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Cognition

April 11, 2023 by ktangen

Judgments

Filed Under: Cognition

April 5, 2023 by ktangen

Metacognition

Metacognition

Metacognition

We know a lot of information. The older you get, the more information you encounter. Learning facts, concepts and behaviors gives you knowledge. Knowing that you have facts concepts and behaviors is metacognition.

Meta (larger than) and cognition (thinking) is what you know about the process of thinking. It is your self-awareness of how your mind works. It is thinking about thinking.

We have to know how our minds work in order to adjust its activity. Fortunately, we have processes that track our thinking processes. We have an executive thinking process which keeps track of our systems and our general progress.

Our minds gather and structure information to use it. All three parts are important: gathering, structuring and using.

Encoding

The technical name for gathering is encoding. It is a critical first step. If we don’t gather information–if we don’t put it in–it is not available to use.

As it turns out, we are both exceptionally good and bad at gathering information. On the good side, we can listen to a band and focus more on one instrument than another. It’s like zoom-listening. We can change back and forth between vocals, guitar, piano and bass. We are great at taking a complex audio input and dividing it into separate threads.

We are also very good at hearing our name in a crowded room. This is called the Cocktail Party Effect. Surrounded by lots of conversations, we can “tune out” all the ones that don’t interest us and have a conversation with one person. This is remarkable.

Even more remarkable is that in the middle of this intense conversation surrounded by noise, we can detect someone in another conversation saying our name. This is a great skill and one not well understood.

English psychologist Donald Broadbent proposed a filter theory to explain the cocktail party effect. He found that two messages delivered at the same time to both ears (like two people talking to you at the same time) made it hard to recall either of them. But when two messages were delivered separately (one to each ear at the same time), people were able to listen to one and ignore the other.

Broadbent’s filter theory now has a lot of exceptions. We process more of what we’re ignoring than he thought. The main premise remains: there is no identification without attention. Once our priority is set, our attention is focused. We give our main attention to one stream but process the rest in the background. The background processing isn’t listening to everything but more a matter of looking for exceptions. We switch our attention if we detect key words (our name, “fire”) or sounds (a baby’s cry, a loud bang).

On the bad side, once our focus is set, we ignore things we think we’d notice. We ignore unexpected objects, including a gorilla in the middle of basketball players.

In a now famous study, Christopher Chabris and Daniel Simons filmed two groups of basketball players tossing a ball to each other. Subjects were told to watch one group and ignore the other. The task was to count how many times the ball was thrown by one team. In the middle of the film, while all the moving and throwing is going on, a man dressed in a gorilla suit enters, strikes his chest and walks off screen.

It is about 50/50 for those who notice the gorilla and those that don’t.

We usually expect we would notice the unusual. Turns out, it may just be chance. We either see it or we are inattentionally blind. Our attention system doesn’t pick it up on our mental radar.

In addition to not noticing unexpected objects (inattentional blindness), we often don’t notice changes in things we are attending to. This is change blindness. When images flash on and off, we assume the new image is the same as the old one. Film editors take advantage of this after noticing that most of the audience doesn’t detect even major changes in background images.

Our eyes don’t stay still. We are not like birds. We are always scanning. These movements (saccades) make our eyes jump back a bit. We don’t notice an image has changed if the change occurs during a saccade.

If we are talking to a person but are interrupted by a blackboard being walked between us, we assume everything is the same when the person reappears. We don’t notice they are wearing a new shirt or that it is a completely different person.

Both inattentional blindness and change blindness are part of a larger cognitive rule. Our brains ignore steady-state information. You don’t know where your left elbow is until I mention you have one. When your attention is drawn to it, your body reports in. The brain says “Tell me if something changes but otherwise be quiet.”

This dismissal of steady-state information allows you to wear clothes without feeling them on your skin. It allows you get used to a busy or noisy environment. It allows you to ignore that you have blood vessels in front of your visual receptors.

Structuring

Attention is important to learning because it is a minimal requirement. You can’t learn it if you can’t see it. You can’t see it if you don’t notice it. Attention doesn’t guarantee learning but there is no learning without attention. Attention is necessary but not sufficient for learning to occur.

