1. Reasoning Lindsay Anderson

2. The Papers “The probabilistic approach to human reasoning”- Oaksford, M., & Chater, N. “Two kinds of Reasoning” – Rips, L. J. “Deductive Reasoning” – Johnson-Laird, P. N.

3. What is reasoning? A systematic process of thought that yields a conclusion from percepts, thoughts, or assertions Reminder: Deduction: general -> specific Induction: specific -> general

4. “The probabilistic approach to human reasoning” Oaksford & Chater PARADOX People have successful reasoning in everyday life, but they perform poorly on laboratory reasoning tasks WHY ?!?!?

5. First: Other Approaches to Reasoning Mental logic & Mental Model approaches: - argue that systematic deviations from logic represent unavoidable performance errors - working memory limitations restrict reasoning ability According to both: people rational in principle but err in practice ______________________________________________ To resolve conflict, Other theorists propose that there are 2 types of rationality: Everyday rationality- does not depend on formal system like logic Formal rationality- is error prone Still, how is everyday success explained?

6. Problem with Standard Logic Allow “strengthening of antecedent” -“if something is a bird it flys” -If tweety is a bird, then can infer that tweety flies -Strengthening antecedent means that when given further info, like “tweety is an ostrich” you still infer that “tweey flies” -Do this in standard logic because ostrich still a bird -This new info about ostrich should defeat the previous conclusion that tweety flies Probabilistic handles this problem by using conditional probability: -If tweety a bird, then probability of flying is high -If tweety an ostrich, probability of flying is 0

7. Probabilistic approach’s Solution… Errors on lab tasks because importing everyday, uncertain, reasoning strategies into laboratory This seemingly “irrational behavior” is a result of the behavior being compared to an inappropriate logical standard When compare behavior to probability theory instead of logic, reasoning seen more positively

8. Probabilistic Models applied in 3 main areas of human reasoning research: Conditional Inference Wason’s selection task Syllogistic Reasoning Applying probability approach to these areas explains ppl’s lab performance as rational attempt to make sense of the lab tasks by using strategies adapted for coping with everyday uncertainty

9. “Two kinds of reasoning” Rips View 1: People can evaluate arguments in at least 2 qualitatively different ways: - In terms of deductive correctness - In terms of inductive strength View 2: Single Psychological continuum; argument strength and correctness are functions of arguments position on this continuum - Deductively correct- max value on continuum - Strong argument- high value on continuum

10. Unitary View of Reasoning Implies only assess argument in terms of strength But, maybe other ways people assess arguments (e.g., plausibility)?

11. Testing Unitary View If the Unitary View correct, then argument evaluation one dimensional If Unitary does not hold true, then must accept that there are other ways people assess goodness of arguments

12. What they did (the experiment)  Participants evaluated arguments in terms of correctness and strength  Deduction Condition: valid/not valid, then rated condifence  Induction Condition: strong/not strong, then rated degree of strength  Varied, wording of instructions to check whether results depended on wording (no effect)

13. Results For unitary to be correct, increases in deductive correctness should mimic increases in inductive strength (b/c reflecting differences on same underlying one-dimensional scale) As can see, this is not happening

14. Conclusion People not using probability as the SOLE basis for both judgments Reasoning is not one-dimensional

15. “Deductive Reasoning” Johnson-Laird 3 Principle Approaches to Deductive Performance: 1. Deduction as process based on Factual Knowledge * 2. Deduction as formal, syntactic process * 3. Deduction as semantic process based on mental models Deduction controversial: may rely on 1 of the above, or some combination

16. Deduction as process based on factual knowledge: Reasoning has nothing to do with logic Instead, reasoning based on memories of previous inferences Come to conclusions based on our current factual knowledge base Problem: This theory does not explain why we can reason about the unknown

17. Deduction as formal, syntactic process: Deduction relies on formal rules of inference Rip’s Theory (& others)- proposes reasoners extract logical forms of premises and use rules to derive conclusions - Rules for sentential connectives like “if” and “or” and for quantifiers like “all” and “some” - Based on natural deduction, so have rules for introducing and eliminating sentential connectives

18. With rules, complications arise: Ex: introducing “And” A B Therefore A and B Therefore A and (A and B) Therefore A and [A and (A and B)] As you can see, this gets very messy

19. Deduction as semantic process based on mental models: Mental models are not based on arrangement of words (syntax), rather they are based on meaning Each mental model represents a possibility - its structure and content capture what is common about all the ways the possibility can occur

20. Example “there are a circle and a triangle” Model captures whats common in any situation where circle and triangle exist Given that premise is true, a conclusion is possible if in at least 1 mental model If in all mental models, conclusion necessary

21. The Phenomena of Deductive Reasoning Reasoning with sentential connectives Conditional reasoning Reasoning about Relations Syllogisms and reasoning with quantifiers The effects of content on deduction The Selection Task Systematic Fallacies in Reasoning (in the context of these phenomena, author discusses evidence for/against 3 main theories so you can arrive at your own conclusion)