Why most dieters fail but some succeed: A goal conflict model of eating behavior

Psychological Review

Volumn 120, Issue 1, 2013, Pages 110-138

  Stroebe, Wolfgang ^a,b   Van Koningsbruggen, Guido M ^c   Papies, Esther K ^a   Aarts, Henk ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

^b UNIVERSITY OF GRONINGEN   (Netherlands)

^c VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

Dieting; Goal conflict model of eating; Restrained eating; Self Regulation

Indexed keywords

ARTICLE; CONFLICT; DIET THERAPY; EATING; FEEDING BEHAVIOR; HUMAN; LOW CALORY DIET; MOTIVATION; OBESITY; PSYCHOLOGICAL ASPECT; PSYCHOLOGICAL MODEL; TREATMENT FAILURE;

CONFLICT (PSYCHOLOGY); DIET, REDUCING; EATING; FEEDING BEHAVIOR; GOALS; HUMANS; MODELS, PSYCHOLOGICAL; OBESITY; OVERWEIGHT; TREATMENT FAILURE;

EID: 84880581487     PISSN: 0033295X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0030849     Document Type: Article

References (167)

1. Aarts, H. (2012). Goals, motivated social cognition and behavior. In S. T. Fiske & C. N. Macrae (Eds.), The SAGE handbook of social cognition (pp. 75-79). London, England: Sage.
2. Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-directed behavior. Journal of Personality and Social Psychology, 78, 53-63. doi:10.1037/0022-3514.78.1.53
3. Aarts, H., & Dijksterhuis, A. (2003). The silence of the library: Environment, situational norm, and social behavior. Journal of Personality and Social Psychology, 84, 18-28. doi:10.1037/0022-3514.84.1.18
4. Aarts, H., & Elliot, A. (2012). Goal-directed behavior. New York, NY: Psychology Press.
5. Aarts, H., Gollwitzer, P. M., & Hassin, R. R. (2004). Goal contagion: Perceiving is for pursuing. Journal of Personality and Social Psychology, 87, 23-37. doi:10.1037/0022-3514.87.1.23
6. Abramson, E. E., & Wunderlich, R. A. (1972). Anxiety, fear and eating: A test of the psychosomatic concept of obesity. Journal of Abnormal Psychology, 79, 317-321. doi:10.1037/h0033200
7. Ach, N. (1905). Ü ber die Willenstätigkeit und das Temperament [On temperament and the activity of the will]. Leipzig, Germany: Quelle & Meyer.
8. Adriaanse, M. A., de Ridder, D. T. D., & de Wit, J. B. F. (2009). Finding the critical cue: Implementation intentions to change one's diet work best when tailored to personally relevant reasons for unhealthy eating. Personality and Social Psychology Bulletin, 35, 60-71. doi:10.1177/0146167208325612
9. Adriaanse, M. A., de Ridder, D. T. D., & Evers, C. (2011). Emotional eating: Eating when emotional or emotional about eating? Psychology & Health, 26, 23-39. doi:10.1080/08870440903207627
10. Andreyeva, T., Long, M. W., Henderson, K. E., & Grode, G. M. (2010). Trying to lose weight: Diet strategies among Americans with overweight or obesity in 1996 and 2003. Journal of the American Dietetic Association, 110, 535-542. doi:10.1016/j.jada.2009.12.029
11. Arnow, B., Kenardy, J., & Agras, W. S. (1995). The Emotional Eating Scale: The development of a measure to assess coping with negative affect by eating. International Journal of Eating Disorders, 18, 79-90. doi:10.1002/1098-108X(199507)18:179::AIDEAT2260180109 3.0.CO;2-V
12. Bargh, J. A., & Ferguson, M. J. (2000). Beyond behaviorism: On the automaticity of higher mental processes. Psychological Bulletin, 126, 925-945. doi:10.1037/0033-2909.126.6.925
13. Bargh, J. A., Gollwitzer, P. M., Lee-Chai, A., Barndollar, K., & Trötschel, R. (2001). The automated will: Nonconscious activation and pursuit of behavioral goals. Journal of Personality and Social Psychology, 81, 1014-1027. doi:10.1037/0022-3514.81.6.1014
14. Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130, 553-573. doi:10.1037-0033-2909.130.4.553
15. Batterham, R. L., Ffytche, D. H., Rosenthal, J. M., Zelaya, F. O., Barker, G. J., & Withers, D. J. (2007, November 1). PYY modulation of cortical and hypothalamic brain areas predicts feeding behaviour in humans. Nature, 450, 106-109. doi:10.1038/nature06212
16. Baucom, D. H., & Aiken, P. A. (1981). Effect of depressed mood on eating among obese and nonobese dieting and nondieting persons. Journal of Personality and Social Psychology, 41, 577-585. doi: 10.1037/0022-3514.41.3.577
17. Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252-1265. doi:10.1037/0022-3514.74.5.1252
18. Baumgartner, H., & Pieters, R. (2008). Goal-directed consumer behavior: Motivation, volition, and affect. In C. P. Haugvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of consumer psychology (pp. 367-392). New York, NY: Erlbaum.