We use our attention to use the information coming in. We tend not to respond to individual elements. We form it into structures we can use. It is not structure making for love of structures. It is structuring information to do something with it.

Derek Cabrera suggests there are four universal structuring factors: distinctions, systems, relationships and perspectives. He sees them as skills we need to develop.

We need the skill of making more and more refined distinctions between ideas or objects. At first, every animal we meet is a dog. Then we learn the difference between dogs and cats and cows. Then we learn to distinguish between different breeds of dogs. I call this skill splitting.

We need the skill of see things as a system. We start with our family, then learn there are other families in the neighborhood. Then we learn we are part of a city, region, country, and continent. Cabrera calls this systems, some call this lumping. I call this skill organizing.

We need the skill of see relationships between ideas. We take one class and discover a whole new area of knowledge. As we take other classes, we learn that segments are the same. Some ideas in chemistry are present in other chemistry classes, in geology, in history and is public affairs. I call this connection skill relating.

We need the skill of seeing things from different perspectives. If I ask you to remember your house, you’ll recall certain items. But if I ask you to pretend you are a realtor, a buyer or a burglar, you will probably recall different items. Learning to see things from different perspectives gives you new insights. Since you’re always looking for something new, I call this skill prospecting.

I have a fifth skill to add. We need the skill of editing. Mental structures need modification and refinement. In addition to the parts distinction, system focus, relationship tracking and perspective taking, we need to learn how to modify our cognitive structures. Sometimes we get stuck with a set view that no longer serves us well. I call this skill editing.

I convert Cabrera’s DSRP into ROPES: relating, organizing, prospecting, editing and splitting. Both models recognize that we incorporate new information into what we already know. Learning doesn’t occur in isolation. We add new parts to existing cognitive structures.

Using

We collect information, add it to what we already know, and use it. Learning has a practical aspect. Unlike our closets, the brain doesn’t store things it doesn’t use. Consequently, the type of encoding depends on its use.

Visual encoding. This is the process of converting information into mental pictures. An obvious example is seeing an object or looking at a photograph. We use the same system regardless of whether we are looking at the Mona Lisa, a flower or a child’s drawing.

We capture a mental image of it plus an emotional reaction. The visual cortex processes the scene and the amygdala processes the emotion. A picture of Mom evokes an emotional reaction as well as a recognition of facial features.

Acoustic encoding. Everything we look at is processed as an image, unless they are words. Reading is translating symbols into sounds and sounds into images. Listening to an audio book produces the same mental images as reading a book because both are processed acoustically.

Tactile encoding. We process how something feels (smooth, soft, dense) with our tactile system. We are interpreting the vibrations on the skin and the pressure on touch receptors. We can both feel the texture of a keyboard and ignore it while we type.

Semantic encoding. When we need to use words, we encode items into our semantic system. We are quite gifted at extracting meaning from our inputs and storing that information to be used in the future.

Elaboration encoding. Elaboration is the process of associating information with other information. It combines new inputs with old information. We constantly update our structures. How you think about something depends on the day. It depends on what when on before and on what you expect to happen tomorrow.

Filed Under: Cognition, Memory

April 4, 2023 by ktangen

Fox & Cat

Expectations & Heuristics

The Fox and the Cat

I love stories of talking animals. These are the kinds of stories the old and wise tell to the young and foolish. The elders don’t hit you in the head with commands but guide you to discover the story’s application. You learn the concept and apply it to your own situation.

The Fox and the Cat is an ancient fable about expectations. The cat and fox are talking, as animals are want to do, about what they would do in case of an emergency. In fables, the emergencies typically are humans: hunters in particular.

The fox, being very smart, has many ways of escape. He is very clever, which he is quick to point out. The cat is impressed by the number and variety of options the fox has. Sadly, the cat has only one method: climb a tree. The fox scoffs but is interrupted by the arrival of the hunters. The cat climbs a tree. The fox is still pondering his options when he is captured.

