses
But, is your sensitivity to the stimulus the only thing that determines your response
Response criterion
Criterion = amount of sensory info observer requires for saying "yes"
Low criterion: say "yes" even if don’t perceieve much evidence for the tone
Liberal responder
High criterion: less willing to say "yes"
Conservative responder
Response is affected by:
Observers sensitivity (to the signal)
And observers response criterion
Catch trials
Catch trials = no target
(method of constant stimuli has none)
Not trying to "trick" observer, make look dumb
Ricky detecs more tones (T-present trials) than Julian
But ricky cant tell the difference between tone (target-present trials) and no-tone (catch trials) better than julian
Payoffs
Payoffs #1 (neutral)
Hit: win $1
Miss: lose $1
FA: lose $1
CR: win $1
.. If pay $1 million per hit, what would observer do?
Hit: win $1M
Miss: lose $1
FA: lose $1
Cr: win $1
Always say yes (liberal responder)
So payoff caused a change in bias (not sensitivity) which can change responses
ROC curves
ROC = receiver operating characteristics
Observers perofrmance on detection task
X-axis: %FA
Y-axis: %hits
How this curve looks, tells you important stuff about SENSITIVITY, INDEPENDENT of biases
Same sensitivity, same stimuli.. But different behavior, IF CHANGE BIAS (payoffs)
Neutral (N)
Give $1 for each hit and each CR
Take away $1 for each miss and each FA
Same ROC curve
If we change the bias, then for
Same observer with the same sensitiviry, and even the same stimuli
The observer may behave differently
But their data point will still fall somewhere on the same ROC curve as before, because that shows their sensitivity
Same roc curve: same sensitivity
Different ROC curve
If 2 peoples data points fall on different ROC curves, the 2 people have different sensitivities
Who=more sensitive (higher hits, lower FA)?
Different ROC curves, different sensitivities
Magnitude estimation
Relationship between the intensity of a stimulus, and the perception of its intensity
If you double the physical intensity of a stimulus,
Does the stimulus now seem doubly bright or doubly loud?
Experimenter: this is the standard light
It has the brightness of 20, which I chose arbitrarily
Experimenter: heres a different, test light
Given that the first one had brightness 20 (because I said so), how bright would you say this one is?
If it seems twice as bright say 40. if it seems half say 10
Experimenter: another test light
Given that the first one had brightness 20, how bright would you say this one is?
Neural processing
Inhibition
Inhibit = stop something from happening
Activity of one neuron can increase/decrease activation in another neuron
Signals can be transmitted "sideways" across retina
Not just forward" through retina (receptors ->optic nerve)
Lateral inhibition = the capacity of an excited neuron to reduce the activity of its neighbors
Why care about low-level neural circuitry?
We detect stimuli, but we also assess the appearance of objects
Simultaneous contrast: perceiving these 2 squares on different backgrounds as being different, even though theyre physically identical
What does it mean to say that info is analyzed?
Info is analyzed, interpreted, transformed
So that the resulting signal is easier for perceptual system to understand
Synapses "process" electrical signals as they travel from receptors along to further into the brain
Neuron B
Circuit with excitation, convergence & inhibition
Leads to neuron B which responds best to a bar of light of a specific length
Neuron B responds best to a medium-length bar of light
Worse to shorter or longer bars of light
Neural processing, by the circuit leading to neuron B, transformed the info
From input (photoreceptor activations)
Into different output (response of neuron B)
Neuron B extracted info (how much evidence it collected for the presence of a certain length bar of light)
Receptive fields
Receptive field (RF): region of retina that, when stimulated, influences firing rate of a particular neuron
Could make neuron fire ore (excitation)
Could make neuron fire less (inhibition)
Measuring a neurons response
Simultaneously:
Present visual stimuli
Record response of a ingle neuron
Answer: what visual stimuli does that neuron respond to
Center-surround receptive fields
Center-surround RFs are characteristic of
Retinal ganglionic cells
The LGN (low-level visual processing)
Excitatory-center, inhibitory surround: common
Inhibitory-center, excitatory-surround: also exist
What will happen?
Higher firing rate than baseline, by a lot
Higher firing rate than baseline, by a little
Lower firing rate than baseline, by a lot
Lower firing rate than baseline, by a lot
Whole inhibitory surround of RF is illuminated. No light falling on excitatory portion of RF. What will happen?
Higher firing rate than baseline, by a lot
Higher firing rate than baseline, by a little
Lower firing rate than baseline, by a lot
Lower firing rate than baseline, by a little
Center surround inhibition explains an illusion
Explaining the Hermann Grid
Perceiving things that arent there
Hermann grid: gray dots at intersections?
