Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2025-01-02T21:38:26.797Z Has data issue: false hasContentIssue false

11 - What's in a Sample? A Manual for Building Cognitive Theories

Published online by Cambridge University Press:  02 February 2010

By
Gerd Gigerenzer
Edited by
Klaus Fiedler  and
Peter Juslin
Gerd Gigerenzer
Affiliation:
Max Planck Institute, Germany
Klaus Fiedler
Affiliation:
Ruprecht-Karls-Universität Heidelberg, Germany
Peter Juslin
Affiliation:
Umeå Universitet, Sweden
Get access

Summary

PREVIEW

How do you build a model of mind? I discuss this question from the point of view of sampling. The idea that the mind samples information – from memory or from the environment – became prominent only after researchers began to emphasize sampling methods. This chapter provides a toolbox of potential uses of sampling, each of which can form a building block in a cognitive theory. In it I ask four questions: who samples, why, what, and how.

Who: In the social sciences (in contrast to the natural sciences), not only researchers sample, but so do the minds they study. Why: I distinguish two goals of sampling, hypotheses testing and measurement. What: Researchers can sample participants, objects, and variables to get information about psychological hypotheses, and minds may sample objects and variables to get information about their world. How: I distinguish four ways to sample: (i) no sampling, (ii) convenience sampling, (iii) random sampling from a defined population, and (iv) sequential sampling. These uses of sampling have received unequal attention. The prime source of our thinking about sampling seems to be R. A. Fisher's small-sample statistics, as opposed to the use of random sampling in quality control, the use of sequential sampling, and the use of sampling for measurement and parameter estimation. I use this legacy to identify potentials of sampling in adaptive cognition that have received little attention.

In his Opticks, Isaac Newton (1952/1704) reported experiments with prisms to demonstrate that white light consists of spectral colors.

Type
Chapter
Information
Information Sampling and Adaptive Cognition , pp. 239 - 260
Publisher: Cambridge University Press
Print publication year: 2005

