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Record W2074644642 · doi:10.1037/h0085797

Informal reasoning: Theory and method.

2004· article· en· W2074644642 on OpenAlexaff
Jonathan St. B. T. Evans, Valerie A. Thompson

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyCognitive psychologyCognitive science

Abstract

fetched live from OpenAlex

The rationale for the present volume is simple: The great majority the everyday reasoning, including that expert groups engaged their professions, is informal. By contrast, most the studies human inference reported by psychologists the literature are formal reasoning. This discrepancy provides considerable cause for concern and not only because cognitive psychology should have some practical application. Excessive focus on formal reasoning tasks has also, our view, inhibited the development good theories human reasoning. What Is Informal Reasoning, and Why Do We Need to Study It? Psychological studies formal reasoning have fallen largely into two domains: deductive reasoning and statistical inference. These two endeavours have much common and some researchers work both areas. In both cases, participants are presented with what problem-solving researchers call well-defined problems. A well-defined problem can be solved by use the information provided and no other; fact, the correct solution to these problems often requires the reasoner to use only the information provided the premises, and to avoid adding background information and knowledge to the problem domain. Instead, a correct solution is achieved by applying a normatively appropriate rule inference. Normative systems are often applied to formal reasoning problems order to define solutions as right or wrong, such that these problems are then construed as tests correct and fallacious reasoning. Hence, these problems are designed to measure the extent to which participants bring to the laboratory an understanding and ability to apply - the relative normative principles. In the case deductive reasoning research, the relevant normative system is formal logic. Participants are given some premises and asked whether a conclusion follows. Under strict deductive reasoning instructions, they are told (a) to assume that the premises are true and (b) to draw or approve only conclusions that necessarily follow. As observed elsewhere (Evans, 2002), this widely used method was developed over 40 years ago when belief logic as a normative and descriptive system for human reasoning was veiy much higher than it is today. In spite the method, much evidence has emerged to support the conclusion that pragmatic factors play a large part human reasoning. We say in spite of because standard deductive instructions aim to suppress precisely those factors that dominate informal reasoning: the introduction prior belief and the expression uncertainty premises and conclusions. In research on statistical inference, a similar story is found. People are asked to make statistical inference on the basis well-defined problems, which relevant probabilities or frequency distributions are provided, and their answers are assessed for correctness against the norms provided by the probability calculus. Research this tradition has been mostly conducted by researchers the heuristics and biases tradition inspired by the work Danny Kahneman and Amos Tversky (Gilovich, Griffin, & Kahneman, 2002; Kahneman, Slovic, & Tversky, 1982). This results an arguably negative research strategy that is similar to much work on deductive reasoning. That is, researchers show primarily what people cannot do (conform to the principles logic or probability theory) and only secondarily address what people actually do. Indeed, one the most common explanations for why intelligent, educated individuals often fail to reason normatively is that they use informal reasoning processes to solve formal reasoning tasks. For example, notwithstanding instructions to the contraiy, reasoners often supplement the information they are provided with background knowledge and beliefs, and make inferences that are consistent with, rather than necessitated by, the premises. If this is the case, it is reasonable to suggest that we study these processes directly, by giving our participants tasks that allow them to express these types behaviours freely, rather than indirectly, via the observation poor performance on a formal task. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.101
GPT teacher head0.425
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2004
Admission routes1
Has abstractyes

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