Part 2: Cognitive Psychology Curriculum

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Welcome back to the Deep Dive.

Today, we are doing something a little different.

We aren't looking at a business case study or a historical event or a new tech trend.

We are topping the hood on the machine that lets you process all of those things in the first place.

We're talking about the brain.

We are talking about the human mind.

And we're not doing this in a spiritual abstract, what is the soul kind of way.

We are talking about the mechanics,

the wetware.

Right.

We are looking at the mind as an information processing system.

Exactly.

We are on a mission today.

We are working through a comprehensive last minute lecture summary of part two of Carol Brown's cognitive psychology.

This is the course companion to the curriculum.

Yeah.

And honestly, reading through the source material, it feels less like traditional psychology and more like we are reverse engineering a supercomputer.

That is the perfect analogy.

And it's exactly how the text frames it.

This entire section is dedicated to the architecture of cognition.

The architecture of cognition.

I like that.

The central idea here is that the mind isn't a black box.

It's like a busy factory floor or a massive computer network.

It takes in raw data from the outside world.

That's the input.

All the sights, sounds, everything.

Everything.

And it processes it through layers of silters and systems and eventually outputs what we call thoughts, decisions, and actions.

It's a massive system, though.

It's not just a clean circuit board.

So before we get lost in the weeds, what is our lot of ground to cover in these chapters?

We represent the last minute lecture team, so we need to be structured.

We are going to follow the data path.

Think of it as a narrative arc.

We start with perception, how we physically get data in from the world.

How we see and hear.

Exactly.

Then we move to attention, how we filter that noise so we don't go crazy.

Then memory, how we store and retrieve that information.

Then what?

Then language, how we communicate it.

And finally, high level thinking, how we use that data to solve problems and make decisions.

So it's a cumulative story.

You can't make a decision if you haven't perceived the problem, paid attention to the details, or remembered the rules.

They all build on each other.

These chapters aren't isolated.

Got it.

So let's stop wasting time and jump straight onto the factory floor.

Section one, visual processing and perception.

Okay, so the fundamental question in chapter 2 .1 is simple but profound.

How do we make sense of raw sensory data?

Right.

Your eye receives light.

That's it.

Focons hitting cells.

How does that become a chair or a face or a sunset?

And this brings us immediately to one of the oldest title fights in psychology,

nature versus nurture.

Always.

Are we born with the software pre -installed or do we have to code it ourselves after birth?

Well, the evidence suggests it's a bit of both really.

But the nature side, the innate ability,

has some fascinating support.

Let's look at Fance's visual preference task from 1961.

Okay, walk us through that.

Fance took infants, some as young as four days old, and showed them a variety of patterned discs.

Some were blank.

Some had jumbled, scrambled features.

Just random shapes.

Random shapes.

And some had features arranged like a proper human face.

Now, logically, a four -day -old baby shouldn't know what a face is.

They haven't learned social skills yet.

You would think not.

And yet they had a preference.

A strong one.

They stared significantly longer at the discs that resembled a human face.

Even at four days old?

Even then.

This suggests that some level of pattern recognition, specifically for social stimuli, is hardwired.

We come out of the womb looking for faces.

It's a survival mechanism.

That makes perfect sense.

You need to bond to survive.

But what about navigating the physical world?

You'd think depth perception, knowing not to crawl off a ledge, would be something you'd want pre -installed.

Oh, for sure.

You don't want to tumble down the stairs the first time you move.

Right.

And the classic visual cliff experiment by Gibson and Walk in 1960 tested exactly that.

I want you to visualize this setup because it's ingenious.

I'm ready.

Imagine a large glass table.

Underneath half the glass, pressed right up against it, is a checkerboard pattern.

Okay, so it looks solid.

Safe.

Totally solid.

But on the other half, the checkerboard pattern is way down on the floor, several feet below the glass.

Ah.

So to the eye, it looks like a steep drop off a cliff.

A cliff.

Yeah.