19. Berridge, K. C., Ho, C.-Y., Richard, J. M., & DiFeliceantonio, A. G. (2010). The tempted brain eats: Pleasure and desire circuits in obesity and eating disorders. Brain Research, 1350, 43-64. doi:10.1016/j.brainres.2010.04.003
20. Berthoud, H.-R. (2004). Neural control of appetite: Cross-talk between homeostatic and non-homeostatic systems. Appetite, 43, 315-317. doi: 10.1018/j.appet2004.04.009
21. Berthoud, H.-R. (2011). Metabolic and hedonic drives in the neural control of appetite: Who is the boss? Current Opinion in Neurobiology, 21, 888-896. doi:10.1016/j.conb.2011.09.004
22. Blaison, C., Imhoff, R., Huhnel, I., Hess, U., & Banse, R. (2012). The affect misattribution procedure: Hot or not? Emotion, 12, 403-412. doi:10.1037/a0026907
23. Bleau, R. (1996). Dietary restraint and anxiety in adolescent girls. British Journal of Clinical Psychology, 35, 573-583. doi:10.1111/j.2044-8260.1996.tb01213.x
24. Boon, B., Stroebe, W., Schut, H., & Ijtema, R. (2002). Ironic processes in the eating behaviour of restrained eaters. British Journal of Health Psychology, 7, 1-10. doi:10.1348/135910702169303
25. Brownell, K. D. (2002). The environment and obesity. In C. G. Fairburn & K. D. Brownell (Eds.), Eating disorders and obesity (pp. 433-438). New York, NY: Guilford Press.
26. Carlin, B. P., & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 57, 473-484. doi:10.2307/2346151
27. Carlin, B. P., & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 57, 473-484. doi:10.2307/2346151
28. Dennis, S., Lee, M. D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: The case of the list-length effect. Journal of Memory and Language, 59, 361-376. doi:10.1016/j.jml.2008.06.007
29. Domingos, P. (1999). The role of Occam's razor in knowledge discovery. Data Mining and Knowledge Discovery, 3, 409-425. doi:10.1023/A: 1009868929893
30. Dougherty, M. R., 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. doi: 10.1037/0033-295X.115.1.199
31. Dougherty, M. R., Gettys, C. F., & Ogden, E. E. (1999). MINERVA-DM: A memory processes model for judgments of likelihood. Psychological Review, 106, 180-209. doi:10.1037/0033-295X.106.1.180
32. Einhorn, H. J. (1970). Use of nonlinear, noncompensatory models in decision making. Psychological Bulletin, 73, 221-230. doi:10.1037/h0028695
33. Eisenberg, P., & Becker, C. A. (1982). Semantic context effects in visual word recognition, sentence processing, and reading: Evidence for semantic strategies. Journal of Experimental Psychology: Human Perception and Performance, 8, 739-756. doi:10.1037/0096-1523.8.5.739
34. Erev, I., & Barron, G. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112, 912-931. doi:10.1037/0033-295X.112.4.912
35. Erev, I., & Roth, A. E. (2001). On simple reinforcement learning models and reciprocation in the prisoner dilemma game. In G. Gigerenzer & R. Selten (Eds.), The adaptive toolbox (pp. 215-232). Cambridge, MA: MIT Press.
36. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140. doi:10.1037/0096-3445.127.2.107
37. Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53, 134-140. doi:10.1037/h0045156
38. Fiske, A. P. (1992). The four elementary forms of sociality: Framework for a unified theory of social relations. Psychological Review, 99, 689-723. doi:10.1037/0033-295X.99.4.689
39. Flavell, J. H. (1982). On cognitive development. Child Development, 53, 1-10. doi:10.2307/1129634
40. Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. Boca Raton, FL: Chapman & Hall/CRC.
41. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.
42. Gelman, A., Meng, X. L., & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica, 6, 733-760.
43. Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107-143. doi:10.1111/j.1756-8765.2008.01006.x
44. Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451-482. doi:10.1146/annurevpsych- 120709-145346
45. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103, 650-669. doi:10.1037/0033-295X.103.4.650
46. Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996). Markov chain Monte Carlo in practice. Boca Raton, FL: Chapman & Hall.
47. Glöckner, A., Betsch, T., & Schindler, N. (2010). Coherence shifts in probabilistic inference tasks. Journal of Behavioral Decision Making, 23, 439-462. doi:10.1002/bdm.668
48. Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247. doi:10.1037/0096-3445.117.3.227
49. Grünwald, P. (2000). Model selection based on minimum description length. Journal of Mathematical Psychology, 44, 133-152. doi:10.1006/jmps.1999.1280
50. Grünwald, P. (2007). The minimum description length principle. Cambridge, MA: MIT Press.
51. Grünwald, P. D., Myung, I. J., & Pitt, M. A. (2005). Advances in minimum description length: Theory and applications. Cambridge, MA: MIT Press.
52. Han, C., & Carlin, B. P. (2001). Markov chain Monte Carlo methods for computing Bayes factors: A comparative review. Journal of the American Statistical Association, 96, 1122-1132. doi:10.1198/016214501753208780
53. Heathcote, A., Brown, S., & Mewhort, D. J. K. (2000). The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin & Review, 7, 185-207. doi:10.3758/BF03212979
54. Hilbig, B. E. (2010). Reconsidering "evidence" for fast-and-frugal heuristics. Psychonomic Bulletin & Review, 17, 923-930. doi:10.3758/PBR.17.6.923
55. Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528-551. doi:10.1037/0033-295X.95.4.528
56. Hoijtink, H. (2001). Confirmatory latent class analysis: Model selection using Bayes factors and (pseudo) likelihood ratio statistics. Multivariate Behavioral Research, 36, 563-588. doi:10.1207/S15327906MBR3604_04
57. Jansen, B. R. J., & van der Maas, H. L. J. (1997). Statistical test of the rule assessment methodology by latent class analysis. Developmental Review, 17, 321-357. doi:10.1006/drev.1997.0437
58. Jansen, B. R. J., & van der Maas, H. L. J. (2002). The development of children's rule use on the balance scale task. Journal of Experimental Child Psychology, 81, 383-416. doi:10.1006/jecp.2002.2664
59. Jefferys, W. H., & Berger, J. O. (1992). Ockham's razor and Bayesian analysis. American Scientist, 80, 64-72.
60. Jeffreys, H. (1961). Theory of probability. Oxford, England: Oxford University Press.
61. Juslin, P., Jones, S., Olsson, H., & Winman, A. (2003). Cue abstraction and exemplar memory in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 924-941. doi:10.1037/0278-7393.29.5.924
62. Juslin, P., Olsson, H., & Olsson, A. C. (2003). Exemplar effects in categorization and multiple-cue judgment. Journal of Experimental Psychology: General, 132, 133-156. doi:10.1037/0096-3445.132.1.133
63. Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A "lazy" algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563-607. doi:10.1207/s15516709cog2605_2
64. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York, NY: Cambridge University Press.
65. Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: Knowledge partitioning and function learning. Psychological Review, 111, 1072-1099. doi:10.1037/0033-295X.111.4.1072
66. Karabatsos, G. (2006). Bayesian nonparametric model selection and model testing. Journal of Mathematical Psychology, 50, 123-148. doi:10.1016/j.jmp.2005.07.003
67. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795. doi:10.1080/01621459.1995.10476572
68. Kass, R. E., & Wasserman, L. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the American Statistical Association, 90, 928-934. doi:10.1080/01621459.1995.10476592
69. Kelley, H., & Busemeyer, J. (2008). A comparison of models for learning how to dynamically integrate multiple cues in order to forecast continuous criteria. Journal of Mathematical Psychology, 52, 218-240. doi: 10.1016/j.jmp.2008.01.009
70. Koller, D., Friedman, N., Getoor, L., & Taskar, B. (2007). Graphical models in a nutshell. In L. Getoor & B. Taskar (Eds.), Introduction to statistical relational learning (pp. 13-55). Cambridge, MA: MIT Press.