The practical conclusion is that it’s better to have one safe exit than a hundred that don’t work. The original moral was probably to not boast about being clever. For our purposes let me suggest that the moral is about how expectations impact decision making.

Expectations are predictions. The cat expected (predicted) it would get caught but knew exactly what to do in case of emergency (run up a tree). The fox expected he’d get away, easily. He had many routes to choose from. As it turns out, thinking takes time and energy. We think fast but deciding between multiple alternatives is slower. Every channel you surf takes a little bit more time. In cognitive science terms, the fox got caught because it takes time to process options.

Deciding also takes energy. Making decisions is tiring. Making many decisions is very tiring. It depletes your mental energy. One reason it is hard to decide what to eat for dinner is depletion. After a long day of making decisions, your decider circuit is tired. Decision fatigue shows up as physical fatigue.

Decision fatigue can lead us to not making decisions. Rather than make another decision, you want to just avoid deciding altogether. Iyengar & Lepper found that the more choices you have, the less you want to decide anything. Facing six options is better than facing 26. You feel outnumbered when there are too many options. It’s a choice-war.

Additionally, your satisfaction decreases when you have too many options. You calculate the odds of happiness based on the number of options. The more options, the less likely you are to have selected the correct one. Then after making a choice, all you can think of is the that correct choice is probably one of the options.

Since having too many options also decreases our satisfaction with whatever we choose, how many is the right number? No one really knows. But here are some of the ways we handle decision making when we are fatigued.

First, we select the status quo. We go with whatever the current setting is. If the TV is on, we leave it on. If it is off, we leave it off. When we are wiped out, we try hard not to decide. We expel no effort.

Second, we choose the default setting. We may turn the TV on but leave it on whatever channel pops up. We expel limited effort.

Third, we use event substitution. This happens a lot in companies. We get everyone in a conference room to handle an issue. Then, instead of solving the marketing-manufacturing problem, we fight about what to have for lunch. We substitute a small fight in place of the larger war.

Fourth, on a smaller scale, we substitute components. This is also called reasoning by simplification. Instead of wrestling with a complex problem with many variables, we work on a simpler and less complex problem. Cognitive psychologists Kahneman & Frederick call this attribute substitution. We make analogies. “The parking problem is a lot like pizza delivery.” “Hunting for a new CEO is a lot like finding a new dry cleaner.” Some analogies are better than others but all serve the purpose of simplification.

Fifth, we substitute real issues with simple rules: heuristics. In contrast to a formula or algorithm which will always work (even if it is not the fastest method), a heuristic is a mental shortcut. It is fast and usually works.

The underlying meaning of heuristic is to find or discover a solution. They ease the cognitive burden of decision making. These “rules of thumb” are practical methods. They don’t guarantee success but they are derived from experience with similar problems. And they are readily accessible.

Obviously, the most fundamental heuristic is trial and error. But it is the least satisfying. Trial and error is what we do after we’ve tried everything else.

George Pólya proposed several heuristics in his book “How To Solve It.” Written in 1945, these suggestions have been around a long time. Pólya suggests drawing a picture of the problem situation and working the problem backwards. These ideas are related to the general suggestion of switching from abstract to concrete or concrete to abstract. They are suggestions on perspective.

You may be less familiar with the Inventor’s Paradox. It says that the more ambitious your plan, the more chances you have of succeeding. This might be a good spot to point out that heuristics aren’t always true. Your mileage may vary.

Herbert Simon offers two thoughts on heuristics in problem solving. He uses the terms “bounded rationality” and “satisficing.” Bounded rationality means that there are always limits to our decision making process. We often lack information (what our competitor will do), can’t track the entire process (we tend to think and think but then jump), and don’t have enough time to think things through (the traffic light is changing; right, left or straight?). We also are limited by our cognitive and computational abilities. There are limits to what we can do.

Simon’s second term, satisficing, is what we do when we run out of time and resources. We find a satisfactory solution which requires sacrificing perfection. When we can’t get optimal, we go for good enough.