But structurally, it's just a flat sheet of glass.

It's perfectly safe to crawl on.

But it looks terrifying.

Exactly.

So they put babies aged 6 to 12 months on the shallow side.

Then they had the mothers stand on the deep side, the cliff side, and call the babies to come to them.

But what did they do?

The babies refused.

They would cry.

They would pat the glass.

They wanted to go to their mothers.

But they wouldn't cross the cliff.

They saw the draw.

They perceived the depth and the

This suggests the depth perception is largely innate.

The hardware is there.

So that's a big point for nature.

But surely the environment nurture plays a role?

Oh, absolutely.

I mean, if I'm raised in a closet, my vision won't develop the same way as if I'm raised in a forest.

That's where nurture hits back.

Yeah.

The text details a study by Blakemore and Cooper involving kittens.

Now, this is one of those studies that probably wouldn't get past an ethics committee today.

They raised kittens in these tall cylinders that had only vertical stripes, no horizontal edges anywhere in their environment.

So their entire world was just up and down.

A completely vertical world.

And when they released these kittens into a normal room later, they were essentially blind to horizontal lines.

No way.

Yes.

They would bump into chair legs because they literally hadn't developed the neural pathways to process that orientation.

That is wild.

It's like if you don't use it, you physically lose the ability to see it.

Precisely.

It shows that while the potential is innate, the specific physiological pathways in the visual cortex need environmental input to develop.

And speaking of physiology, we should probably trace the path of light because the book makes a point of the biological how.

Good call.

So light hits the cornea, go through the iris and the lens, which accommodates shade to focus and hits the retina at the back of the eye.

And the retina is where the magic starts.

That's where it turns into a neural signal.

It has five layers of cells.

The signal travels from there to the lateral geniculate nuclei in the thalamus.

The brain's big relay station.

It's a relay station.

Exactly.

And then to the visual cortex.

But here is the cool part.

The text distinguishes between two mainstreams or pathways coming out of there.

The parvocellular and the magnocellular.

Exactly.

The parvocellular pathway is the what is it path.

It handles color and fine detail.

The magnocellular pathway is the where is it going path.

It processes movement.

So even at that low level, the brain is already splitting up what things look like and how things move.

It's already sorting the data.

Okay, so we have the hardware,

but the world is chaotic.

I'm looking at you, the microphone, the window, the books.

It's a mess of colors and shapes.

How do we organize all these blobs into coherent objects?

That brings us to Gestalt theory.

The central idea here is the Law of Pregnance.

Which is a German term.

Right.

It basically means good figure or simplicity.

Our brain is efficient.

Or you could say lazy, depending on how you look at it.

I like efficient.

It tries to perceive the simplest best structure possible out of whatever it's looking at.

Can you walk us through some of the specific laws mentioned in the chapter?

Sure.

There's the Law of Proximity.

Things that are close together are grouped together.

If you see a bunch of dots, and four are clumped here and four are clumped there, you see two groups, not eight random dots.

Right.

And then there's similarity.

Yep.

Things that look alike are grouped.

If you have a grid of red and blue dots, you'll see columns of red and columns of blue.

What about when we see things that aren't actually there, like connecting the dots?

That's closure.

If I draw a circle but leave a small gap in the line, your brain doesn't see curved line.

It sees circle.

It automatically fills in the gap to create a whole shape.

And the last one is apparent motion.

Yeah, this is how neon signs in movies work.

Nothing is actually moving on the sign.

It's just light A turning off and light B turning on.

But your brain groups the flashes and says, that arrow is moving right.

It's a brain's autocorrect for vision.

That's a great way to put it.

Now, we touched on depth perception with the visual cliff.

How exactly do we calculate distance?

I know stereopsis is the big one, using two eyes.

Right.

Stereopsis uses the slight difference between what your left eye sees and what your right eye sees to triangulate depth.

But the text also lists monocular cues hacks you can use with just one eye.

