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.
What is reasoning?
• A systematic process of thought that yields a conclusion from percepts, thoughts, or assertions
• Reminder:Deduction: general -> specificInduction: specific -> general
“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 ?!?!?
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?
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
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
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
“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
Unitary View of ReasoningImplies only assess argument in terms of strength
But, maybe other ways people assess arguments (e.g., plausibility)?
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
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)
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
Conclusion
• People not using probability as the SOLE basis for both judgments
• Reasoning is not one-dimensional
“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
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
Deduction as formal, syntactic process:
• Deduction relies on formal rules of inferenceRip’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
• With rules, complications arise:
Ex: introducing “And”ABTherefore A and BTherefore A and (A and B)Therefore A and [A and (A and B)]
As you can see, this gets very messy
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
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
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)