Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T14:38:39.103Z Has data issue: false hasContentIssue false
  • English
  • Français

The “is-ought fallacy” fallacy

Published online by Cambridge University Press:  14 October 2011

Mike Oaksford  and
Nick Chater
Mike Oaksford
Affiliation:
Department of Psychological Sciences, Birkbeck College, University of London, London, WC1E 7HX, United Kingdom. [email protected]://www.bbk.ac.uk/psyc/staff/academic/moaksford
Nick Chater
Affiliation:
Behavioural Sciences Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/faculty/members/Nick/Chater-
Get access Rights & Permissions [Opens in a new window]

Abstract

Mere facts about how the world is cannot determine how we ought to think or behave. Elqayam & Evans (E&E) argue that this “is-ought fallacy” undercuts the use of rational analysis in explaining how people reason, by ourselves and with others. But this presumed application of the “is-ought” fallacy is itself fallacious. Rational analysis seeks to explain how people do reason, for example in laboratory experiments, not how they ought to reason. Thus, no ought is derived from an is; and rational analysis is unchallenged by E&E's arguments.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 5 , October 2011 , pp. 262 - 263
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Anderson, J. R. (1991) Is human cognition adaptive? Behavioral and Brain Sciences 14:471–517.CrossRefGoogle Scholar
Blake, A., Bulthoff, H. H. & Sheinberg, D. (1996) Shape from texture: Ideal observers and human psychophysics. In: Perception as Bayesian inference, ed. Knill, D. & Richards, W., pp. 287–321. Cambridge University Press.CrossRefGoogle Scholar
Chater, N. & Oaksford, M. (1999) The probability heuristics model of syllogistic reasoning. Cognitive Psychology 38:191–258.CrossRefGoogle ScholarPubMed
Chater, N. & Oaksford, M. (in press) Normative systems: Logic, probability, and rational choice. In: The Oxford handbook of thinking and reasoning, ed. Holyoak, K. & Morrison, R.. Oxford University Press.Google Scholar
Chater, N., Tenenbaum, J. & Yuille, A., eds. (2006) Probabilistic models of cognition. Special Issue: Trends in Cognitive Sciences 10.Google ScholarPubMed
Edgington, D. (1995) On conditionals. Mind 104:235–329.CrossRefGoogle Scholar
Hahn, U. & Oaksford, M. (2007) The rationality of informal argumentation: A Bayesian approach to reasoning fallacies. Psychological Review 114:704–32.CrossRefGoogle ScholarPubMed
Klauer, K. C. (1999) On the normative justification for information gain in Wason's selection task. Psychological Review 106:215–22.CrossRefGoogle Scholar
Krebs, J. R. & Davies, N., eds. (1996) Behavioural ecology: An evolutionary approach, 4th edition. Blackwell.Google Scholar
Kreps, D. M. (1992) A course in microeconomic theory. Harvester Wheatsheaf.Google Scholar
Nelson, J. (2005) Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain. Psychological Review 112:979–99.CrossRefGoogle ScholarPubMed
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101:608–31.CrossRefGoogle Scholar
Oaksford, M. & Chater, N. (1998a) Rationality in an uncertain world. Psychology Press.Google Scholar
Oaksford, M. & Chater, N., eds. (1998b) Rational models of cognition. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2003) Optimal data selection: Revision, review and re-evaluation. Psychonomic Bulletin and Review 10:289–318.CrossRefGoogle Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Oaksford, M., Chater, N. & Larkin, J. (2000) Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory, and Cognition 26:883–99.Google Scholar
Pirolli, P. (2007) Information foraging: A theory of adaptive interaction with information. Oxford University Press.CrossRefGoogle Scholar
Ramsey, F. P. (1931) The foundations of mathematics and other logical essays. Routledge and Kegan Paul.Google Scholar
Sober, E. (1993) Philosophy of biology. Oxford University Press.Google Scholar
Wason, P. C. (1968) Reasoning about a rule. Quarterly Journal of Experimental Psychology 20:273–81.CrossRefGoogle Scholar