71. Kontkanen, P., Myllymaki, P., & Tirri, H. (2001). Comparing prequential model selection criteria in supervised learning of mixture models. In T. Jaakkola & T. Richardson (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (pp. 233-238).
72. Kruglanski, A. W., & Gigerenzer, G. (2011). Intuitive and deliberate judgments are based on common principles. Psychological Review, 118, 97-109. doi:10.1037/a0020762
73. Kruschke, J. K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. Oxford, England: Elsevier Academic Press.
74. Kruschke, J. K. (2012). Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, "Philosophy and the Practice of Bayesian Statistics." British Journal of Mathematical and Statistical Psychology. Advance online publication. doi:10.1111/j.2044-8317.2012.02063.x
75. Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55, 1-7. doi: 10.1016/j.jmp.2010.08.013
76. Lee, M. D., & Cummins, T. D. R. (2004). Evidence accumulation in decision making: Unifying the "take the best" and the "rational" models. Psychonomic Bulletin & Review, 11, 343-352. doi:10.3758/BF03196581
77. Lee, M. D., & Newell, B. R. (2011). Using hierarchical Bayesian methods to examine the tools of decision making. Judgment and Decision Making, 6, 832-842.
78. Lee, M. D., & Wagenmakers, E.-J. (2010). A course in Bayesian graphical modeling for cognitive science. Unpublished manuscript. Retrieved from http://www.ejwagenmakers.com/BayesCourse/BayesBookWeb.pdf
79. Lemaire, P., & Siegler, R. S. (1995). Four aspects of strategic change: Contributions to children's learning of multiplication. Journal of Exper- imental Psychology: General, 124, 83-97. doi:10.1037/0096-3445.124.1.83
80. Lewandowsky, S., Kalish, M., & Ngang, S. K. (2002). Simplified learning in complex situations: Knowledge partitioning in function learning. Journal of Experimental Psychology: General, 131, 163-193. doi: 10.1037/0096-3445.131.2.163
81. Lewandowsky, S., & Kirsner, K. (2000). Knowledge partitioning: Contextdependent use of expertise. Memory & Cognition, 28, 295-305. doi: 10.3758/BF03213807
82. Lewis, S. M., & Raftery, A. E. (1997). Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator. Journal of the American Statistical Association, 92, 648-655.
83. Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103, 410-423. doi:10.1198/016214507000001337
84. Little, D. R., & Lewandowsky, S. (2009). Beyond nonutilization: Irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35, 530-550. doi:10.1037/0096-1523.35.2.530
85. Logan, G. D. (1988). Towards an instance theory of automatization. Psychological Review, 95, 492-527. doi:10.1037/0033-295X.95.4.492
86. Logan, G. D. (2002). An instance theory of attention and memory. Psychological Review, 109, 376-400. doi:10.1037/0033-295X.109.2.376
87. Loomes, G., Moffat, P. G., & Sugden, R. (2002). A microeconomic test of alternative stochastic theories of risky choice. Journal of Risk and Uncertainty, 24, 103-130. doi:10.1023/A:1014094209265
88. Lovett, A. (1988). Choice. In J. R. Anderson & C. Lebiere (Ed.), The atomic components of thought (pp. 255-296). Mahwah, NJ: Erlbaum.
