Sensation and Perception Exam 2

Psychophysics

Scientific study of relation between physical stimuli and perception, tools for understanding mind/body connection, and foundation of experimental psychology

Founder of psychophysics

Weber and Fechner

Weber’s Law

Discovered relation between stimulus change and perception, just noticeable difference (JND proportional to stimulus magnitude, formula is ΔI/I = k

Example of Weber’s Law

Lifting weights: the difference depends on staring weight

JND increases with intensity

Everyday applications: sound volume or brightness

Fechner’s Law (how does it expand on Weber’s idea)

Attempts to link physical and mental directly

S = k log l

Psychological experience grows logarithmically with stimulus intensity

Took Weber’s fraction and assumed that every single JND across a sensory scale feels like an equal unit of psychological change

Method of constant stimuli

Psychometric function (what does its sigmoid shape represent?)

Graph relating stimulus intensity to detection probability

Sigmoid shape reflects transition from chance to certainty

Why is bias a problem in threshold measurement?

How does the two alternative forced choice reduce bias?

Subject chooses between two intervals. They are strictly required to choose between predefined options and cannot opt out with neutral or uncertain responses

Define Absolute Threshold and give an example

minimum intensity of a physical stimuli that an individual can detect at least 50% of the time

Example: entering a theater, sudden darkness creates temporary blindness. The absolute threshold would be the faintest detectable light

Dark Adaptation Curve (what does it show about rods and cones?)

It shows that rods and cones adapt to darkness at different speeds and sensativities:

Cones: first 5-10 mins, adapt fast but have low final sensitivity to dim light

Rods: next 20-40 mins, adapt slowly but reach high final sensitivity

Rod-Cone break: the inflection point around 7-10 mins where rods become more sensitive than cones and take over night vision

Which receptors dominate in dim light vision and why?

Rods dominate in dim light because of high sensitivity to lights and high convergence (multiple rod cells connect to a single cell which then connects to a ganglion cell)

Why is mathematics important in perception theories? What are its limits?

Alan Turing

Created concept of Turing Machine

Abstract device manipulating symbols

Foundation of computer science

Universal machine: any computer can emulate any other

Brain viewed as computing device

Basis for cognitive simulations

In Turing’s analogy, how is the brain like a computer?

both are information processing systems that manipulate symbols and follow rules to solve problems

Symbol manipulation, rule based steps (algorithms) and universal computation

Claude Shannon

Redundancy, noise, channel capacity, and transmission rate

Define redundancy and give an example

extra or repeated data in a message that is not required to convey the message is measured as the fraction of a message determined by statistical rule

Example: spelling rules, such as being able to easily spot an error in a word due to repeated letters or predictable patterns.

What is channel capacity and why does it matter?

maximum information transmitted simultaneously and reliably over a noisy channel

In Shannon’s model, the cortex acts as what?

the cortex acts as the information source or destination

David Marr

Defined computational theory of vision. Any information processing system must be understood at three distinct levels

emphasis: perception = information processing task

1. Computational: What is the goal?

2. Algorithmic: What representation/rules?

3. Implementational: What neural structures?

Marr’s airline reservation analogy

if you want to understand an international network of airline reservation computers, knowing how the physical computer circuits, silicon chips, or transistors work is insufficient

Define and compare apperceptive vs associative agnosia

apperceptive: cannot copy/ match shapes

associative: cannot recognize objects

Analog vs Symbolic representations

Analog: continuous mapping. ex. thermometer, speedometer

Symbolic: discrete, arbitrary symbols. ex. letters, numbers

Grandmother cells: hypothetical neuron in the brain that responds only to a highly specific concept such as your grandmother

Center surround receptive fields (what do they detect? Why efficient?)

Balance of excitation and inhibition

Detect edges and changes in input

Efficient because they discard redundancy

Which theory rejected internal representations and emphasized natural stimuli?

Ecological Perception

Principle of univariance

Response of a sensory neuron depends on multiple stimulus properties

But output varies along only one dimension

Who first described univariance in color vision?

Naka and Rushton (1966)

How do cone photoreceptor responses demonstrate univariance?