German psychologist Gerd Gigerenzer says we should embrace satisficing. He calls this approach “fast and frugal.” He maintains that we get more accurate decisions if we don’t weigh all the options. We should ignore part of the information. It helps focus our attention and clarify our priorities.

The general principle is called the focusing effect. It is our tendency to put too much importance on one aspect of an event. We focus on the past experience of others (all these people became zillionaires) and not on our likelihood for success. If you ask “how much happier are tall people than short people,” the wording assumes a difference that doesn’t exist. But we go with it.

In Tversky & Kahneman’s study of human decision making, they note that we tend to rely too heavily on the first bit of information we receive. We make it an anchor, and make all of our calculations based on that pin. Once the anchor is set, our mental boat doesn’t drift very far. Manufacturer-suggested or list prices are there to make subsequent prices seem more reasonable. Saving 50% off of an inflated price sounds better to us than paying full price for a lower initial offer.

Dan Ariely likes to ask an audience to recall the last two digits of their social security number. These are chance numbers with no actual power or significance. He asks if they would pay that amount for several items (wine, computer, chocolates, etc.). Then he has them bid for the items. People with higher ending digits bid higher. Once we have an anchor, even if it is by chance, we tend to stay with it.

Anchoring is hard to avoid. It seems quite built in. If you were asked if Thomas Jefferson died before or after age 9 (or before-after age 120), it seems unlikely that this anchor would affect you but it often does. Students given similar options, guessed closer to their anchor, whichever one they received.

Julian Rotter proposes a more general expectation theory that has two major components: the size of the reward and the likelihood of receiving it. He says we balance both parts of the equation. We go for risky propositions if the reward is high but stay with predictability when the rewards are low.

Filed Under: Cognition, Learning

March 29, 2023 by ktangen

Regression

Day 6 Regression Day 6: Prediction

With regression, you can make predictions. Accurate predictions requires a strong correlation. When there is a strong correlation between two variables (positive or negative), you can make accurate predictions from one to the other. If sales and time are highly correlated, you can predict what sales will be in the future…or in the past. You can enhance the sharpness of an image by predicting what greater detail would look like (filling in the spaces between the dots with predicted values). Of course the accuracy of your predictions, depends on the strength of the correlation. Weak correlations produce lousy predictions.

Regression is like looking into the future or the past

An extension of the correlation, a regression allows you to compare your data looks to a specific model: a straight line. Instead of using a normal curve (bell-shaped hump) as a standard, regression draws a straight line through the data. The more linear your data, the better it will fit the regression model.Once a line of regression is drawn, it can be used to make specific predictions. You can predict how many shoes people will buy based on how many hats they buy, assuming there is a strong correlation between the two variables.

Just as a correlation can be seen in a scatterplot, a regression can be represented graphically too. A regression would look like a single straight line drawn through as many points on the scatterplot
as possible. If your data points all fit on a straight line (extremely unlikely), the relationship between the two variables would be very linear.

ScatterplotJ

Most likely, there will be a cluster or cloud of data points. If the scatterplot is all cloud and no trend, a regression line won’t help…you wouldn’t know where to draw it: all lines would be equally bad.

But if there the scatterplot reveals a general trend, some lines will obviously be better than others. In essence, you try draw a line that follows the trend but divides or balances the data points equally.

In a positive linear trend, the regression line will start in the bottom left part of the scatterplot and go toward the top right part of the figure. It won’t hit all of the data points but it will hit most or come close to them.

Regression line through data

You can use either variable as a predictor. The choice is yours. But the results mostly likely won’t be the same, unless the correlation between the two variables is perfect (either +1 or -1). So it matters which variable is selected as a predictor and which is characterized as the criterion (outcome variable).

Predicting also assumes that the relationship between the two variables is strong. A weak correlation will produce a poor line of prediction. Only strong (positive or negative) correlations will produce accurate predictions.

A regression allows you to see if the data looks like a straight line. Obviously, if your data is cyclical, a straight line won’t represent it very well. But if there is a positive or negative trend, a straight line is a good model. It is not so much that we apply the model to the data; more like we collect the data and ask if it looks this model (linear), that model (circular or cyclic) or that model (chance).