So things artists use to create depth in a painting.

Exactly.

Like linear perspective parallel lines, like train tracks converging in the distance.

Interposition.

If one object overlaps another, the one blocking the view is closer.

And texture gradients.

Right.

Detail disappears the further away something is.

A pebbly beach up close is detailed.

Far away is just a smooth color.

Your brain uses all of these to build a 3D map.

So we have all these mechanisms, but there seems to be a philosophical split in the text on how this actually works.

We have Gibson and we have Gregory.

What's the beef there?

It's the classic battle between bottom up and top down processing.

Gibson argues for bottom up or direct perception.

He says the environment, what he calls the optic array, provides all the info we need.

We just pick up affordances.

A chair affords sitting.

A knob affords turning.

We don't need to think about it.

We just perceive its function directly from the visual data.

And Gregory's view.

Gregory argues for top down or constructivist theory.

He says, hold on, sensory data is often poor or ambiguous.

He thinks we use past experience and schemas to guess or construct what we are seeing.

So we're building reality, not just seeing it.

According to Gregory, yes.

And this explains optical illusions.

An illusion is the brain making a bet based on past experience and losing the bet.

That's a great way to put it.

Okay.

So moving on from just seeing raw shapes, let's talk about section two, object recognition.

How do I know that the thing I'm seeing is a chair, whether it's from the front, the side, or upside down?

This is a massive computational challenge.

And early theories were a bit too simple.

Take template theory.

Okay.

The idea was that we have a stored template, like a cookie cutter for every object in our memory.

To recognize a letter A, we match the image to the A template.

But wouldn't you need a billion templates?

An A in different fonts, sizes, angles?

Exactly.

It's incredibly inefficient.

So then psychology moved to prototype theory, where you match to a general average concept rather than an exact match.

But the text highlights feature theory as a more robust approach.

Which is?

You break objects down into their basic components, like lines, curves, and angles.

This brings us to the pandemonium model by Selfridge, which I love just for the visual.

Paint a picture for us.

It's such a colorful analogy.

Imagine your mind is full of demons, all shouting.

Okay.

First, image demons see the raw object.

They pass that to feature demons, who start shouting if they see their specific feature, like a vertical line or a sharp curve.

So they're specialists.

Exactly.

Then cognitive demons, listen to all that shouting.

If the cognitive demon for the letter R hears a lot of shouting about a vertical line, a curve, and a diagonal leg, it starts shouting, oh, rah, rah, too.

And then finally a decision demon listens to, who is shouting the loudest and says, it's an R.

It sounds chaotic, but it explains how we build up recognition from simple features.

Is there a more 3D version of this in the text?

Yes.

More is computational theory.

It's a step -by -step modeling process.

You start with a raw primal sketch, just light intensities and edges,

then the full primal sketch shapes and outlines.

Okay, that makes sense.

Then you get the 2 .5D sketch.

Wait, what is 2 .5D?

It's depth and orientation, but only from your perspective.

It's viewer centered.

It knows the front of the car is closer than the back, but it doesn't have a full model of the car itself.

So the final stage is the full model.

Exactly.

The 3D model, which is an independent representation.

This is when you know the mug has a handle, even if you can't see it from your angle.

And Biederman took this further with genes.

Right.

Biederman's genes theory is brilliant.

It suggests that all objects are made of 36 basic 3D shapes called genes.

Things like cylinders, blocks, cones.

The building blocks of vision.

Basically.

If you can see the edges, what he calls invariant properties,

and identify the genes and how they're arranged, you can identify the object.

A flashlight is just a cylinder gene plus a cone gene.

Now, what about faces?

Are they just another object made of genes?

No, the text is clear that face recognition is special.

It's a different system.

So it gets its own model.

Bruce Young's model shows it's a distinct process.

It involves a specific path.

First, structural encoding.

Building the face mentally.

Then expression analysis.

Are they smiling?

Frowning.

Then facial speech.

Like lip reading.

