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A comprehensive overview of problem-solving, reasoning, and decision-making processes. It explores various problem-solving strategies, including routine and non-routine approaches, well-defined and ill-defined problems, and metacognitive techniques. The document delves into different types of reasoning, such as deductive, inductive, and abductive reasoning, and examines the role of heuristics and algorithms in decision-making. It also discusses common fallacies and biases that can influence our reasoning and decision-making processes.
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problem - current state does not match goal state problem solving - transforming current state into goal state representation - conceptualizing of the problem, most critical step, includes facts and info, requirements to meet goal state, methods to achieve goal state routine problem solving - based on prior knowledge or experience nonroutine problem solving - new situation; procedures attempted may or may not work well-defined problems - clear goals, all needed information, obvious when you reached the goal - may be new ill-defined problems - Goals, information, or knowledge of a situation may be unclear metacognition - self-monitoring of your progress
epistemic monitoring - legitimate representation, appropriate understanding and methods, holistic approach closed domains - applying information from one domain to another insight - solutions not based on trial and error or a sequential progression creativity - novel solutions, previously unexperienced, seeing components of a problem differently functional fixedness - seeing components as having only one function dopaminergic theory of positive affect - neurotransmitter dopamine released during pleasurable activity confirmation bias - find information consistent with hypothesis disconfirmation bias or counterfactual stragtedy - tendency to seek out evidence inconsistent with a hypothesis we don't believe, and neglect information consistent with it
framing - how the problem is presented can influence the responses analogical reasoning - problem solving by analogy - comparing current problem to previous problems and solutions reason - drawing conclusions from premises premises - facts, information as understood validity - assessing whether conclusions follow from premises, even if some info is incorrect linear reasoning - reasoning based on ordering objects seriation - lists or hierarchies based on a dimension Transitivity - inferences based on other relationships - if A>B and B>C , than C>D
linear syllogism - a logical argument in which the conclusion follows necessarily from the premises unmarked terms - generic, close to basic concept marked terms - contain additional meaning principle of congruity - refers to step 2 below, matching how a question is encoded to stored information deductive reasoning - general to specific inductive reasoning - specific to general Abductive Reasoning - induction leading to the most likely conclusion, but may not be correct - medical diagnosis, scientific method Conversion - erroneous assumption that statements are reversible
subjective expected utility - The probability (expectation) of satisfaction (utility) resulting from choosing a specific alternative in a decision. Unconscious processing - the tendency to make conclusions and decisions without thinking about them Heuristics - mental short cuts to decision making algorithms - explore or compute every possibility representativeness heuristic - to what degree the current situation resembles our prototype availability heuristic - easy to retrieve information forms the basis of the decision anchoring and adjustment heuristic - early information influences later decisions somatic marker hypothesis - emotion circuitry in prefrontal cortex contributes to decision making
fallacy - mistake in logic or reasoning sunk costs - repeating an investment after initial loss gambers fallacy - past evens affect future outcomes hot outcome fallacy - after a series of wins, wins will continue Conjunction fallacy - two separate independent events are seen as independent Law of Large Numbers - the larger the number of individuals that are randomly drawn from a population, the more representative the resulting group will be of the entire population law of small numbers - small samples can appear to deviate from chance, leading to mistaken conclusions false clusters - groups of random outcomes seen as non-random risk averse - when framed as gains, take the safe option, the guaranteed gain
stage 2 - Metacognition emerges in which stage of development of reasoning? faster, less - Overall, heuristics are _______ and _______ accurate than algorithms. unconscious processing - Which concept from decision making is most similar to the concept of incubation that we discussed during creative problem solving?