Why is there no gray spot at fixation (when you look at it)
Smaller RFs at fixation (fovea)
A small RF can fit within a single gridline
So, inhibition at the RF's surround isnt different between
At an intersection
Along a gridline
RF is filled with uniform light; the 2 neurons will emit identical responses
Lateral inhibition can explain many illusions of appearance but:
Benary Cross: both gray triangles receive same amount of lateral inhibition
But B looks ligter than A
The sensory code
Specificity coding
Representation of a specific stimulus by the firing of a neuron specialized to respond to just it
Population coding
Representation of specific stimuli by pattern of firing of many neurons
Each neuron fires to each face, but by different amounts
Sparse coding
A particular object is represented by a pattern of firing in a small number of neurons
The stimulus for vision: light
Wavelength: distance between 2 peaks of the electromagnetic wave
Visible light; the range of this energy that can stimulate the human visual system
Light energy emanates from light source
Light is reflected off of objects in the environment, and some of it then makes it to our eyes
The way that this light bounces off of objects
Provides clues about the properties of these objects
The visual system interprets this pattern of incoming light
To determine the properties of these objects
But what we perceive visually is filtered through the properties of the visual system
Focusing the incoming light on the retina
An image = made up of many points (little pixels)
To explain focusing the image, we explain in terms of what happens to just one of these points
Accommodation: lens changes shape so that images of nearby objects arent blurry on the retina
The lens of your eye Is usually relatively flat (relaxed state)
That works well for focusing images of far away things on the back of your eye
But when needed, muscles can make it rounder & fatter
This rounder shape curves light rays more than the flat shape does
And that’s exactly what you need so that close up objects wont cast blurry images on the back of your eye
Accommodation
Ciliary muscles change the shape of the lens
Fatter lens bends light rays more sharply on way to retina
The retina
Transduction
The transformation of one form of energy to another
Nervous system converts
Patterns of physical energy (light)
Into neural events (photoreceptor electrical signal)
Part of the light-sensitive photopigment (in outer segment of photoreceptor) absorbs light & changes shape
Causes receptor to generate electrical signal
Dark adaptation
Major theme
What the receptors do encode, vs don’t
Affects what info can get in, for neural processing and thus affects what you can and cant perceive
Ex:
Dark adaption: increase in sensitivity that occurs when illumination changes from light to darkness
What does dark adaption DO to your visual receptor that helps you see better after time in the dark?
2 systems
Dark adaption occurs in 2 stages:
Faster stage due to adaptation of cones
Slower stage due to adaptation of rods
2 diff systems controlling vision
Coping with huge change in light levels
The dark adatation curve
Overview:
Curve moving DOWN (w/time in dark) means
Threshold (amount of stimulus necessary for detection) is DECREASING
Sensitivity is INCREASING
FULL dark adaptation curve
Both rod & cone systems active
Where do you have the observer look?
Right at the test light, or off to the side?)
Dark adaptation curve: cones only
Where do you have the observer look?
Right at the test light, or off to the side?)
Dark adaptation curve for rods only
Where do you have the observer look?
Rod-cone break
When lights go off, sensitivity of both cones and rods begins increasing
At first, cones control vision - 4 min to max sensitivity
Rods - 30 mins to max sensitivity
After 7 mins, rods control vision (more sensitive)
Photoreceptor pigments & dark adaptation
What is dark adaptation?
Why when the light goes off does your visual sensitivity gradually increase?
First, what happens during transduction? (light ->neural event)
Pigments in receptors contain opsin (large molecule), attached to retinal
Transduction: retinal detaches from opsin
& whole thing CHANGES (loses) color
Red->orange->yellow->transparent /white
=pigment bleaching
Visual pigments must recover after responding
Retinal has to re-attach to opsin
=pigment regeneration
Photoreceptors face backwards to be near pigment epithelium
Rods adapt more slowly than cones because the rod pigment regenerates slowly
Visual pigment regeneration is responsible for increased sensitivity that occurs during dark adaptation (its why dark adaptation helps)
2 systems
Cones: low-sensitivity detectors
Active at high light levels (daylight)
Rods: high-sensitivity detectors
Useful at low light levels (night)
Different adaptation rates too
2 - detector system helps us see across huge variation of intensity levels encountered in the environment
THAT MAJOR THEME AGAIN
You CANT see an object
If it doesn’t emanate or reflect light that ends up activating rod & or cone receptors in your retina
Info can only get in to brain if activates a receptor
Properties of rod & cone photopigments play important role in shaping perceptions
Spectral sensitivity
Spectral sensitivity curve
Switch from showing
White light (contains all wavelenghts of spectrum)
Too monochromatic light (contains just one wavelength)
Persons sensitivity to light at each wavelength
Dark adaptatiton shifts vision from cones to rods
This affects spectral sensitivity
Cones are more sensitive to longer - wavelength light (red)
Rods are more sensitive to shorter wavelength light
Absorption spectrum
Spectral sensitivity
Observer persons sensitivity to light at each wavelength
(Pigment) absorption spectrum
Amount of light absorbed by a photopigment (substance), at each wavelength
Rod spectral sensitivity curve (of person)
Absorption spectrum of the rod pigment
Spectral sensitivity of of vision is due to absorption of light by rod visual pigment
Convergence
Convergence: more than one neuron sends signals to another neuron
Rods converge more than cones so rods are more sensitive
Ganglion cell receives input from (average) 120 rods, vs (avergae) 6 cones
At low light levels (high sensitivity needed)
A ganglion cell receiving input from many rods might still get activated but not a ganglion cell receiving input from only a few cones)
Cones converge less than rods sp cones have better acuity
Acuity = ability to see details
All-cone fovea has good acuity
Visual acuity drops as move from fovea to periphery
Visual acuity also drops during dark adaptation (sensitivity increase): cone vision --> rod vision
Lots of CONVERGENCE gives rods low acuity & high sensitivity
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