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. Hillsdale, NJ: ErlbaumGoogle Scholar
Brunswik, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62, 193–217CrossRefGoogle Scholar
Busemeyer, J. R., & Rapoport, A. (1988). Psychological models of deferred decision making. Journal of Mathematical Psychology, 32, 91–134CrossRefGoogle Scholar
Chater, N., & Oaksford, M. (2005). Mental mechanisms: Speculations on human causal learning and reasoning. In Fiedler, K. & Juslin, P. (Eds.), Information sampling and adaptive cognition. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 65, 145–153CrossRefGoogle ScholarPubMed
Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684CrossRefGoogle Scholar
Danziger, K. (1990). Constructing the subject: Historical origins of psychological research. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American Psychologist, 34, 571–582CrossRefGoogle Scholar
Einhorn, H. J., & Hogarth, R. M. (1975). Unit weighting schemes for decision making. Organizational Behavior and Human Performance, 13, 171–192CrossRefGoogle Scholar
Erev, I., & Roth, A. E. (2001). Simple reinforcement learning models and reciprocation in the prisoner's dilemma game. In Gigerenzer, G. & Selten, R. (Eds.), Bounded rationality: The adaptive toolbox (pp. 215–231). Cambridge, MA: MIT PressGoogle Scholar
Estes, W. K. (1959). The statistical approach to learning theory. In Koch, S. (Ed.), Psychology: A study of science (Vol. 2, pp. 380–491). New York: McGraw-HillGoogle Scholar
Fisher, R. A. (1935). The design of experiments. Edinburgh: Oliver and BoydGoogle Scholar
Fisher, R. A. (1955). Statistical methods and scientific induction. Journal of the Royal Statistical Society, Series B, 17, 69–78Google Scholar
Frey, B. S., & Eichenberger, R. (1996). Marriage paradoxes. Rationality and Society, 8, 187–206CrossRefGoogle Scholar
Garcia y Robertson, R., & Garcia, J. (1985). X-rays and learned taste aversions: Historical and psychological ramifications. In Burish, T. G., Levy, S. M., & Meyerowitz, B. E. (Eds.), Cancer, nutrition and eating behavior: A biobehavioral perspective (pp. 11–41). Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
Gigerenzer, G. (1981). Messung und Modellbildung in der Psychologie [Measurement and modeling in psychology]. Munich: Ernst Reinhardt (UTB)Google Scholar
Gigerenzer, G. (1984). External validity of laboratory experiments: The frequency-validity relationship. American Journal of Psychology, 97, 185–195CrossRefGoogle Scholar
Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254–267CrossRefGoogle Scholar
Gigerenzer, G. (2000). Adaptive thinking: Rationality in the real world. New York: Oxford University PressGoogle Scholar
Gigerenzer, G., & Fiedler, K. (2004). Minds in environments: The potential of an ecological approach to cognition. Manuscript submitted for publicationGoogle Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650–669CrossRefGoogle ScholarPubMed
Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98, 506–528CrossRefGoogle Scholar
Gigerenzer, G., & Murray, D. J. (1987). Cognition as intuitive statistics. Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
Gigerenzer, G., & Richter, H. R. (1990). Context effects and their interaction with development: Area judgments. Cognitive Development, 5, 235–264CrossRefGoogle Scholar
Gigerenzer, G., & Selten, R. (Eds.) (2001). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT PressGoogle Scholar
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Krüger, L. (1989). The empire of chance. How probability changed science and everyday life. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple heuristics that make us smart. New York: Oxford University PressGoogle Scholar
Griffin, D. & Tversky, A. (1992). The weighing of evidence and the determinants of confidence. Cognitive Psychology, 24, 411–435CrossRefGoogle Scholar
Hammond, K. R. (1966). The psychology of Egon Brunswik. New York: Holt, Rinehart & WinstonGoogle Scholar
Hammond, K. R., & Stewart, T. R. (Eds.) (2001). The essential Brunswik: Beginnings, explications, applications. New York: Oxford University PressGoogle Scholar
Hoffrage, U., & Hertwig, R. (2005). Which world should be represented in representative design? In Fiedler, K. & Juslin, P. (Eds.), Information sampling and adaptive cognition. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Juslin, P., Winman, A., & Olssen, H. (2000). Naive empiricism and dogmatism in confidence research: A critical examination of the hard-easy effect. Psychological Review, 107, 384–396CrossRefGoogle ScholarPubMed
Kelley, H. H. (1967). Attribution theory in social psychology. In Levine, D. (Ed.), Nebraska symposium on motivation (Vol. 15, pp. 192–238). Lincoln: University of Nebraska PressGoogle Scholar
Kelly, G. A. (1955). The Psychology of personal constructs. New York: NortonGoogle Scholar
Kendall, M. G. (1943). The advanced theory of statistics (Vol. 1). New York: LippincottGoogle Scholar
Klayman, J., Soll, J., Juslin, P., & Winman, A. (2005). Subjective confidence and the sampling of knowledge. In Fiedler, K. & Juslin, P. (Eds.), Information sampling and adaptive cognition. Cambridge, UK: Cambridge University PressCrossRefGoogle Scholar
Luce, R. D. (1977). Thurstone's discriminal processes fifty years later. Psychometrika, 42, 461–489CrossRefGoogle Scholar
Luce, R. D. (1988). The tools-to-theory hypothesis. Review of G. Gigerenzer and D. J. Murray, “Cognition as intuitive statistics”. Contemporary Psychology, 32, 151–178Google Scholar
Luce, R. D., & Green, D. M. (1972). A neural timing theory for response times and the psychophysics of intensity. Psychological Review, 79, 14–57CrossRefGoogle Scholar
Martignon, L., & Laskey, K. B. (1999). Bayesian benchmarks for fast and frugal heuristics. In Gigerenzer, G., Todd, P. M., & the ABC Research Group, Simple heuristics that make us smart (pp. 169–188). New York: Oxford University PressGoogle Scholar
Michotte, A. (1963). The perception of causality. London: Methuen. (Original work published 1946.)Google Scholar
Newton, I. (1952). Opticks: Or a treatise of the reflections, refractions, inflections and colours of light. New York: Dover. (Original work published 1704.)Google Scholar
Pearson, E. S. (1939). “Student” as statistician. Biometrika, 30, 210–250Google Scholar
Rieskamp, J., & Hoffrage, U. (1999). When do people use simple heuristics and how can we tell? In Gigerenzer, G., Todd, P. M., & the ABC Research Group, Simple heuristics that make us smart (pp. 141–167). New York: Oxford University PressGoogle Scholar
Robinson, W. (1950). Ecological correlation and the behavior of individuals. American Sociological Review, 15, 351–357CrossRefGoogle Scholar
Sedlmeier, P., & Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies?Psychological Bulletin, 105, 309–316CrossRefGoogle Scholar
Selten, R. (2001). What is bounded rationality? In Gigerenzer, G. & Selten, R. (Eds.), Bounded Rationality: The adaptive toolbox (pp. 13–36). Cambridge, MA: MIT PressGoogle Scholar
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99–118CrossRefGoogle Scholar
Simon, H. A. (1956). Rational choice and the structure of environments. Psychological Review, 63, 129–138CrossRefGoogle Scholar
Stevens, S. S. (Ed.) (1951). Handbook of experimental psychology. New York: WileyGoogle Scholar
Stigler, S. M. (1999). Statistics on the table: The history of statistical concepts and methods. Cambridge, MA: Harvard University PressGoogle Scholar
Swijtink, Z. G. (1987). The objectification of observation: Measurement and statistical methods in the nineteenth century. In Krüger, L., Daston, L., & Heidelberger, M. (Eds.), The probabalistic revolution, Vol. I: Ideas in history (pp. 261–285). Cambridge, MA: MIT PressGoogle Scholar
Tanner, W. P. Jr., & Swets, J. A. (1954). A decision-making theory of visual detection. Psychological Review, 61, 401–409CrossRefGoogle ScholarPubMed
Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34, 273–286CrossRefGoogle Scholar
Todd, P. M., & Miller, G. F. (1999). From pride and prejudice to persuasion: Satisficing in mate search. In Gigerenzer, G., Todd, P. M., & the ABC Research Group, Simple heuristics that make us smart (pp. 287–308). New York: Oxford University PressGoogle Scholar
Mises, R. (1957). Probability, statistics, and truth. London: Allen and Unwin. (Original work published 1928.)Google Scholar
Wald, A. (1950). Statistical decision functions. New York: WileyGoogle Scholar
Wells, G. L., & Windschitl, P. D. (1999). Stimulus sampling and social psychological experimentation. Personality and Social Psychology Bulletin, 25, 1115–1125CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×