Sage Journals: Discover world-class research

Abstract

Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.

Keywords

optimal experimental design Bayesian decision theory probability gain hypothesis testing computer simulation

Get full access to this article

View all access options for this article.

References

Baron

(1985). Rationality and intelligence. Cambridge, England: Cambridge University Press.

Baron

Beattie

Hershey

J.C.

(1988). Heuristics and biases in diagnostic reasoning: II. Congruence, information, and certainty. Organizational Behavior and Human Decision Processes, 42, 88–110.

Butko

N.J.

Movellan

J.R.

(2008). I-POMDP: An infomax model of eye movement. Retrieved April 15, 2010, from IEEE Xplore: http://dx.doi.org/doi:10.1109/DEVLRN.2008.4640819

Cavagnaro

D.R.

Myung

J.I.

Pitt

M.A.

Kujala

(2010). Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22, 887–905.

Denzler

Brown

C.M.

(2002). Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 145–157.

Fedorov

V.V.

(1972). Theory of optimal experiments. New York: Academic Press.

Gigerenzer

Hoffrage

(1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102, 684–704.

Ginzburg

Sejnowski

T.J.

(1996). Dynamics of rule induction by making queries: Transition between strategies. In Cottrell

G.W.

(Ed.), Proceedings of the 18th annual conference of the Cognitive Science Society (pp. 121–125). Mahwah, NJ: Erlbaum.

Good

I.J.

(1950). Probability and the weighing of evidence. New York: Griffin.

10.

Hertwig

Barron

Weber

E.U.

Erev

(2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534–539.

11.

Klayman

(1995). Varieties of confirmation bias. Psychology of Learning and Motivation, 42, 385–418.

12.

Klayman

Y.-W.

(1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94, 211–228.

13.

Knowlton

B.J.

Squire

L.R.

Gluck

M.A.

(1994). Probabilistic classification learning in amnesia. Learning and Memory, 1, 106–120.

14.

Krauss

Martignon

Hoffrage

(1999). Simplifying Bayesian inference: The general case. In Magnani

Nersessian

Thagard

(Eds.), Model-based reasoning in scientific discovery (pp. 165–179). New York: Kluwer Academic/Plenum.

15.

Kruschke

J.K.

Johansen

M.K.

(1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 1083–1119.

16.

Kullback

Liebler

R.A.

(1951). Information and sufficiency. Annals of Mathematical Statistics, 22, 79–86.

17.

Lindley

D.V.

(1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27, 986–1005.

18.

Matsuka

Corter

J.E.

(2008). Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology, 61, 1067–1097.

19.

McKenzie

C.R.M.

(2004). Hypothesis testing and evaluation. In Koehler

D.J.

Harvey

(Eds.), Blackwell handbook of judgment and decision making (pp. 200–219). Oxford, England: Blackwell.

20.

McKenzie

C.R.M.

(2006). Increased sensitivity to differentially diagnostic answers using familiar materials: Implications for confirmation bias. Memory & Cognition, 34, 577–588.

21.

Nakamura

(2006). Neural representation of information measure in the primate premotor cortex. Journal of Neurophysiology, 96, 478–485.

22.

Nelson

J.D.

(2005). Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain. Psychological Review, 112, 979–999.

23.

Nelson

J.D.

(2008). Towards a rational theory of human information acquisition. In Oaksford

Chater

(Eds.), The probabilistic mind: Prospects for rational models of cognition (pp. 143–163). Oxford, England: Oxford University Press.

24.

Nelson

J.D.

(2009). Naïve optimality: Subjects’ heuristics can be better-motivated than experimenters’ optimal models. Behavioral and Brain Sciences, 32, 94–95.

25.

Nelson

J.D.

Cottrell

G.W.

(2007). A probabilistic model of eye movements in concept formation. Neurocomputing, 70, 2256–2272.

26.

Nelson

J.D.

Tenenbaum

J.B.

Movellan

J.R.

(2001). Active inference in concept learning. In Moore

J.D.

Stenning

(Eds.), Proceedings of the 23rd conference of the Cognitive Science Society (pp. 692–697). Mahwah, NJ: Erlbaum.

27.

Nickerson

R.S.

(1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2, 175–220.

28.

Oaksford

Chater

(1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608–631.

29.

Oaksford

Chater

(1996). Rational explanation of the selection task. Psychological Review, 103, 381–391.

30.

Peterson

C.R.

Beach

L.R.

(1967). Man as an intuitive statistician. Psychological Bulletin, 68, 29–46.

31.

Rehder

Hoffman

A.B.

(2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1–41.

32.

Savage

L.J.

(1954). The foundations of statistics. New York: Wiley.

33.

Sedlmeier

Gigerenzer

(2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General, 130, 380–400.

34.

Skov

R.B.

Sherman

S.J.

(1986). Information-gathering processes: Diagnosticity, hypothesis-confirmatory strategies, and perceived hypothesis confirmation. Journal of Experimental Social Psychology, 22, 93–121.

35.

Slowiaczek

L.M.

Klayman

Sherman

S.J.

Skov

R.B.

(1992). Information selection and use in hypothesis testing: What is a good question, and what is a good answer? Memory & Cognition, 20, 392–405.

36.

Trope

Bassok

(1982). Confirmatory and diagnosing strategies in social information gathering. Journal of Personality and Social Psychology, 43, 22–34.

37.

Wason

P.C.

(1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12, 129–140.

38.

Wason

P.C.

(1966). Reasoning. In Foss

B.M.

(Ed.), New horizons in psychology (pp. 135–151). Harmondsworth, England: Penguin.

39.

Wason

P.C.

Johnson-Laird

P.N.

(1972). Psychology of reasoning: Structure and content. Cambridge, MA: Harvard University Press.

40.

Wells

G.L.

Lindsay

R.C.L.

(1980). On estimating the diagnosticity of eyewitness nonidentifications. Psychological Bulletin, 88, 776–784.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.15 MB

Experience Matters

Abstract

Keywords

Get full access to this article

References

Supplementary Material