NOTES – The Psychology of Planning:

Example: Mendeleev’s Periodic System

• Used as an analogy to illustrate problem decomposition, constraints, and search processes.

• Problem-solving strategies include laying cards to maximize progress and using heuristics (mental shortcuts) to guide decisions.

Problem Decomposition

• Sub-goal specification: Breaking down complex problems into smaller, manageable tasks.

• Orders of Decomposition:

  • Breadth-first: Makes minimal commitments to each part of the problem.

  • Depth-first: Provides immediate feedback but requires more cognitive effort.

  • Opportunistic: Capitalizes on the current state to make decisions.

The Problem Space (Newell & Simon, 1972)

• The problem space represents all possible paths between the initial and goal states.

• The larger the problem space, the harder it is to find the solution due to the vast number of possible states.

• Bounded Rationality: Humans cannot process all information, so they use satisficing (choosing an option that is good enough) to make decisions.

Task Environment

• Problem Representation: The way a problem is presented to a solver influences how they approach it (Zhang & Norman, 1994).

  • Format: The visual or organizational layout of the problem.

  • Thematic Content: Familiarity with the problem topic or scenario.

  • Conditions: External factors, like risk or urgency, that affect decision-making.

Information Processing System

• Working Memory: Limited capacity to hold information and plan.

  • Example: In chess, players must consider approximately 35 possible moves per turn.

• Long-Term Memory: Stores knowledge of operators, solutions, and constraints, which aids problem-solving.

• Expertise: The more expertise an individual has, the better their ability to search and apply appropriate heuristics.

Search Using Heuristics

• Means-Ends Analysis: Breaking down problems by setting sub-goals and applying operators to reduce the distance between the initial and goal states (e.g., fixing a car tire step by step).

• Other Heuristics:

  • Hill-Climbing: Always move towards the goal with each step.

  • Trial and Error: Testing various solutions without a clear plan.

  • Sampling Heuristics: Using strategies like anchoring and representativeness to guide decisions.

Consequences of Not Planning

• Study on Problem Solving (Ormerod et al., 2013): Demonstrated the impact of acting without proper planning. Solutions often suffer from maximization and minimization effects, leading to suboptimal results.

• N-Ball Problem: Demonstrates the cost of improper planning by weighing balls in a problem-solving scenario, showing how first weighs can influence solution rates.

Why Planning Matters (Example: Essay Writing)

• Time on Task: Proper planning ensures time is well-distributed across tasks.

• Macro and Micro-structure: Planning both the overall structure and the finer details of tasks (e.g., writing an essay) is crucial for success.

Key Concepts

• Bounded Rationality: Humans are limited by the information they can process at any one time.

• Problem-Space Search: Heuristic-based searches reduce cognitive load and help find solutions.

• Sub-goals and Decomposition: Breaking problems into manageable parts is crucial for solving complex tasks.

NOTES – The Psychology of Planning:

Overview of Proof and Inference

1.    Definition of Proof: Proof is the process of establishing a fact or truth through argument or inference. This involves tasks like explanation, diagnosis, prediction, and imagination.

2.    Types of Inference:

o   Deduction: Drawing specific conclusions from general premises.

o   Induction: Forming general conclusions from specific instances.

o   Abduction: Inferring the best explanation for an observed phenomenon.

Cognitive Processes in Deduction

1.    Structure/Form: Deductive reasoning relies on the logical structure to validate conclusions.

2.    Semantics: Meaning influences reasoning by making conclusions appear plausible or implausible.

3.    Statistics: The frequency of events affects our perception of probable outcomes.

Inference as Logical Reasoning

1.    Classical Syllogisms: These are deductive arguments with two premises leading to a conclusion.

o   Example: "All orange mammals are Orangutans. Trump is an orange mammal. Therefore…?"

2.    Conditional Inferences: Reasoning based on "if-then" statements, often seen in hypothetical situations.

3.    Transitive Inferences: Using comparisons to infer relationships indirectly.

Conditional Syllogisms

• Modus Ponens: If A, then B. A is true; therefore, B is true.

• Modus Tollens: If A, then B. B is false; therefore, A is false.

• Affirming the Consequent and Denying the Antecedent are logical fallacies that often lead to incorrect conclusions.

Structural View and the Selection Task

1.    Formal Logic: This approach argues that syntactic structure is essential in determining an argument’s validity, e.g., Piaget's stages of cognitive development.

2.    The Selection Task (Wason & Evans, 1975): Participants test logical rules through a card selection game, demonstrating common errors in human logic.

3.    Pragmatic Reasoning Schemas (Cheng & Holyoak, 1985): These schemas (permission and obligation) help us apply rules about how the world works practically, as shown in tasks like checking immigration rules.

Mental Models Theory (Johnson-Laird, 1983)

1.    Mental Models: Reasoners draw conclusions by considering all possible outcomes that do not contradict their premises.

2.    Constraints:

o   Principle of Truth: Only truth-based models are used in reasoning.

o   Working Memory Capacity: Limits how many models can be maintained simultaneously.

o   Procedural Semantics: Understanding based on experience, not strict logic.

Information Gain and Bayesian Reasoning

1.    Bayesian Probability: Helps assess the likelihood of a hypothesis based on prior probabilities.

2.    Information Gain (Oaksford & Chater, 1994; 2007): Reasoning is driven by maximizing information gain, reducing uncertainty by assessing the rarity of events.

3.    Selection Task Variation: Adjusting task structure (e.g., frequency of certain cards) to study the impact of information gain.

Dual-System Theory

1.    System 1: Fast, intuitive, heuristic-driven, often error-prone (e.g., firefighters’ quick decisions).

2.    System 2: Slow, logical, analytic, less prone to heuristics but may overlook simpler solutions.

3.    Interplay Between Systems: Different types of reasoning (logic, mental models, statistics) engage System 1 or System 2 depending on context.