Response depends on both intensity and wavelength

Same response can come from bright long wave or dim short wave

Ambiguous coding of color and brightness

Somatosensory example of univariance (Pacinian corpuscles)

Pacinian corpuscle: response depends on vibration amplitude and frequency

Same firing rate from high intensity/low frequency or low intensity/high frequency

Why are single neurons unreliable coding units?

Define population coding.

Pattern of activity across many neurons encodes stimulus

Each neuron tuned to specific values

Together represent full stimulus dimension

Visual cortex example of population coding

How the brain represents the orientation of a line or edge

Cells tuned to orientation or spatial frequency

Distributed responses form a profile

Weighted average can estimate orientation

Auditory system example of population coding

Cells in auditory thalamus tuned to inter aural delay

population code used to localize sound sources

List two advantages of population codes

Redundant, so resilient to neuron loss

Handle variability and noise well

General coding scheme across senses

Trichromacy theory (how does it explain color coding)

Three cones types: S (blue), M (green), and L (red)

Each wavelength produces 3 values

Relative responses across cones encode color

Objects reflect different proportions of wavelengths

Define metamers and give an example

Different wavelengths, same cone responses

Appear identical despite different spectra

Which technology uses trichromacy principles?

Televisions, computers use pixels that use RGB dots

Printers use cyan, magenta and yellow inks

Define sparse coding

Small subset of neurons active at once

maximum information with minimum activity

efficiency through sparsity

What are Jennifer Aniston neurons? What do they show?

Highly selective medial temporal lobe cells that respond to images of specific individuals

Which illusion shows how lateral inhibition exaggerates differences?

The pyramid illusion

Should illusions be treated as errors or windows into coding?

Windows, illusions are shortcuts in the brain exposed because of artificial stimuli

Why is sensory input ambiguous?

The exact pattern of stimulation on our sensory organs can be caused by an infinite number of different physical realities

Retinal image is shaped by multiple factors

Factorization analogy: multiple possible decompositions of sensory data

Bottom up vs Top down processing

Bottom Up: information flows from sensory input to higher level structures

Gestalt Principles: Proximity, similarity, continuity, closure

Top down: prior knowledge and expectations influence perception. Ambiguous figures reveal the role of context and experience

List and explain at least 2 Gestalt principles

Set of rules describing how the human brain organizes isolated visual elements into meaningful wholes

Proximity: elements that are close to one another are perceived as a single group

Similarity: elements that look alike are grouped together

Closure: the brain will fill in missing gaps to complete a familiar, enclosed shape even if the lines are incomplete

Continuity: the eye prefers to follow the most continuous path

Define and give examples of bistable stimuli

ambiguous visual patterns or objects that can be perceived in two distinct ways, but only one at a time

Necker cube: a simple line drawing of a wireframe cube with no depth cues. It can be perceived in facing multiple directions

What brain activity supports top down attentional involvement in bistable perception?

Increased activity in the frontal and parietal cortex. These regions help direct attention and influence which of two possible interpretations is consciously perceived

Define binocular rivalry

Type of bistable perception that occurs when each eye is presented with a different image, causing perception to alternate between the two images instead of combining them into one

What are Mooney faces and why do they require top down knowledge?

Black and white, two tone images of faces with little detail. they require top down processing because the brain uses prior knowledge and experience with faces to recognize the image as a face

Bayesian inference (priors, likelihood, and MAP estimate)

Bayesian inference is the process of combining prior knowledge with new sensory evidence to make the best estimate about the world

Example of cue combination in perception

Cue combination is when the brain combines multiple sensory cues to make a more accurate perception. For example, when judging the distance of an object, the brain combines cues (such as retinal disparity) and monocular cues (such as size and perspective) to determine how far away it is

Strengths and limits of Bayesian models

Strengths: Bayesian models explain how perception combines prior knowledge with sensory evidence and can account for how people make predictions and handle uncertainty

Limits: They do not always explain how the brain actually performs these computations and can be difficult to apply because the correct priors and probabilities are often unknown

Why do people drive too fast in fog according to Bayesian models?

People rely on prior experience about driving speed and distance. In fog, sensory information is less reliable, so the brain may rely too heavily on these priors, causing drivers to underestimate their speed