If the data approximates a straight line, you can then use that information to predict what will happen in the future. Predicting the future assumes, of course, that conditions remain the same. The stock market is hard to predict because it gets changing, up and down, slowly up, quickly down. It’s too erratic to predict its future, particularly in the short run.

Formula for a straight line

If you roll a bowling ball down a lane and measure the angle it is traveling, you can predict where the ball will hit when it reaches the pins. The size, temperature and shape of the bowling lane are assumed to remain constant for the entire trip, so a linear model would work well with this data. If you use the same ball on a grass lane which has dips and bulges, the conditions are not constant enough to accurately predict its path.

Predicting the future also assumes that the relationship between the two variables is strong. A weak correlation will produce a poor line of prediction. Only strong (positive or negative) correlations will produce accurate predictions.

A regression is composed of three primary characteristics. Any two of these three can be used to draw a regression line: pivot point, slope and intercept.

Formula for slope

First, the regression line always goes through the point where the mean of X and the mean of Y meet. This is reasonable since the best prediction of a variable (knowing nothing else about it) is its mean. Since the mean is a good measure of central tendency (where everyone is hanging out), it is a good measure to use.

Second, a regression line has slope. For every change in X, slope will indicate the change in Y. If the correlation between X and Y is perfect, slope will be 1; every time X gets larger by 1, Y will get larger by 1. Slope indicates the rate of change in Y, given a change of 1 in X.

Third, a regression line has a Y intercept: the place where the regression line crosses the Y axis. Think of it as the intersection between the sloping regressing line and vertical axis.

Regression means to go back to something. We can regress to our childhood; regress out of a building (leave the way we came in). Or regress back to the line of prediction. Instead of looking at the underlying data points, we use the line we’ve created to make predictions. Instead of relying on real data, we regress to our prediction line.

There are two major determinants of a prediction’s accuracy: (a) the amount of variance the predictor shares with the criterion and (b) the amount of dispersion in the criterion.

Taking them in order, if the correlation between the two variables is not strong, it is very difficult to predict from one to the other. In a strong positive correlation, you know that when X is low Y is low. Know where one variable is makes it easy to the general location of the other variable.

A good measure of predictability, therefore, is the coefficient of determination (calculated by squaring r). R-squared (r2) indicates how much the two variables have in common. If r2is close to 1, there is a lot of overlap between the variables and it becomes quite easy to predict one from the other.

Even when the correlation is perfect, however, predictions are limited by the amount of dispersion in the criterion. Think of it this way: if everyone has the same score (or nearly so), it is easy to predict that score, particularly if the variable is correlated with another variable. But if everyone has a different score (lots of dispersion from the mean), guessing the correct value is difficult.

The standard error of estimate (see) takes both of these factors into consideration and produces a standard deviation of error around the prediction line. A prediction is presented as plus or minus its see.

The true score of a prediction will be within 1 standard error of estimate of the regression line 68% of the time. If the predicted score is 15 (just to pick a number), we’re 68% sure that the real score is 15 plus or minus 3 (or whatever the see is).

Similarly, we’re 96% sure that the real score falls within two standard deviations of the regression line (15 plus or minus 6). And we’re 99.9% sure that the real score fall within 3 see of the prediction (15 plus or minus 9).

NextProbability 

Captain psychology

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  • What is statistics?
  • Ten Day Guided Tour
    • Measurement
    • Central Tendency
    • Dispersion
    • Z Scores
    • Correlation
    • Regression
    • Probably
    • Independent t-Test
    • One-Way ANOVA
    • Advanced Procedures
  • How To Calculate Statistics
  • Start At Square One
  • Practice Items
  • Resources
    • Formulas
    • Critical Values of t
    • Critical Values of F
    • Critical Values of r
    • Nonparametrics
    • Decision Tree.
  • Final Exam

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Filed Under: Cognition, Statistics

