Final Flashcards
Event boundaries in memory and cognition
Physical boundaries (doorways) and scenarios (before/after an exam) define separate events in episodic memory. We forget items immediately after crossing an event boundary.
Doorway effect
When we forget what we were thinking about upon entering a different room - explained by event boundaries.
Prediction: Gambler’s fallacy
The false belief that a predicted outcome of an independent event depends on past outcomes - we assume outcomes are linked when they are random. Thinking one is due for a ‘win’ have a run of ‘losses’/
Gambler’s fallacy in the real world
People continue to invest after several losses on the stock market. U.S. judges in refugee asylum cases are more likely to deny (grant) asylum after granting (denying) asylum to the previous applicant. Loan officers are more likely to deny a loan application after approving the previous application.
The hot-hand belief
Thinking that a person who experiences success will keep having success - ‘a winning streak’. Eg. 91% of basketball fans thought that a player is more likely to make a shot after making 2 shots than after missing a shot. Just because something feels true, doesn’t mean it is true.
Heuristics and biases
Heuristic processing is central for making intuitive and rapid judgments - predictive purpose of cognition. Over-application can lead to serious errors on our judgments and reasoning - stereotyping, gambling addictions.
Post-mortem technique
Learning from your failures. Helps to minimize the over-reliance on heuristics.
Pre-mortem technique
To anticipate and prevent our mistakes before they result in catastrophe. You are on the verge of making a decision, look ahead at challenges that could cause failure, create plan to navigate those challenges.
Inductive reasoning
Concrete form of reasoning. Making general conclusions from specific observations. The conclusions can be false, this is is a “probably but not definitely true” - type of reasoning. When we are unaware of inductive reasoning, it can become a heuristic. Basis of much of human learning from experience. Eg., language learning.
Deductive reasoning
Abstract form of reasoning. Using general theories to reason about specific observations.
Deductive vs inductive reasoning
Inductive reasoning: concrete
Specific observations –> Generalizations –> Theories
Deductive reasoning: abstract
Experiments <– Predictions <– Theories
Different development and brain networks for deductive vs inductive reasoning
Induction: develops age 7 to 11. Deduction: develops in teenage years. Different brain recruitment: notable in frontal cortex, different types of information organization. Different cognitive skills linked to two systems of reasoning: System 1: automatically, and with little effort (inductive). System 2: slower and requires more effort (deductive).
Logic and Syllogisms
Deductive reasoning: formal systems for generating statements that will be true if rules of the system are followed. Syllogisms: Premises are presumed to be true. Determine if the premise statements support the conclusion based on the logical structure not content: Major premise (general), Minor premise (specific), Conclusion (test).
Validity of syllogisms
Validity: Is the conclusion true given the premises’ logical form? Valid = logical rules. Truth = world knowledge / content. A valid structure (All A are B: All B are C: Therefore, all A are C).
Types of syllogisms
All statements: All A are B
Negative Statements: No A is a B. Also means no B is A. Some statements: Some A are B - at least one, possibly all.
Atmosphere effect
People rate a conclusion as valid when the qualifying word (e.g, ‘all’, ‘some’) in the premise match those in the conclusion.
Mental model theory
People construct mental stimulations of the world based on statements (e.g., syllogisms) to judge logic and validity. A negative statement.
Omission bias
People tend to have more trouble reasoning with negative information. Biased thought that “withholding is not as bad as doing” - inaction is harder to classify as wrong than action.
The trolley problem
A trolley is coming down a track and there are five people on the track. If you. do not intervene, the trolley will hit and kill five people. There is a lever close to you that you can switch the direction of the trolley onto another set of tracks that one man is on. 3 scenarios:
a) Do nothing and kill 5 people
b) Switch train to another track and kill one man.
c) Stop the trolley and save five people by pushing a large man to his death in front of trolley.
Which decision is more “immoral”. Although C is a utilitarian response, many do not choose C due to adverse emotion.
Ventromedial prefrontal lesions
Less emotional response leads to more utilitarian response.
High-functioning autism
Differences in emotion processing leads to more utilitarian response.
Positive emotion induction
Healthy individuals with heightened positive mood (shown positive film prior to problem) more likely to say they would push the man (utilitarian response) than control group in prior example.
The belief bias seen in syllogisms
People have problems reasoning with syllogisms in which logical validity conflicts with truth. The content of a syllogism can lead to errors due to a belief bias. The tendency to think a syllogism is valid if the conclusions are believable.
Conditional Reasoning
“If P then Q” statements where P is the antecedent and Q is the consequence. How to test if the condition statement “if it is raining, I will get wet” is valid?
Wason’s task: Conditional reasoning
If a card has a vowel on one side, then it has an even number on the other side, which card should you flip? Conditional statement: If ‘vowel’ then ‘even’. Many do not test this statement correctly. Confirmation bias: tendency to seek confirmatory evidence for a hypothesis.
Conditional reasoning and falsification
The falsification principle: You need to look for situations that would falsify a rule.
Familiarity effects
If a person is drinking a beer (P), then the person is over 21 years old (Q). Cards have have on one side and beverage on the other side. Which card(s) do you need to flip to verify this statement?
Familiarity affects judgements
It even affects our judgment of time. The return trip effect: Time judged returning on a route (now familiar) is rated as shorter than initial route.
Problem Solving - 3 aspects
Going from a problem to goal state. Problem solving is a multi-step cognitive process that involves three aspects:
1. Recognizing and representing problem - focus on relevant information.
2. Analyzing and solving it - problem solving cycle.
3. Assessing the solution’s effectiveness - and store it in an appropriate form.
The problem solving cycle
- Define the problem.
- Brainstorm Solutions.
- Pick a solution
- Implement the solution
- Review the results
Recursive: Repeat this cycle as many times as necessary to find solution.
Applicable: Apply successful cycles (solution to new problems - must be able to generalize)
Ensure a solution’s effectiveness - Generalization
Generalization is important for adaptive behaviour. For problem solving, it involves storing specific solutions without detail to apply. to new scenarios. Memory for solutions should include ‘essence’ and not specific details for generalization to occur.
Well defined problems
Requirements are unambiguous. All information needed to solve the problem is present. Goal directness: defined goal state, task constraints (clear steps), single/expected outcome. Applying algorithms, puzzles.
Ill defined problems
How to overcome problem / the goal is ambiguous. Requires added information. Situational. Have few limitations (rules) for how to solve the problem. Multiple solutions or expected outcomes. Social problem solving. Laptop is broken.
Ill-defined problems carry a load
No constraints. Greater activity in the right lateral prefrontal cortex for ill-defined anagrams. Solving ill-defined problems carries a greater ‘cognitive load’. Cognitive load: the amount of information held in mind at one time.
Moravec’s paradox
Artificial intelligence (AI) can solve well-defined problems well, but not ill-defined problems and simple skills. “Everything that’s easy is hard, and everything that’s hard is easy”. AI defined by the use of algorithms, deep neural networks, that work well with certainty but not with uncertainty.
