Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time

Judgment and Decision Making

Volumn 6, Issue 1, 2011, Pages 23-42

  Glöckner, Andreas ^a   Bröder, Arndt ^b  

^a MAX PLANCK INSTITUTE FOR RESEARCH ON COLLECTIVE GOODS   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Confidence; Decision time; Parallel constraint satisfaction; Probabilistic inferences; Recognition; Strategy classification

Indexed keywords

EID: 79951588971     PISSN: None     EISSN: 19302975     Source Type: Journal    
DOI: None     Document Type: Article

References (60)

1. Batchelder, W. H., & Riefer, D. M. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review, 6, 57-86.
2. Betsch, T., & Glöckner, A. (2010). Intuition in judgment and decision making: Extensive thinking without effort. Psychological Inquiry, 21, 279-294.
3. Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without tradeoffs. Psychological Review, 113, 409-432.
4. Bröder, A. (2010). Outcome-based strategy classification. In A. Glöckner & C. L. M. Witteman (Eds.), Foundations for tracing intuition: Challenges and methods (pp. 61-82). London: Psychology Press & Routledge.
5. Bröder, A., & Eichler, A. (2006). The use of recognition information and additional cues in inferences from memory. Acta Psychologica, 121, 275-284.
6. Bröder, A., & Gaissmaier, W. (2007). Sequential processing of cues in memory-based multiattribute decisions. Psychonomic Bulletin & Review, 14, 895-900.
7. Bröder, A., & Schiffer, S. (2003a). Bayesian strategy assessment in multi-attribute decision making. Journal of Behavioral Decision Making, 16, 193-213.
8. Bröder, A., & Schiffer, S. (2003b). Take The Best versus simultaneous feature matching: Probabilistic inferences from memory and effects of reprensentation format. Journal of Experimental Psychology: General, 132, 277-293.
9. Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 133-154). Malden, MA: Blackwell Publishing.
10. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432-459.
11. DeKay, M. L., Patino-Echeverri, D., & Fischbeck, P. S. (2009). Distortion of probability and outcome information in risky decisions. Organizational Behavior and Human Decision Processes, 109, 79-92.
12. Dougherty, M. R. P., Franco-Watkins, A. M., & Thomas, R. (2008). Psychological plausibility of the theory of probabilistic mental models and the fast and frugal heuristics. Psychological Review, 115, 199-211.
13. Erdfelder, E., Auer, T.-S., Hilbig, B. E., Aßfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift für Psychologie - Journal of Psychology, 217, 108-124.
14. Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53, 134-140.
15. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669.
16. Gigerenzer, G., & Goldstein, D. G. (1999). Betting on one good reason: The take the best heuristic Simple heuristics that make us smart (pp. 75-95). New York, NY: Oxford University Press.
17. Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98, 506-528.
18. Glöckner, A. (2001). The maximizing consistency heuristic: Parallel processing in human decision making. Unpublished Diploma Thesis, University of Heidelberg, Germany.
19. Glöckner, A. (2006). Automatische Prozesse bei Entscheidungen [Automatic processes in decision making]. Hamburg, Germany: Kovac.
20. Glöckner, A. (2008). Does intuition beat fast and frugal heuristics? A systematic empirical analysis. In H. Plessner, C. Betsch & T. Betsch (Eds.), Intuition in judgment and decision making (pp. 309-325). Mahwah, NJ: Lawrence Erlbaum.
21. Glöckner, A. (2009). Investigating intuitive and deliberate processes statistically: The Multiple-Measure Maximum Likelihood strategy classification method. Judgment and Decision Making, 4, 186-199.
22. Glöckner, A. (2010). Multiple measure strategy classification: Outcomes, decision times and confidence ratings. In A. Glöckner & C. L. M. Witteman (Eds.), Foundations for tracing intuition: Challenges and methods. (pp. 83-105). London: Psychology Press & Routledge.
23. Glöckner, A., & Betsch, T. (2008a). Do people make decisions under risk based on ignorance? An empirical test of the Priority Heuristic against Cumulative Prospect Theory. Organizational Behavior and Human Decision Processes, 107, 75-95.
24. Glöckner, A., & Betsch, T. (2008b). Modeling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberate decision making. Judgment and Decision Making, 3, 215-228.
25. Glöckner, A., & Betsch, T. (2008c). Multiple-reason decision making based on automatic processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 1055-1075.
26. Glöckner, A., Betsch, T., & Schindler, N. (2010). Coherence shifts in probabilistic inference tasks. Journal of Behavioral Decision Making, 23, 439-462.
27. Glöckner, A., & Herbold, A.-K. (2011). An eye-tracking study on information processing in risky decisions: Evidence for compensatory strategies based on auto matic processes. Journal of Behavioral Decision Making, 24, 71-98.
28. Glöckner, A., & Hilbig, B. E. (2010). What is adaptive about adaptive decision making? Testing single- versus multiple-strategy models in probabilistic inference tasks. Manuscript submitted for publication.
29. Glöckner, A., & Hodges, S. D. (in press). Parallel constraint satisfaction in memory-based decisions. Experimental Psychology.