89. Luan, S., Schooler, L. J., & Gigerenzer, G. (2011). A signal-detection analysis of fast-and-frugal trees. Psychological Review, 118, 316-338. doi:10.1037/a0022684
90. Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009). The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28, 3049-3067. doi:10.1002/sim.3680
91. Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). Win- BUGS-A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10, 325-337. doi:10.1023/A: 1008929526011
92. Marewski, J. N., & Schooler, L. J. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118, 393-437. doi: 10.1037/a0024143
93. Masson, M. E. J. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behavior Research Methods, 43, 679-690. doi:10.3758/s13428-010-0049-5
94. Mata, R., von Helversen, B., & Rieskamp, J. (2011). When easy comes hard: The development of adaptive strategy selection. Child Development, 82, 687-700. doi:10.1111/j.1467-8624.2010.01535.x
95. Meng, X. L. (1994). Posterior predictive p-values. Annals of Statistics, 22, 1142-1160. doi:10.1214/aos/1176325622
96. Milinski, M., & Wedekind, C. (1998). Working memory constrains human cooperation in the prisoner's dilemma. PNAS: Proceedings of the National Academy of Sciences, USA, 95, 13755-13758. doi:10.1073/pnas.95.23.13755
97. Myung, I. J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190-204. doi:10.1006/jmps.1999.1283
98. Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4, 79-95. doi:10.3758/BF03210778
99. Myung, J. I., & Pitt, M. A. (2009). Optimal experimental design for model discrimination. Psychological Review, 116, 499-518. doi:10.1037/a0016104
100. Newell, B. R. (2005). Re-visions of rationality? Trends in Cognitive Sciences, 9, 11-15. doi:10. 1016/j. tics.2004.11.005
101. Newell, B. R., & Lee, M. D. (2011). The right tool for the job? Comparing an evidence accumulation and a naïve strategy selection model of decision making. Journal of Behavioral Decision Making, 24, 456-481. doi:10.1002/bdm.703
102. Nilsson, H., Rieskamp, J., & Wagenmakers, E.-J. (2011). Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55, 84-93. doi:10.1016/j.jmp.2010.08.006
103. Nosofsky, R. M. (1991). Tests of an exemplar model for relating perceptual classification and recognition memory. Journal of Experimental Psychology: Human Perception and Performance, 17, 3-27. doi:10.1037/0096-1523.17.1.3
104. Nosofsky, R. M., & Johansen, M. K. (2000). Exemplar-based accounts of "multiple-system" phenomena in perceptual categorization. Psychonomic Bulletin & Review, 7, 375-402.
105. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plusexception model of classification learning. Psychological Review, 101, 53-79. doi:10.1037/0033-295X.101.1.53
106. Ntzoufras, I. (2009). Bayesian modeling using WinBUGS. Hoboken, NJ: Wiley.
107. Patalano, A. L., Smith, E. E., Jonides, J., & Koeppe, R. A. (2001). PET evidence for multiple strategies of categorization. Cognitive, Affective, & Behavioral Neuroscience, 1, 360-370. doi:10.3758/CABN.1.4.360
108. 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. doi:10.1037/0278-7393.14.3.534
109. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The use of multiple strategies in judgment and choice. In N. J. Castellan (Ed.), Individual and group decision making (pp. 19-39). Hillsdale, NJ: Erlbaum.
110. Persson, M., & Rieskamp, J. (2009). Inferences from memory: Strategyand exemplar-based judgment models compared. Acta Psychologica, 130, 25-37. doi:10.1016/j.actpsy.2008.09.010
111. Piaget, J. (1952). The origins of intelligence in children. New York, NY: International University Press. doi:10. 1037/11494-000
112. Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Sciences, 6, 421-425. doi:10.1016/S1364-6613(02)01964-2
113. Pitt, M. A., Myung, I. J., & Zhang, S. B. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491. doi:10.1037/0033-295X.109.3.472
114. Raftery, A. E. (1995). Bayesian model selection in social research. In P. V. Marsden (Ed.), Sociological methodology (pp. 11-196). Cambridge, England: Blackwell.
115. Rieskamp, J. (2006). Perspectives of probabilistic inferences: Reinforcement learning and an adaptive network compared. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 1355-1370. doi:10.1037/0278-7393.32.6.1355
116. Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure: How opportunity costs affect strategy selection. Acta Psychologica, 127, 258-276. doi:10.1016/j.actpsy.2007.05.004
117. Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135, 207-236. doi:10.1037/0096-3445.135.2.207
118. Rissanen, J. (1986a). A predictive least-squares principle. IMA Journal of Mathematical Control and Information, 3, 211-222. doi:10.1093/imamci/3.2-3.211
119. Rissanen, J. (1986b). Stochastic complexity and modeling. Annals of Statistics, 14, 1080-1100. doi:10.1214/aos/1176350051