Kind of.

Then personal identity nodes.

Connecting the face to a memory of that person.

And finally, name retrieval.

Which explains why you can recognize a face at a party but completely blank on the name.

Exactly.

Name retrieval is the very last step in a long chain.

If the chain breaks early,

you recognize the face but can't place it.

We've all been there.

Okay, let's switch gears from eyes to ears.

Section three.

Speech and word recognition.

How do we turn sound waves into words?

Again, we see this interplay of bottom up, the raw data, what we hear, and top down, what we expect to hear, processing.

And top down is really important here, right?

Crucial.

The text mentions the phonemic restoration effect.

Give us an example of that.

If you were listening to a sentence like, the state governors met with their respective legislatures and someone coughs right over the S sound in legislatures, you don't hear a gap.

You just hear the word normally.

You physically hear the missing sound because your brain fills it in based on the context.

That's top down processing, filling in the gaps of reality.

Wild.

What about the mechanics of recognizing the word itself?

There are a few competing theories.

The motor theory is interesting.

It suggests we recognize words by mentally mimicking the mouth movements of the speaker.

We sort of say it internally to understand it.

But the big one is the cohort theory.

Right.

By Marslin Wilson.

He called it the elimination game.

How does it work?

Imagine you hear the sound spa.

Immediately, your brain activates every single word that starts with that sound.

Spaghetti, spaniel, spat, spark.

That's the cohort.

Okay.

A whole crowd of words.

A crowd.

As the next sound comes in, spag.

Words like spaniel and spark get eliminated.

You keep narrowing it down until only one word is left.

That's the recognition point.

And it happens so fast we don't even notice.

And models like trace and the interactive activation model suggest this isn't just a one -way street.

Features activate letters, letters activate words, but words can also feed back down to help recognize letters.

It's a highly interactive network.

Okay.

So we're perceiving all this stuff.

Visual, auditory, tactile.

If we processed everything at once, our heads would explode.

We'd be overwhelmed.

We need a filter.

This leads us to section four.

Attention.

Attention is the gatekeeper.

And we generally talk about focused attention, the spotlight and divided attention, or multitasking.

Let's start with focused.

The classic study here is Cherry's cocktail party problem.

Right.

Imagine you are at a party.

It's loud.

You're listening to one person.

Cherry simulated this with a shadowing task.

Okay.

Describe that for the listeners.

You wear headphones.

One message plays in your left ear.

A different one in your right.

You have to repeat one message out loud.

You're shadowing it and ignore the other.

And what happens to the ignored message?

You barely process it.

You might notice if the voice changes from male to female.

Big physical change.

But you won't notice if the language changes from English to German or if the speech is reversed.

So you miss the meaning entirely.

Totally.

This led to the bottleneck theories of attention.

The idea that only so much can get through.

Exactly.

And broadband's filter theory proposed that the bottleneck was absolute.

An early filter.

Unattended info hits a buffer and is simply rejected.

It doesn't get through to processing at all.

But that was too strict, wasn't it?

Way too strict.

Because at a cocktail party, if someone across the room shouts your name, you hear it.

Right.

That breaks through.

It breaks through.

That shouldn't happen if broadband was right.

So Treisman proposed the attenuation theory.

The volume knob theory.

The volume knob.

Perfect.

It's not a wall.

It's a filter that turns things down.

Unattended info isn't blocked.

It's just quiet.

But if a signal is strong enough or important enough, like your name or the word fire,

it breaks through the threshold.

What about Deutsch and Deutsch?

They went further, suggesting late selection.

They argued everything gets processed for meaning, but we only become conscious of and respond to what is most relevant.

It's a debate about where exactly the filter sits early or late in the process.

And visually, we have the spotlight and zoom lens theories.

Spotlight theory says attention is like a beam of light.

We move around.

Zoom lens theory adds a layer.

We can widen the beam to see the whole scene with less detail or narrow it to focus intensely on one small part.

Let's talk multitasking.

