PSY270H5F Cognition: Reasoning & Problem Solving

Cognition: The Machinery of the Mind

• Course: PSY270H5F
• Instructor: Dr. Benjamin Wolfe
• Class schedule: Wednesdays, 9am - 12pm, DV 2072
• Email: [email protected]

Lecture Outline

• One Minute Survey Questions
• Lecture 11: Reasoning and Problem Solving
• Deductive and Inductive Reasoning
• What is a problem?
• Problem-solving strategies
• Expertise and its limits
• Constraints and self-constraints

Reasoning

• Asking "what's most probable?"
• How do we figure this out?
  • Evidence
  • Experience
  • Logical Thinking

Types of Reasoning

• Inductive
  • What's likely?
  • Probabilistic
• Deductive
  • What must be true?
  • Logical rules

Drawing Conclusions (Syllogisms)

• Two premises and a conclusion
• If the two premises are true, the conclusion must also be true (within the syllogism's bounds)
• If either premise is false, the entire syllogism breaks down.

What if we know 'p' is true?

• Modus ponens: If 'p' is true, then 'q' must also be true.

What if we don't observe 'p'?

• Modus tollens: If 'p' is false, then 'q' is not necessarily true (must not be true).

Can the consequent alone tell us anything?

• Affirmation of the Consequent is not a valid form of deductive reasoning.

What if the antecedent does not happen? (not-p)

• Knowing that the antecedent ('p') did not happen does not tell us anything definitive about the consequent ('q').

The Wason Card Selection Task

• Rule based on cards and their side properties (vowel/even number)
• Determining which card to flip to validate or disprove the rule.
• Reveals limitations of human reasoning; bias toward confirming rules instead of disproving them.

Hacking the Problem (Permission Schema)

• If A is satisfied, then B may be carried out.
• Examples of using permission schemas in real-world scenarios (airport immigration)
• Just changing instructions can easily alter decision making.

Inductive Reasoning

• Drawing conclusions about what's most probable given prior evidence and experience
  • Building on known properties or features.
  • Assuming universality; how representative is a smaller example of a wider set?

Confirmation Bias

• Favoring information and situations that confirm existing beliefs or knowledge and not exploring other possibilities.

Dealing with Uncertainty(Bayes' Rule)

• Bayes' Rule: posterior probability is the product of prior probability and likelihood.

Prior Probabilities

• Taking existing knowledge into consideration during problem solving (e.g., how common is that thing already).

Range of Chonkitude

• The frequency distribution of a feature.

Thinking Differently (Reframing)

• Reframing or changing the way you first view problems to generate new, creative solutions.

Kaplan and Simon's Results

• Framing a problem in more useful formats can positively impact outcome/solution.

Structural Features vs Surface Features

• Focusing on the underlying mechanisms of a problem, as opposed to the superficial appearance.

Thinking Laterally

• Reframing problems in innovative ways to generate creative solutions

What's the real problem?

• Problem of identification vs description
• Experiential knowledge vs. formal knowledge

Expertise

• High performance, but often underestimate themselves.

Problem Solving Strategies

• Means-end analysis: identifying subgoals and pursuing them to achieve ultimate goals.

This quiz covers Lecture 11 of PSY270H5F, focusing on reasoning and problem-solving strategies. Topics include deductive and inductive reasoning, the nature of problems, techniques for drawing conclusions, and the limitations of expertise. Test your understanding of how we arrive at logical conclusions and the mechanics behind reasoning.