PSYC236 – Cognition & Perception Lecture 1 Notes

Administrative Details

• Subject: PSYC236 – How We Think & See (Cognition & Perception)

• Teachers: Dr. Harold Hill, Dr. Steve Palmisano (slides are based on their work)

• Learning Materials:

  • Check the subject outline on Moodle for all important info.

  • Lecture slides and tutorial materials are put online every week, ahead of time.

  • There will be short quizzes in tutorials each week; look at the unit outline to see what topics they will cover.

  • Recordings of lectures (Echo360) are available.

Core Historical & Conceptual Questions

• Charles Bonnet (CB) & the “mind’s movie screen”:

  • CB syndrome means people see clear visual things that aren't real, even though they know they're not real. This shows that your brain can “see” things even without new information from your eyes.

• Molyneux’s Question (1693):

  • Idea: Imagine someone born blind who can tell a cube from a ball by touching them. If they suddenly could see, would they immediately know which is the cube and which is the ball just by looking?

  • Meaning: This question makes us think about how our different senses work together and whether we learn to see or if it's something we're born knowing. It's about 'nature vs. nurture' (what's natural vs. what's learned).

Basic Definitions

• Sensation:

  • “What happens when our sense organs are stimulated” (Weiten, 2007).

  • It's the basic process where physical energy (like light or sound) is turned into brain signals.

• Perception:

  • “How we choose, arrange, and understand what our senses tell us” (Weiten, 2007).

  • This is how our brain makes sense of raw sensory information to create meaningful images, sounds, or feelings (like knowing an object is a "cup").

Distal vs. Proximal Stimulus

• Distal stimulus: This is the real object or energy in the world (e.g., a perfectly round hula hoop).

• Proximal stimulus: This is the pattern of energy that actually hits our sense organs (e.g., the hula hoop might look like an oval on your eye's retina if you look at it from an angle).

  • Our brain uses clues from both the simple sensory input and our knowledge to figure out what the real object is.

Eye ≈ Camera Analogy (With Caveats)

• Similarities:

  • Both have limited but good clarity.

  • Both have an adjustable opening (our pupil vs. a camera's aperture).

  • Both use a lens to focus light.

  • Both have a light-sensitive surface (our retina vs. film/sensor).

  • Our brain even has built-in "face detection" areas.

• Crucial difference: A camera just records; our visual system has to understand and interpret what it sees.

Neural Investment in Vision

• About $\frac{1}{3}$ to $\frac{1}{2}$ of our brain's outer layer (cortex) is used just for seeing.

• Problems with different parts of the vision system in the brain lead to different vision issues (e.g., damage to V1 causes blindness; damage to the lower part of the brain can cause problems recognizing objects; damage to the upper part can cause issues with movement and coordination).

Five Classical Senses (plus others)

• We usually talk about five main senses: sight, hearing, taste, touch, and smell.

• Each sense turns physical things (like light waves or chemicals) into what we experience as perceptions (like seeing colours or smelling a flower).

Focusing on Visual Perception

• Physical → psychological pipeline:

  • Light bounces off objects and enters our eyes.

  • Special cells in our eyes (rods and cones) turn light into electrical signals.

  • These signals travel through a nerve to a part of the brain called LGN, then to the visual cortex.

  • In the brain's cortex, these signals are processed so we can consciously see things like colour, size, distance, and movement.

Parallel Processing Architecture

• Our brain processes different features of what we see at the same time and independently:

  • Shape/form

  • Motion

  • Colour

  • Depth

• Then, our brain puts these pieces together to recognize objects (e.g., recognizing a face) or help us act (e.g., reaching to grab something).

Perceptual Achievements vs. Physical Challenges

• Achievements (how amazing our perception is):

  • Even though we have two eyes, we see one clear world.

  • Our eyes see things in 2D, but we experience a vivid $3D$ world.

  • When we move our eyes or head, the world around us still looks stable.

  • We can often recognize a whole object even if we only see parts of it (our brain \"fills in the blanks\").

  • We blink all the time, but our vision seems continuous.

• Underlying challenge: Making sense of what we see is a very complex calculation for our brain.

Demonstrations of Ambiguity & Multistability

• Static vs. dynamic Necker cube: A simple drawing of a cube can look like it's pointing in two different directions, and our brain can flip between these interpretations.

• “Flat street art”: Some street drawings look $3D$ because they play tricks with our eyes using perspective (how things look closer or farther away).

Why Study Perception / Illusions?

• Diagnostic tool for brain damage: Studying how people perceive things can help doctors find brain damage (e.g., if someone ignores one side of space, or has trouble recognizing objects).

