ECON 1 Lecture Notes - Lecture 17: Taylor & Francis, Motivated Reasoning, Psychological Science
Problem 4 Complex cognition 18/09/2020
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The sleep of reason produces monsters
Learning goals
1. How do we reason? real life applications: reasoning in our everyday lives
2. What are the different strategies for reasoning?
3. What influences our reasoning skills?
4. Types/Theories of reasoning (deductive/inductive reasoning)?
5. How is reasoning flawed? What typical mistakes to people make when reasoning? Why do we
believe in fake news?
Literatures
• Baumard, N., & Boyer, P. (2013). Religious beliefs as reflective elaborations on intuitions: A modified
dual-process model. Current Directions in Psychological Science, 22(4), 295-300.
• Bago, B., Rand, D. G., & Pennycook, G. (2020). Fake news, fast and slow: Deliberation reduces belief in
false (but not true) news headlines. Journal of experimental psychology: general.
• Baron, J. (2020). Religion, cognitive style, and rational thinking. Current Opinion in Behavioral Sciences,
34, 64-68.
• Dorison, C. A., Minson, J. A., & Rogers, T. (2019). Selective exposure partly relies on faulty affective
forecasts. Cognition, 188, 98-107.
• Eysenck, M. W., & Keane, M. T. (2013). Cognitive Psychology: A Student's Handbook. 7th Edition.
London: Taylor & Francis. Chapter 14, pp.589-631.
• Gervais, W. M., & Norenzayan, A. (2012). Analytic thinking promotes religious disbelief. Science,
336(6080), 493-496.
• Mercier, H. (2016). The argumentative theory: Predictions and empirical evidence. Trends in Cognitive
Sciences, 20(9), 689- 700.
• Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). Everyday consequences of analytic thinking.
Current Directions in Psychological Science, 24(6), 425-432.
• Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better
explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50.
• Raoelison, M. T., Thompson, V. A., & De Neys, W. (2020). The smart intuitor: Cognitive capacity
predicts intuitive rather than deliberate thinking. Cognition, 204, 104381.
• Watson‐Jones, R. E., Busch, J. T., & Legare, C. H. (2015). Interdisciplinary and Cross‐Cultural
Perspectives on Explanatory Coexistence. Topics in Cognitive Science, 7(4), 611-623
Problem 4 Complex cognition 18/09/2020
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Cognitive Psychology: A Student's Handbook. 7th Edition. London: Taylor & Francis. Chapter 14
Inductive reasoning
Deductive reasoning
→ Forming generalizations that may be probable
but not certain from examples or sample
phenomena.
o It involves making generalized conclusion
from premises referring to a particular
instance
→ Conclusions are probably, but not necessarily,
true – generalization provides no certainty that
premise will be true at a later time
→ Scientist often make use of it
→ Analogical reasoning – individual tries to solve
current problem by retrieving information about
a similar problem that was solved in the past
→ Hypothesis testing – there is a difference
between confirmation (attempt to obtain
evidence that will confirm correctness of
hypothesis) and falsification (attempt to falsify
hypotheses by experimental tests)
→ Reasoning to a conclusion from a set of premises
or statements where that conclusion follows
necessarily from the assumptions that the
premise are true
o It involves drawing conclusion that are
definitely valid provided other statements
are assumed to be true
→ Related to problem-solving – deductive
reasoning has a definitive goal, but solution is
not obvious
→ Based on formal logic – however, most people
do not use traditional logic to solve problems
→ Conditional reasoning – based on conditional
propositions that have the form "if p, then q"
→ Syllogistic reasoning – involves arriving at a
conclusion based on two or more propositions
that are assumed to be true
Hypothesis testing
→ Karl Popper (1968) argued that there is a important distinction between CONFIRMATION and FALSIFICATION.
o Confirmation involves the attempt to obtain evidence confirming the correctness of one's
hypothesis.
o Falsification involves the attempt to falsify hypotheses by experimental tests.
According to him, we cannot achieve confirmation via hypothesis testing.