120. Rissanen, J. (1987). Stochastic complexity. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 49, 223-239.
121. Rissanen, J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42, 40-47. doi:10.1109/18.481776
122. Rissanen, J. (2007). Information and complexity in statistical modeling. New York, NY: Springer.
123. Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic connectionist model of decision making. Psychological Review, 108, 370-392. doi:10.1037/0033-295X.108.2.370
124. Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review, 12, 573-604. doi:10.3758/BF03196750
125. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136
126. Sewell, D. K., & Lewandowsky, S. (2011). Restructuring partitioned knowledge: The role of recoordination in category learning. Cognitive Psychology, 62, 81-122. doi:10.1016/j.cogpsych.2010.09.003
127. Shao, J. (1993). Linear model selection by cross-validation. Journal of the American Statistical Association, 88, 486-494. doi:10.1080/01621459.1993.10476299
128. Shao, J. (1997). An asymptotic theory for linear model selection. Statistica Sinica, 7, 221-242.
129. Shiffrin, R. M., Lee, M. D., Kim, W., & Wagenmakers, E.-J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284. doi:10.1080/03640210802414826
130. Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8, 481-520. doi:10.1016/0010-0285(76)90016-5
131. Siegler, R. S. (1994). Cognitive variability: A key to understanding cognitive development. Current Directions in Psychological Science, 3, 1-5. doi:10.1111/1467-8721.ep10769817
132. Siegler, R. S., & Shipley, C. (1995). Variation, selection, and cognitive change. In T. Simon & G. Halford (Eds.), Developing cognitive competence: New approaches to process modeling (pp. 31-76). Hillsdale, NJ: Erlbaum.
133. Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1-20. doi:10.1146/annurev.ps.41.020190.000245
134. Speekenbrink, M., & Shanks, D. (2010). Learning in a changing environment. Journal of Experimental Psychology: General, 139, 266-298. doi:10.1037/a0018620
135. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 36, 111-147. doi:10.2307/2984809
136. Stone, M. (1977). Asymptotic equivalence of choice of model by crossvalidation and Akaike's criterion. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 39, 44-47.
137. Todd, P. M., & Gigerenzer, G. (2001). Shepard's mirrors or Simon's scissors? Behavioral and Brain Sciences, 24, 704-705. doi:10.1017/S0140525X01650088
138. Todd, P. M., Gigerenzer, G., & the ABC Research Group. (2012). Ecological rationality: Intelligence in the world. New York, NY: Oxford University Press.
139. Townsend, J. T. (1975). The mind-body equation revisited. In C. Cheng (Ed.), Philosophical aspects of the mind-body problem (pp. 200-218). Honolulu, HI: Honolulu University Press.
140. Tversky, A., & Kahneman, D. (1981, January 30). The framing of decisions and the psychology of choice. Science, 211, 453-458. doi: 10.1126/science.7455683
141. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108, 550-592. doi:10.1037/0033-295X.108.3.550
142. Usher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111, 757-769. doi:10.1037/0033-295X.111.3.757
143. van der Maas, H. L. J., Quinlan, P. T., & Jansen, B. R. J. (2007). Towards better computational models of the balance scale task: A reply to Shultz and Takane. Cognition, 103, 473-479. doi:10.1016/j.cognition.2007.01.009
144. Vandierendonck, A. (1995). A parallel rule activation and rule synthesis model for generalization in category learning. Psychonomic Bulletin & Review, 2, 442-459. doi:10.3758/BF03210982
145. Vanpaemel, W. (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology, 54, 491-498. doi:10.1016/j.jmp.2010.07.003
146. von Helversen, B., & Rieskamp, J. (2008). The mapping model: A cognitive theory of quantitative estimation. Journal of Experimental Psychology: General, 137, 73-96. doi:10.1037/0096-3445.137.1.73
147. Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17, 228-243. doi:10.1037/a0027127
148. Veling, H., Holland, R. W., & van Knippenberg, A. (2008). When approach motivation and behavioral inhibition collide: Behavior regulation through stimulus devaluation. Journal of Experimental Social Psychology, 44, 1013-1019. doi:10.1016/j.jesp.2008.03.004
149. Veling, H., van Koningsbruggen, G. M., Stroebe, W., & Aarts, H. (2012). Dieting without drawing on motivation: Facilitating weight loss by reducing unintentional reward-driven eating behavior. Manuscript in preparation.