April 5, 2021 by ktangen

Intelligence

Einstein

Story

[Read more…] about Intelligence

Filed Under: Cognition

April 5, 2021 by ktangen

Problem Solving

puzzle cube

Story

 

Terms

  • 8 Ds
  • abstract intelligence
  • abstraction
  • ad hoc methods
  • adult education
  • algorithm
  • analogy
  • animal lab studies
  • arithmetic
  • behaviorism
  • being lost
  • blank slate
  • brainstorming
  • CAVD Test of Intelligence
  • completion
  • connectionism
  • content specific
  • current state
  • directions
  • divide and conquer
  • doctrine of formal discipline
  • domain knowledge
  • educational psychology
  • elements
  • empiricism
  • escape
  • experimental approach
  • factor analysis
  • general learning theory
  • getting around roadblocks
  • getting over obstacles
  • goal state
  • GROW
  • having no clue of what to do
  • hill climbing
  • hitting a dead end
  • How to Solve It (Pólya)
  • hypothesis testing  
  • ill-defined problems
  • ill-structured problems
  • innate ideas
  • lateral thinking
  • law of effect
  • law of exercise
  • law of readiness
  • local high
  • Locke, John
  • means-end analysis
  • mechanical intelligence
  • mental representation
  • method of focal objects
  • Mill, John & John Stuart
  • morphological analysis
  • multifactor theory of intelligence
  • negative transfer
  • neural bonds
  • OODA loop
  • operant conditioning
  • orderly manner
  • PDCA
  • positive transfer
  • problem definition
  • problem finding
  • problem metaphors
  • problem shaping
  • problem solving
  • problem space
  • problem-cycle
  • proof
  • psychometrics
  • punishment
  • puzzle boxes
  • reasonable creatures
  • reduction
  • research
  • responses
  • retrograde analysis
  • reverse engineering
  • root-cause analysis
  • search space
  • searching
  • social intelligence
  • S-R connections (bonds)
  • stamped in
  • stamped out
  • stepping stone
  • stimuli
  • stuck in the mud
  • sub-goals
  • Tangen’s 6 Steps
  • theory of identical elements
  • Thorndike, E.L.
  • Tower of Hanoi task
  • train of thought
  • trained mind
  • transfer of training
  • transferable skills
  • trial-and-error
  • vocabulary
  • well-defined problems
  • well-structured problems

 

 

Quiz

1. Thorndike founded:

  • a. connectionism
  • b. associationism
  • c. generalization
  • d. sensitization

 

2. Thorndike’s research with cats and dogs involved:

  • classical conditioning
  • working memory
  • cognitive maps
  • puzzle boxes

3. For Thorndike, correct responses are:

  • a. stamped out
  • b. stamped in
  • c. lateralized
  • d. fractured

4. The CAVD test of intelligence includes:

  • a. completion
  • b. analogy
  • c. vividness
  • d. distance

5. Who proposed that the mind is like a muscle:

  • a. Stevenson
  • b. Thorndike
  • c. Watson
  • d. Locke

 

 

Answers

1. Thorndike founded:

  • a. connectionism
  • b. associationism
  • c. generalization
  • d. sensitization

2. Thorndike’s research with cats and dogs involved:

  • a. classical conditioning
  • b. working memory
  • c. cognitive maps
  • d. puzzle boxes

3. For Thorndike, correct responses are:

  • a. stamped out
  • b. stamped in
  • c. lateralized
  • d. fractured

4. The CAVD test of intelligence includes:

  • a. completion
  • b. analogy
  • c. vividness
  • d. distance

5. Who proposed that the mind is like a muscle:

  • a. Stevenson
  • b. Thorndike
  • c. Watson
  • d. Locke

aaaaaa

 

Some solutions seem obvious, after the fact. Often we struggle with a problem so long that we become frustrated. But sometimes, we find a creative and fun solution to a difficult problem.

Here are 5 things we will cover:

  • Problem Solving
  • Tangen’s Six Steps
  • Puzzle Boxes
  • Three Laws
  • Problem Solution Strategies

[Read more…] about Problem Solving

Filed Under: Cognition

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