30. Goldstein, D. G., & Gigerenzer, G. (1999). The recognition heuristic: How ignorance makes us smart Simple heuristics that make us smart (pp. 37-58). New York, NY: Oxford University Press.
31. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90.
32. Hilbig, B. E. (2008). Individual differences in fast-andfrugal decision making: Neuroticism and the recognition heuristic. Journal of Research in Personality, 42, 1641-1645.
33. Hilbig, B. E. (2010). Reconsidering "evidence" for fast and frugal heuristics. Psychonomic Bulletin & Review, 17, 923-930.
34. Hilbig, B. E., Erdfelder, E., & Pohl, R. F. (2010). One-reason decision-making unveiled: A measurement model of the recognition heuristic. Journal of Experimental Psychology: Learning, Memory, & Cognition, 36, 123-134.
35. Hilbig, B. E., & Pohl, R. F. (2008). Recognizing users of the recognition heuristic. Experimental Psychology, 55, 394-401.
36. Hilbig, B. E., & Pohl, R. F. (2009). Ignorance- versus evidence-based decision making: A decision time analysis of the recognition heuristic. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 1296-1305.
37. Hochman, G., Ayal, S., & Glöckner, A. (2010). Physiological arousal in processing recognition information: Ignoring or integrating cognitive cues? Judgment and Decision Making, 5, 285-299.
38. Hogarth, R. M. (2001). Educating intuition. Chicago, IL: University of Chicago Press.
39. Holyoak, K. J., & Simon, D. (1999). Bidirectional reasoning in decision making by constraint satisfaction. Journal of Experimental Psychology: General, 128, 3-31.
40. Jekel, M., Nicklisch, A., & Glöckner, A. (2010). Implementation of the Multiple-Measure Maximum Likelihood strategy classification method in R: Addendum to Glöckner (2009) and practical guide for application. Judgment and Decision Making, 5, 54-63.
41. Marewski, J. N., Gaissmaier, W., Schooler, L., Goldstein, D. G. & Gigerenzer, G. (2010). From recognition to decisions: Extending and testing recognition-based models for multi-alternative inference. Psychonomic Bulletin & Review, 17, 287-309.
42. Martignon, L., & Hoffrage, U. (1999). Why does onereason decision making work? A case study in ecological rationality Simple heuristics that make us smart (pp. 119-140). New York, NY: Oxford University Press.
43. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88, 375-407.
44. Monroe, B. M., & Read, S. J. (2008). A general connectionist model of attitude structure and change: The ACS (Attitudes as Constraint Satisfaction) model. Psychological Review, 115, 733-759.
45. Montgomery, H. (1989). From cognition to action: The search for dominance in decision making. In H. Montgomery & O. Svenson (Eds.), Process and structure in human decision making (pp. 23-49). Oxford, England: John Wiley & Sons.
46. Newell, B. R., & Fernandez, D. (2006). On the binary quality of recognition and the inconsequentially of further knowledge: Two critical tests of the recognition heuristic. Journal of Behavioral Decision Making, 19, 333-346.
47. Newell, B. R., & Shanks, D. R. (2004). On the role of recognition in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 923-935.
48. Oppenheimer, D. M. (2003). Not so fast! (and not so frugal!): Rethinking the recognition heuristic. Cognition, 90, B1-B9.
49. Pachur, T., Bröder, A., & Marewski, J. N. (2008). The recognition heuristic in memory-based inference: Is recognition a non-compensatory cue? Journal of Behavioral Decision Making, 21, 183-210.
50. Pachur, T., & Hertwig, R. (2006). On the psychology of the recognition heuristic: Retrieval primacy as a key determinant of its use. Journal of Experimental Psychology-Learning Memory and Cognition, 32, 983-1002.
51. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 534-552.
52. Pohl, R. F. (2006). Empirical tests of the recognition heuristic. Journal of Behavioral Decision Making, 19, 251-271.
53. Read, S. J., Vanman, E. J., & Miller, L. C. (1997). Connectionism, parallel constraint satisfaction processes, and Gestalt principles: (Re)introducing cognitive dynamics to social psychology. Personality and Social Psychology Review, 1, 26-53.
54. Richter, T., & Späth, P. (2006). Recognition is used as one cue among others in judgment and decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 150-162.
55. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207-236.
56. Rogers,W. H. (1993). Regression standard errors in clustered samples. Stata Technical Bulletin, 13, 19-23.
57. Russo, J. E., Carlson, K. A., Meloy, M. G., & Yong, K. (2008). The goal of consistency as a cause of information distortion. Journal of Experimental Psychology: General, 137, 456-470.
58. Simon, D., Krawczyk, D. C., Bleicher, A., & Holyoak, K. J. (2008). The transience of onstructed preferences. Journal of Behavioral Decision Making, 21, 1-14.
59. Svenson, O. (1992). Differentiation and Consolidation Theory of human decision making: A frame of reference for the study of pre- and post-decision processes. Acta Psychologica, 80, 143-168.
60. Thagard, P., & Millgram, E. (1995). Inference to the best plan: A coherence theory of decision. In A. Ram & D. B. Leake (Eds.), Goal-driven learning (pp. 439-454). Cambridge, MA: MIT Press.