150. Veltkamp, M., Aarts, H., & Custers, R. (2008). Perception in the service of goal pursuit: Motivation to attain goals enhances the perceived size of goal-instrumental objects. Social Cognition, 26, 720-736. doi:10.1521/soco.2008.26.6.720
151. Visscher, T. L. S., & Seidell, J. C. (2001). The public health impact of obesity. Annual Review of Public Health, 22, 355-375. doi:10.1146/annurev.publhealth.22.1.355
152. Vohs, K. D., & Baumeister, R. F. (2011). Handbook of self-regulation: Research, theory, and applications (2nd ed.). New York, NY: Guilford Press.
153. Vohs, K. D., & Heatherton, T. F. (2000). Self-regulatory failure: A resource depletion approach. Psychological Science, 11, 249-254. doi: 10.1111/1467-9280.00250
154. Volkow, N. D., Wang, G. J., Fowler, J. S., & Telang, F. (2008). Overlapping neuronal circuits in addiction and obesity: Evidence of systems pathology. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 3191-3200. doi:10.1098/rstb.2008.0107
155. Wallis, D. J., & Hetherington, M. M. (2004). Stress and eating: The effects of ego-threat and cognitive demand on food intake in restrained and emotional eaters. Appetite, 43, 39-46. doi:10.1016/j.appet.2004.02.001
156. Wang, G.-J., Tomasi, D., Backus, W., Wang, R., Telang, F., Geliebter, A.,... Volkow, N. D. (2008). Gastric distention activates satiety circuitry in the human brain. NeuroImage, 39, 1824-1831. doi:10.1016/j.neuroimage.2007.11.008
157. Wansink, B. (2004). Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annual Review of Nutrition, 24, 455-479. doi:10.1146/annurev.nutr.24.012003.132140
158. Ward, A., & Mann, T. (2000). Don't mind if I do: Disinhibited eating under cognitive load. Journal of Personality and Social Psychology, 78, 753-763. doi:10.1037/0022-3514.78.4.753
159. Wardle, J., & Beales, S. (1987). Restraint and food intake: An experimental study of eating pattern in the laboratory and in normal life. Behaviour Research and Therapy, 25, 179-185. doi:10.1016/0005-7967(87)90044-1
160. Werthmann, J., Roefs, A., Nederkoorn, C., Mogg, K., Bradley, B. P., & Jansen, A. (2011). Can(not) take my eyes of fit: Attention bias for food in overweight participants. Health Psychology, 30, 561-569. doi: 10.1037/a0024291
161. Westenhoefer, J., Broeckmann, P., Münch, A. K., & Pudel, V. (1994). Cognitive control of eating behaviour and the disinhibition effect. Appetite, 23, 27-41. doi:10.1006/appe.1994.1032
162. Willett, W. C., Manson, J. E., Stampfer, M. J., Colditz, G. A., Rosner, B., Speizer, F. E., & Hennekens, C. H. (1995). Weight, weight change, and coronary heart disease in women: Risk within the 'normal' weight range. JAMA: Journal of the American Medical Association, 273, 461-465. doi:10.1001/jama.1995.03520300035033
163. Wing, R. R. (2004). Behavioral approaches to the treatment of obesity. In G. A. Bray & C. Bouchard (Eds.), Handbook of obesity: Clinical applications (2nd ed., pp. 147-167). New York, NY: Dekker.
164. Wing, R. R., & Hill, J. O. (2001). Successful weight loss maintenance. Annual Review of Nutrition, 21, 323-341. doi:10.1146/annurev.nutr.21.1.323
165. Woody, E. Z., Costanzo, P. R., Liefer, H., & Conger, J. (1981). The effects of taste and caloric perceptions on the eating behavior of restrained and unrestrained subjects. Cognitive Therapy and Research, 5, 381-390. doi:10.1007/BF01173690
166. Yeomans, M. R., Leitch, M., & Mobini, S. (2008). Impulsivity is associated with the disinhibition but not restraint factor from the Three Factor Eating Questionnaire. Appetite, 50, 469-476. doi:10.1016/j.appet.2007.10.002
167. Zeigarnik, B. (1927). Das Behalten erledigter und unerledigter Handlungen [Memory for completed and interrupted tasks]. Psychologische Forschung, 9, 1-85.