Conclusion

The theory concludes that the reasoning process is multifaceted:

• Logic (System 2): Focuses on form and syntactic structure.

• Mental Models (System 1 → System 2): Driven by semantics and plausible possibilities.

• Statistics (System 1): Based on the frequency and utility of information.

NOTES – The Psychology of Insight:

The Nature of Insight

• Example Problem: The 9-dot problem showcases insight.

•  

• Phenomena:

  • Fixation: Functional fixedness limits one's ability to see new solutions.

  • Impasse: Reaching a mental block in problem-solving.

  • ‘Aha’ Moment: Sudden realization that leads to the solution.

  • Gestalt Accounts: Suggests that perception of a ‘whole’ can restrict potential moves.

Theories on the Importance of Insight

• Key Concepts:

  • Consciousness: Questions whether we control our thinking.

  • Determinism: Explores productive (creative) vs. reproductive (routine) thought.

  • Modularity: Debates if insight represents a unique mental process.

Three Theories of Insight

1.    Representational Change Theory (Knoblich et al., 1999):

o   Insight is difficult due to knowledge constraints.

o   Experiment: Problems like “IV = V + II” are easier than “III = III + III,” showing that mental representations affect ease of problem-solving.

2.    Criterion of Satisfactory Progress (MacGregor, Ormerod & Chronicle, 2001):

o   Difficulty stems from action constraints, not just knowledge.

o   Matchstick Algebra: Shifts mental approach in puzzles by adjusting representations and limiting moves.

3.    Knowledge and Strategy Integration:

o   Example: The eight-coin problem, where a visual or verbal hint can improve solution rates.

o   Results show that specific hints increase insight by enabling new conceptual groupings.

Enhancing Insight through Analogy

• Analogy: Applying known solutions from similar situations to new problems.

  • Example: Fortress Problem to Radiation Problem transfer, with successful solution rates significantly higher when hints are provided (92% with hints vs. 20% without).

Enhancing Insight through Incubation

• Incubation: A break from the problem, shown to help in problem-solving across different types of thinking:

  • Divergent Thinking: Broad incubation benefits.

  • Linguistic Insight: Incubation aids under low cognitive load.

  • Visual Insight: Requires extensive preparation for incubation to assist effectively.

Enhancing Insight through Sleep

• Sleep’s Role: Facilitates analogical transfer in problem-solving.

  • Study by Monaghan et al.: Demonstrates sleep’s positive effect on analogical reasoning and problem-solving.

  • Application: Suggests completing exercises before sleep to leverage sleep benefits.

Key Takeaways

• Valuing sleep can enhance problem-solving ability.

• Suggested routine: Solve an initial problem before sleep and revisit or solve a similar problem after waking.

NOTES – The Psychology of Choice:

Lecture Overview

• Focus: Examines types of choice, decision-making models (normative and descriptive), and choice phenomena.

• Models Discussed:

  • Normative Models: Focus on "rational" decision-making aimed at optimizing outcomes (e.g., expected value and expected utility).

  • Descriptive Models: Include theories like prospect theory that explain real-life deviations from rational decision-making.

Types of Choice

• Choice Situations:

  • Often initiated by instructions or prompts.

  • Can involve uncertainty reduction, hypothesis testing, predictions, or selecting among alternatives.

  • Range between rational and irrational decisions.

Normative/Prescriptive Models

• Rational Decision-Making: Defined as choosing the optimal outcome.

  • Expected Value (EV): Measures the highest resource value by multiplying objective value and probability.

  • Expected Utility (EU): Focuses on the highest psychological value, determined by subjective utility and probability.

• Example of EU Calculation: The EU of a choice depends on probability and utility, where an act (A) gives an outcome (o) with probability PA(o)PA​(o) and utility U(o)U(o).

Violations of Expected Utility Theory

• Certainty and Framing Effects: People often make different choices based on how options are presented (Kahneman & Tversky, 1981).

Prospect Theory (Descriptive Model)

• Developed by Kahneman & Tversky (1979): Accounts for irrational decision-making patterns.

  • Editing Phase: Simplifies options by using heuristics (e.g., availability, anchoring).

  • Evaluation Phase: Calculates anticipated outcomes by weighting probabilities.

• Phenomena Explained by Prospect Theory:

• Loss Aversion: People prefer certain gains over risky ones but become risk-seeking to avoid certain losses.

• Probability Weighting: Overestimating unlikely events and underestimating likely ones (e.g., fear of rare events like shark attacks).

System 1 and System 2 in Decision-Making

• Dual Processes: Decision-making can involve quick, intuitive judgments (System 1) or slow, deliberate reasoning (System 2).

Hypothesis Testing & Pseudodiagnosticity

• Bayesian Influence: Rational choice theory applies Bayesian principles but often falters in real-world decisions.

• Pseudodiagnosticity: Tendency to prioritize confirming a hypothesis rather than testing alternatives.

Preference Reversals

• Lichtenstein and Slovic (1971): People often reverse preferences when the framing changes (e.g., choosing a low-risk bet but assigning higher value to a high-risk one when selling).

• Testing Prospect Theory: Ball et al. (2012) observed preference reversals attributed to shifts between System 1 and System 2 but noted that other factors (like transparency or treatment type) didn’t significantly impact results.

Key Biases and Effects

• Anchoring: Initial information (e.g., credit card minimum payments) heavily influences subsequent judgments and decisions.

• Disjunction Effect and Delay Discounting: Investigated in studies by Tversky & Shafir (1992) and Matta et al. (2012), these biases relate to decision-making under uncertainty and time preferences.