We all like to think we're good at it.

But we aren't.

Not really.

Kahneman's central capacity theory suggests we have a single pool of mental energy or resources.

So it's a limited budget.

A limited budget.

Yeah.

If a task is easy, it takes a cup of energy.

You have plenty left.

If a task is hard, it drains the whole pool.

You can't do anything else.

But sometimes we can do two things at once.

I can drive and listen to music.

That's where Alport's modular theory comes in.

He argues we have different modules or processing systems.

You could do a visual task driving and an auditory task listening together because they use different machinery.

So they don't interfere.

Right.

But try to write an email while listening to a podcast.

Two language tasks and you'll fail miserably.

The module is jammed.

Got it.

So we've perceived, we've attended.

Now we need to keep it.

Section five, the multi -store model of memory.

This is the OG blueprint of memory.

Atkinson and Schifrin gave us the classic multi -store model or MSM.

Think of it as a flow chart.

Okay.

Information comes into sensory memory.

A tiny bit moves to short -term memory.

And when we rehearse it, it can move to long -term memory.

Three separate stores.

Let's break this down.

Sensory memory.

It has a huge capacity but a tiny, tiny duration.

Sperling showed this with flashing letters.

He flashed a grid of letters for a fraction of a second.

And people saw them all.

They saw them all.

But the memory faded before they could say them all.

It's like a spark that's gone in an instant.

And short -term memory or STM is the bottleneck.

Yes.

George Miller gave us the famous magic number.

Seven plus or minus two, that's all we can hold.

A phone number, basically.

Pretty much.

And Peterson and Peterson showed that without rehearsal,

info just decays and has gone from STM in about 15 to 30 seconds.

And then long -term memory, LTM, is the unlimited warehouse.

Correct.

The strongest evidence for these separate stores is the serial position effect.

Right, the U -shaped curve.

The U -shaped curve.

If I give you a list of words to remember, you remember the start.

That's the primacy effect because those words had time to get rehearsed into LTM.

You remember the end.

That's the recency effect because they are still sitting in STM.

And no one gets lost.

The middle falls into the gap.

So why do we forget, though?

Is it just fading away?

That's one reason, trace decay, the memory literally fades over time.

There's also displacement new info pushes old info out of the short -term bucket.

And then there's interference.

Proactive and retroactive.

Right.

Proactive interference is when old memories block new ones.

Like when you can't remember your new password because your old one is stuck in your head.

Retroactive is when new memories overwrite old ones.

And sometimes the memory is there.

We just can't find it.

Cue -dependent forgetting.

This is based on Tolving's encoding specificity principle.

Memory is in the library, but you've lost the index card.

You need the right cue.

A smell, a place, a mood to trigger the retrieval.

Now, the MSM was a bit too simple, wasn't it?

It treated short -term memory like a passive bucket.

It really did.

Enter battling hitch.

Section six, the working memory model.

This was a huge upgrade in 1974.

They argued STM is an active workspace, not just a holding pen.

They called it working memory.

And it has components.

So it's not one thing.

It's a multi -component system.

The boss is the central executive.

It makes decisions and allocates focus.

It's the attention controller.

Then you have the slave systems.

The minions.

The minions, exactly.

The phonological loop is your inner voice and inner ear.

It handles sound and language.

If you're repeating a phone number in your head, that's the loop.

Okay.

And the visuospatial scratch pad?

That's the inner eye.

It handles shapes, layout, and where things are in space.

And the proof for this distinction comes from dual task studies.

Tell me about those.

You can do a visual task, like imagining the layout of your house, and a verbal task, like counting backwards at the same time.

Because they use different systems.

Different systems.

But try to do two visual tasks, like tracking a moving light and imagining your house.

And you crash.

The scratch pad gets overloaded.

Okay.

Moving to long -term memory.

It's not all just one big blob, either.

Section seven.

Types of long -term memory.

Colvin gave us the key distinction here.