• Foundation for design: Knowing how we perceive helps design things like art, virtual reality (VR), and flight simulators, so they work well with how our brains naturally see.

• Illusions reveal:

  • Where our perception system can break down.

  • The hidden guesses our brain makes to interpret the world.

  • That what we see isn't always exactly what's on our retina; we live in a world that our brain interprets.

Classic Illusions & Sensation vs. Perception Examples

• Müller-Lyer-type line trio (AB vs. BC) demo:

  • The actual physical lengths of the lines are the same (this is sensation).

  • But they look like different lengths (this is perception) because our brain uses the angles at the ends and tries to guess depth or processes the whole picture.

• Checker-shadow illusion (Adelson):

  • Squares A and B on the checkerboard have the exact same brightness.

  • However, because of the surrounding shadow and how bright the other squares are, square B looks lighter. This shows how context changes our perception.

Theoretical Frameworks

1. Constructivism (Top-Down)

• Main thinkers: Helmholtz (unconscious inference), Gregory (hypothesis testing).

• Basic idea:

  • The image on our eye isn't enough; our brain builds what we see using what it already knows, past experiences, and expectations.

  • What we perceive are like educated guesses, so illusions happen when our brain makes the wrong guess.

• Issues:

  • How do babies see if they don't have much past experience yet?

• Examples:

  • Necker cube: Our brain tries out two different guesses about its depth.

  • Ponzo illusion: Converging lines (like train tracks) make us think there's depth, so our brain makes the top bar/person seem bigger because it looks like it's farther away.

  • Impossible figures (Penrose triangle): These show how our brain tries to build a 3D shape, even if that shape can't exist in the real world.

2. Direct Perception (Gibsonian, Bottom-Up)

• Main thinker: J.J. Gibson.

• Basic idea: The environment itself provides enough rich information for us to directly see things.

  • “What you see is what you get” – no need for the brain to do complex rebuilding.

  • The environment offers “affordances” (what an object can be used for), and our perception directly picks up on these.

• Examples: How textures change as they get farther away, or how the size of an object changing in our vision tells us how close it is (like a ball getting bigger as it flies towards us telling us when it'll hit).

• Critiques: Do we really use all the information out there directly? How does our brain process all that rich data?

• Ponzo reinterpretation: For the Ponzo illusion, this theory suggests the perceived size difference is because the lines cover more or less “ground” in our visual field.

3. Information-Processing / Computational (Marr)

• Main thinker: David Marr.

• Main idea: He thought the brain works like a computer, processing information step-by-step.

• Marr’s three levels of analysis:

  1. Computational: What is the overall goal? (e.g., to figure out the shape of surfaces).

  2. Algorithmic/Representational: What steps (algorithms) and ways of storing info (representations) does the brain use to reach that goal? (e.g., finding edges, using both eyes to see depth).

  3. Implementation: How is this actually done in the brain's hardware (neurons)? (e.g., specific neural circuits).

• Hierarchical stages (steps the brain takes):

  • Primal sketch: First, the brain finds basic features like lines, spots, and bars.

  • $2.5D$ sketch: Then, it figures out surfaces and their depth as seen from our viewpoint.

  • $3D$ model: Finally, it creates a general model of the object that doesn't depend on our viewpoint, allowing us to recognize it.

Additional Illusion Sets

• Horizontal-Vertical illusion: A vertical line often looks longer than a horizontal line of the same actual length.

• Restaurant/consumer trickery:

  • Larger plates make food portions look smaller.

  • The shape of a glass can make us think there's more or less liquid than there actually is.

Practical & Ethical Implications

• Design: How we design user interfaces for apps, signs, and safety equipment needs to consider how people naturally perceive things.

• Marketing/Food industry: Using plate and glass sizes to influence how much people eat raises ethical questions about manipulating consumers.

• Clinical: Understanding how perception can go wrong helps doctors diagnose and treat neurological conditions (like helping people recover from brain injuries).

Outstanding Explanatory Challenges

• Retinal image properties:

  • The image on our eye is upside down, constantly wiggling, 2D, and we only see clearly in the very center.

• Yet, we experience the world as upright, stable, immersive, and full of detail. How our brain magically corrects for these issues is still an active area of research.

Key Take-Home Points

• Perception isn't just passively copying what's out there; it's an active, guessing, and sometimes wrong process.

• Different theories (Constructivist, Gibsonian, Computational) each give us helpful insights into how perception works.

• Studying illusions helps us discover the hidden rules, limits, and brain processes that are usually invisible to us.