Wason’s 2-4-6 task
→ Peter Wason (1960) devised a hypothesis-testing task that has attracted much interest
o Participants were told three numbers, 2-4-6, conformed to a simple relational rule. Their task
was to generate sets of three numbers and provide reasons for each choice. After each
choice, the experimenter indicated whether the set of numbers conformed to the
experimenter's rule.
o Rule: “Three numbers in ascending order of magnitude”
▪ Only 21% were correct with their first attempt, and 28% never discovered the rule.
→ Most people show CONFIRMATION BIAS:
o In hypothesis testing, seeking evidence that supports one's beliefs.
→ Wason's analysis was limited because he did not take account of people expectations. We can see the
limitation by drawing a distinction between confirmation bias and positive testing. Positive testing
means generating number that are an instance of your hypothesis. This is confirmatory only if you
believe that your hypothesis is correct. If you believe your hypothesis is wrong, then generating
number not conforming to your hypothesis can be regarded as confirming behavior
Limitation
→ The task differs from real life hypothesis testing
o They receive immediate accurate feedback but not told why the number they produced
attracted a yes or a no response. In the real world the feedback is much more informative
→ It is very general in that it applies to a fairly high proportion of sets of three number
o Most rules or hypothesis apply to only a smallish proportion or possible object or event.
o Positive testing works poorly on the 2-4-6 tasks but no with most other forms of hypothesis
testing.
→ Most people show confirmation bias
o There is less evidence of confirmation bias if the hypothesis being tests is someone else’s
Problem 4 Complex cognition 18/09/2020
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Hypothesis testing: simulated and real research environments
→ Popper (1968) argued that a crucial feature of all truly scientific theory is falsifiability.
o He also argued that scientists should focus on falsification rather than confirmation because
the latter is impossible to achieve fully.
o This view is partially correct but somewhat simplistic
→ There is a popular view that "scientific discovery is the result of genius, inspiration, and sudden insight"
o Scientific typically use “weak methods” which are very general and can be applied to almost
any scientific problem.
o Several methods are also use in everyday problem solving
→ Weak methods:
1. Kulkarni and Simon (1988) found scientists make extensive use of the UNUSUALNESS HEURISTIC or
rule of thumb.
▪ This involve focusing on unusual or unexpected findings and then using them to
guide future theorizing and research.
▪ A rule of thumb used by scientists in which unexpected findings are used to develop
new hypotheses and lines of research.
2. Other heuristics included the following: challenge conventional wisdom; adopt a step-by-step
approach; carry out many experiments on a trial-and-error basis.
3. Trickett et al. (2009) also found evidence of "what if' reasoning:
▪ It involved conceptual simulations that allowed the scientists to work out the
detailed implications of some hypotheses
Limitations
→ The commitment motivating real scientists to defend their own theories and disprove those of other
scientists is lacking
→ Real scientists typically work in teams, whereas participants in simulated research environment
sometimes work on their own
→ Real research is typically influenced by major motivational and social factors largely absent from
simulated research environment
Deductive reasoning
Conditional reasoning
→ A form of deductive reasoning based on “if … then” propositions
→ In this logical system, symbols stand for sentences and logical operators are applied to them to reach
conclusion.
→ Building a sentence
o P stands for proposition, and is the antecedent (E.G. IT IS RAINING)
o Q is the consequent (E.G. NANCY GETS WET);
o A logical operator “if…then” is used to link P and Q: if P, then Q
→ Experiment with counterexamples:
o Number of counterexamples had a major impact on participants' willingness to accept valid
inferences which is contrary to traditional logic
o Reasoning performance of participants high in working memory capacity was better than
those low in working memory capacity
→ Strategies that people can use with invalid
o Statistical strategy: which is contrary to the dictates of traditional logic. It is basically intuitive
and involves estimating the probability that the conclusion is true given what the individuals
knows about the world.
o Counterexample strategy: which is more cognitively demanding. This strategy involves trying
to think of a counterexample contradicting the conclusion and accepting the conclusion if no
counterexamples come to mind.
▪ It is easy to find counterexamples with respect to the conclusion of problem but
more difficult with problem.
The counterexample strategy was used on 49% or trials with unlimited time but on only
1.7% of trials with limited time. In contrast, the less demanding statistical strategy was used
on 55% of limited-time trials but only 19% of unlimited-time trials
