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Reasoning - Coggle Diagram
Reasoning
Dual process theory
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DPT is used to explain reasoning, judgement and decision making.
Many cognitive biases and other behavioural phenomena can be explained as system 1 processes, system 2 processes or the interaction of the systems.
System 1: implicit, automatic, low effort, rapid, independent of intelligence etc.
System 2: explicit, controlled, high effort, limited by intelligence etc.
System 1: fast intuitive responses which are learnt through experience such as heuristics, emotions and expert intuition.
System 2: slow, analytic responses that are calculated from the info presented. Expected utility model, weighing up likelihoods and benefits, using mental models to find logically valid solutions.
Evidence: Heuristic responses are fast but analytic responses are slow. fast response deadlines prevent analytic thinking and only allow heuristic responses.
WM load: heuristic thinking requires little WM resource & analytical thinking requires a lot. Secondary WM load prevents analytic thinking.
Cognitive reflection test (Frederick, 2005): bat & a ball cost £1.10 in total. Bat costs £1.00 more than the ball. How much is the ball? Initial response: 10p, inhibit intuitive response and do the maths = 5p. Cognitive misers use S1 - prone to bias.
Critique: do all properties form a dichotomy or are some continuum? e.g automatic/controlled, a controlled process can slowly become auto through practice (Schneider & Shiffrin, 1977).
Are 2 systems necessary to explain the findings? (Kruglanski & Gigerenzer, 2011). E.g. speeded response findings could be 1 process with different criteria (logic and belief), belief is quicker to apply.
Normative system: logic
Calculating which conclusions follow a firm set of facts and the connections between them. Typically focuses on necessary inferences - must be true, facts and conclusions typically have 2 truth values: true or false. Many problems are uncertain - answer lies between the 2 extremes. Not 100% certain.
Diagnostic reasoning: Give the symptoms - a certain diagnosis is likely but not necessarily true (Flores et al, 2014). In times of uncertainty - use the info available to draw conclusion.
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Relational reasoning: e.g. hippos are beginning than sheep, sheep are bigger than toads. Conclusion hippos are bigger than toads.
Mental model theory
Rarely reason using logical rules or probability equations. We generate possibilities of different situations described by the facts.
Integrate the facts into 1 or more models, if a conclusion is true in every model then it must be a valid conclusion (Johnson-Laird, 1983).
If multiple models are valid, 1 is favoured on grounds of coherence: completeness, consistency, plausibility or we generate new models.
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Profound bullshit
People with low CRT scores are more likely to rate computer-generated pseudo statements as deeply meaningful (Pennycook, Cheyne, Barr et al, 2015).
More likely to believe in conspiracy theories (Swami, Voraceck et al, 2014). Paranormal (Pennycook, Cheyne, Seli et al, 2012).
Fake news (Pennycook & Rand, 2018) and less likely to believe in evolution (Gervais, 2015).
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Conditional reasoning
'If-Then' evaluations, so if A then B. Conclusion may not necessarily be true - logically valid inference. Fact 1: If Pippa is a dog, then Pippa is an animal. Fact 2: Pippa is a dog. We can conclude that Pippa is an animal. Easy problem, 96.8% of people get this right (Schroyens et al, 2001)