He said we have episodic memory and semantic memory.

Okay.

What's the difference?

Episodic is mental time travel.

It's your personal experiences, your last birthday, what you had for breakfast.

It's knowing when something happened.

It's tied to a time and place.

And semantic.

Semantic is the encyclopedia in your head.

It's facts.

Knowing that Paris is in France.

You know what it is, but you probably don't remember the specific moment you learned it.

So one is a story, the other is a fact sheet.

That's a great way to think about it.

And they use different brain areas.

The prefrontal cortex is heavily involved in the episodic storytelling stuff.

There's also this weird thing mentioned in the text called implicit learning.

This is fascinating.

It's learning without realizing you are learning.

Barry and Broadbent demonstrated this with a sugar factory computer simulation.

The sugar factory?

Participants had to manage a factory's sugar output by adjusting variables.

It was based on a really complex formula they weren't told.

Over time, they got really good at it.

But could they explain how?

No.

When asked how they did it, what the rules were, they couldn't explain them.

They had learned the complex system implicitly without conscious awareness.

It's like intuition.

Now let's talk about a specific type of processing.

Reading.

Section 8.

We think our eyes glide across the page, but they don't, do they?

Not at all.

They jerk.

These little jumps are called saccades.

And cognitively, when we read, we use a dual root model.

Root 1 and root 2.

Root 1 is grapheme -phonome conversion.

That's sounding it out.

You use this for new words or nonsense words like blick.

And root 2.

That's the lexical route or semantic route.

You recognize the whole word as a picture and access its meaning directly.

You don't sound out the word the or yacht.

You just know it.

And dyslexia gives us evidence for this separation.

Perfect evidence.

Phonological dyslexia is where root 1 is broken.

You can't sound out new words.

Surface dyslexia is where root 2 is broken.

You can't recognize irregular words.

So someone with surface dyslexia might.

They might look at the word plaint and say it rhymes with mint because they're forced to use root 1, the sounding out route, and it fails them on irregular words.

Wow.

But reading isn't just decoding, it's understanding.

Comprehension, yes.

And here we reconstruct meaning.

Bartlett's War of the Ghost study from 1932 is the classic example here.

Tell us about that.

He had British participants read a Native American folk tale that had a very different structure and supernatural elements.

Then he asked them to recall it later.

And what happened?

They changed the story.

They normalized it to fit their own culture.

They replaced hunting seals with fishing.

They completely omitted the ghost because it didn't fit their logic.

So they rewrote the memory.

They rewrote the memory to fit their own cultural schemas.

It proves memory is reconstructive, not a recording.

We edit reality to make it make sense to us.

We rewrite the script to fit our worldview.

Speaking of scripts, let's look at language production in section 9.

How do we get the words out?

It's a cooperative, goal -directed process.

But the mistakes we make tell us how it works.

We have slopes of the tongue.

Right.

Dell's spreading activation theory suggests that when you're preparing to speak, multiple words and sounds get activated in the brain's network.

And sometimes the wrong one wins the race.

Like saying, cuff of coffee instead of cup of coffee.

Exactly.

That's an anticipatory error.

You're grabbing the F from coffee too early.

Yeah.

We also have exchange errors where you swap sounds like a blushing crow instead of a crushing blow.

And the Weaver Plus Plus model by Lavelle tries to map this out.

It does.

If you speech is a very logical computer -like process, you start with the concept, then select the lemma, which is like an abstract word without its sound.

Then you select the morphemes and phonemes.

And finally, articulation.

And this is all happening in specific brain areas.

Broca's area for production and grammar.

Wernicke's area for understanding and meaning.

The two famous ones.

Exactly.

If you damage Broca's, you understand but can't speak fluently.

It's Broca's speech.

If you damage Wernicke's, you can speak fluently, but it's meaningless word salad.

Okay, we are in the homestretch.

High -level thinking, section 10, problem -solving.

We have two main approaches here in the text.

