Thought-Reasoning PDF

Thought- Reasoning Experimental Psychology Course Prof. Federica Biassoni A.Y. 2024/2025 In a proposition, a sequence of thoughts is organized. An argument is a proposition made up of reasons for the claim (or premises) and a final claim (or conclusion). The claim may report...

Thought- Reasoning Experimental Psychology Course Prof. Federica Biassoni A.Y. 2024/2025 In a proposition, a sequence of thoughts is organized. An argument is a proposition made up of reasons for the claim (or premises) and a final claim (or conclusion). The claim may report about certainty or probability of a fact. Deductive reasoning a. No addictive things are inexpensive b. Some cigarettes are inexpensive c. Therefore, some addictive things are cigarettes. Deductive reasoning a. No addictive things are inexpensive b. Some cigarettes are inexpensive c. Therefore, some cigarettes are not addictive. Syllogism All humans are mortal. Aristotle is human. Aristotle is mortal. Unicorns don’t fly. I don’t fly. Therefore I am a unicorn. Deductive reasoning Logical rules Deductive validity: for logicians, it is impossible for the conclusion of the argument to be false if its premises are true What about humans? Deductive reasoning Best evidence that people use logical rules is that the number of rules the argument requires is a good predictor of the argument’s difficulty: the more rules are needed… The more likely is that people will make a mistake The longer they will take to make a correct decision. If it’s raining, I’ll take my umbrella If it’s raining, I’ll take my umbrella It’s raining If I take an umbrella, I’ll lose it Therefore, I’ll take my umbrella It’s raining Therefore, I’ll lose my umbrella Deductive reasoning: the effects of content The “belief bias”: contrary to the rules of deductive logic, the great majority of subjects will judge a logically invalid conclusion as valid if it seems plausible to them. Effects of content Our ability to evaluate a deductive argument often depends on the content of propositions as well as on logical rules The content of a problem affects the rules we use; sometimes we use rules that are less abstract and more relevant in everyday problems, called pragmatic rules (see for instance the permission rule) We can problem-solve by creating a mental model, that is a concrete representation of the situation Wason Selection Task If a card has a vowel on one side, it has an even number on the other side. Which cards would you turn over to determine whether the claim is correct? Pragmatic rules: think about the problem in terms of permission. If a person is drinking a beer, he/she must be over 18. à The content of a problem affects whether a pragmatic rule is activated. Deductive reasoning: the effects of content Applying pragmatic rules and creating mental models have in common SENSITIVITY TO THE CONTENT: They are determined by the content of the problem, in contrast to the application of logical rules. Inductive Reasoning a. Sam qualified in accounting at college. b. Sam now works for an accounting company. c. Therefore, Sam is an accountant. Deductive vs Inductive Reasoning An argument can be good even if not deductively valid à it is inductively strong: it is improbable that the conclusion is false if the premises are true. Deductive reasoning Inductive reasoning Involves using general premises to Involves starting from specific form a specific conclusion. premises and forming a general conclusion. Inductive reasoning uses premises to generalize a conclusion à the premises endorse but not warrant the conclusion, that is an hypothesis! Inductive strength is a matter of probabilities, not certainties (and - according to logicians - it should be based on the theory of probability). Inductive Reasoning: Do we really rely on the rules of probability theory? Linda’s problem (Kahneman & Tversky, 1982) “Linda is 31 years old, single, outspoken, and very bright. In college she majored in philosophy, and was deeply concerned with issues of discrimination.” Estimate the probabilities of the following statements. 1. Linda is a bank clerk. 2. Linda is a bank clerk and she is active in the feminist movement. Inductive Reasoning: Do we really rely on the rules of probability theory? Two relevant probability rules: 1. base-rate rule à the probability of something being a member of a class is greater the more class members there are 2. conjunction rule à the probability of a proposition cannot be less than the probability of that proposition combined with another proposition Inductive Reasoning Heuristic is a short-cut procedure that is relatively easy to apply and can often lead to the right answer, but not inevitably. People use heuristics in everyday life, though they are not always dependable. Tversky & Kahneman (1973, 1983, 1996) have shown that people violate some basic rules of the probability theory when making inductive judgments. Daniel Kahneman, Nobel Prize in Economics in 2002 for his research on Behavioural Economics, having integrated insights from psychological research into economic science, especially concerning human judgment and decision making under uncertainty conditions. Linda’s problem (Kahneman & Tversky, 1982) “Linda is 31 years old, single, outspoken, and very bright. In college she majored in philosophy, and was deeply concerned with issues of discrimination.” Estimate the probabilities of the following statements. 1. Linda is a bank clerk. 2. Linda is a bank clerk and she is active in the feminist movement. Similarity heuristic à people make judgments based on the similarity between current situations and their prototypes of those situations. Inductive Reasoning Estimate the probabilities of the following statements. 1. To die in a plane crash. 2. To die in a car accident. Inductive Reasoning Availabilty heuristic à people have the tendency to use information that comes to their mind quickly and easily when making decisions, so they base judgments and decisions on the availability of information in memory. Inductive Reasoning Estimate the probabilities of the following statements. 1. Sometime during the year 2024 there will be a massive flood in California in which more that 1000 people will drown. 2. Sometime during the year 2024 there will be an earthquake in California, causing a massive flood in which more that 1000 people will drown. Causality heuristic à people estimate the probability of a situation by the strength of the casual connections between the events in the situation. Inductive Reasoning Estimate the probabilities of the following statements. 1. A man wearing a suit and tie and carrying a briefcase is a lawyer. 2. A man wearing jeans and T-shirt and carrying a backpack is a lawyer. Representativeness heuristic à people have the tendency to estimate the probability of an event based on how similar it is to a known situation. In other words, we compare it to a situation, prototype, or stereotype we already have in mind. Reasoning and decision making Choose between these two snacks: 80% lean 20% fat Reasoning and decision making: Effects and biases Framing effects à the same information, problem, or options can be structured and presented in different ways and the way they are structured have a strong influence on the decision making process. Reasoning and decision making: Effects and biases Belief bias à the tendency to abandon logical rules in favour of our own personal beliefs Reasoning and decision making: Effects and biases Confirmation bias à we give more credence to evidence that is in line with our previous beliefs than to evidence that contradicts it The neural basis for reasoning Research supports distinction between deductive and inductive reasoning à Different parts of the brain were activated when people evaluated deductive validity compared to inductive strength. Reasoning at a glance Reasoning at a glance Deductive Reasoning: reasoning from the top down (from general principles to a conclusion about a specific case) Begin with set of premises: if premises are true, then conclusion cannot be false Content sensitivity and pragmatic rules. Inductive Reasoning: reasoning from the bottom up, starting with specific facts and trying to develop a general principle Involves “likelihood”, not “certainty” In probability estimation, heuristics and biases exert a great influence In both reasoning à violation of logical rules.