The gestalt approach is all about inside that aha moment.

Light bulb moment.

That light bulb moment.

It's about restructuring how you see the problem.

Mayor's pendulum problem is the great example.

Describe the room for us.

You are in a room with two strings hanging from the ceiling, too far apart to grab both at once.

You also have a few random objects, including a pair of pliers.

The goal is to tie the strings together.

And people get stuck.

They get stuck because of functional fixedness.

They see the pliers only as a gripping tool, their normal function.

So what's the solution?

The insight is to see the pliers differently.

Use them as a weight, tie them to one string, swing it like a pendulum, run to the other string, grab it, and then catch the swinging pliers.

You have to overcome your fixed idea of what pliers are to solve it.

Precisely.

Then there's the information processing approach by Newell and Simon.

This is much more methodical.

They view problem -solving as navigating a problem space.

You start at the initial state and need to find a path to the goal state.

Think of the Tower of Hanoi puzzle.

With the discs and pegs.

Right.

Moving discs from peg to peg with specific rules.

How do we navigate that space?

We use algorithms which check every possibility guaranteed solution, but very slow.

Or we use shortcuts.

Or we use heuristics rules of thumb.

Means ends analysis is a big one.

You break the huge problem into smaller sub -goals.

To get the big disc to the end, I first need to move the small disc out of the way.

Finally, let's look at how we make choices.

Section 11.

Judgments, decisions, and reasoning.

We like to think we are logical creatures, but...

But we are usually not.

We rely heavily on these mental shortcuts, these heuristics.

The representativeness heuristic is when you judge something based on stereotypes.

Give me an example.

If I describe someone as quiet, intellectual, and wearing glasses, you might think librarian.

Ignoring the statistical base rate that there are way, way more salespeople than librarians in the world.

Right.

And the availability heuristic.

That's judging probability based on how easy it is to remember examples.

We fear plane crashes more than car crashes, because plane crashes are dramatic, vivid, and easy to recall, even though cars are statistically far more dangerous.

What about when money is involved?

Are we rational, then?

Even less so.

We have utility theory, which is a logical cost -benefit analysis.

But humans suffer from loss aversion.

We feel the pain of losing $10 way more than we feel the joy of finding $10.

And the sump cost effect.

Oh, yeah.

We throw good money after bad just because we've already invested time or money.

I've already watched an hour of this terrible movie.

I might as well finish it.

And framing?

Framing is huge.

Yeah.

If I tell you a surgery has a 90 % survival rate, you'll probably take it.

If I tell you the exact same surgery has a 10 % mortality rate, you might not.

Same stats, different frame, different decision.

And finally, logic itself.

Deductive reasoning.

This is the if A, then B stuff.

Modus ponens is easy.

If A is true, B is true.

We get that.

But we really struggle with the negatives.

And we often fall for the logical fallacy called affirmation of the consequence.

Explain that one.

If I say, if it rains, the street is wet, and then I tell you the street is wet, you might incorrectly assume it must have rained.

But maybe a street cleaner just went by.

B, being true, doesn't prove A is true.

Wow.

Okay, let's just recap this journey for a second.

We started with a single photon hitting the retina.

A single bit of data.

We watched that signal get processed into shapes and objects.

We saw how we filter all that noise with attention, store it in memory, reconstruct it with language, and finally use it to solve problems and make decisions that are often well flawed.

It really highlights that the world we experience is an end product.

We covered perception, memory, language, and thought.

But the thread connecting them all is processing.

The mind isn't a mirror reflecting reality.

It's a factory manufacturing it.

That brings us to our final thought.

I want to leave the listener with this question.

If our perception is constructed, if our memory is reconstructed and edited every time we access it, and if our decisions are biased by how things are framed,

how much of your reality is objective truth?

And how much is just a cognitive creation?

Something to keep you up at night.

This has been a deep dive into part two of cognitive psychology.

A warm thank you from the Last Minute Lecture Team for listening.

Keep thinking.