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On the category adjustment model: another look at Huttenlocher, Hedges, and Vevea (2000)

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Abstract

Huttenlocher et al. (J Exp Psychol Gen 129:220–241, 2000) introduce the category adjustment model (CAM). Given that participants imperfectly remember stimuli (which we refer to as “targets”), CAM holds that participants maximize accuracy by using information about the distribution of the targets to improve their judgments. CAM predicts that judgments will be a weighted average of the imperfect memory of the target and the mean of the distribution of targets. Huttenlocher et al. (2000) report on three experiments and conclude that CAM is “verified”. We attempt to replicate the conditions in Experiment 3 from Huttenlocher et al. (2000). We analyze judgment-level data rather than averaged data. We find evidence of a bias toward a set of recent targets rather than a bias toward the running mean. We do not find evidence of learning. The judgments in our dataset are not consistent with CAM. We discuss other defects in HHV—including dividing by zero. It seems that evidence for CAM is a statistical artifact that appears when researchers analyze data averaged across trials and do not consider a recency bias.

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Notes

  1. This is also sometimes referred to as the regression effect (Stevens and Greenbaum 1966). The representativeness heuristic (Kahneman and Frederick 2002; Kahneman and Tversky 1973) makes similar predictions. Crosetto et al. (2020) find evidence of the central tendency bias in responses to belief elicitations when the distribution is known to be uniformly distributed.

  2. HHV refer to this as the category.

  3. The entire paragraph is as follows: Our model is a precisely specified Bayesian model. It holds that in pursuing the goal of maximizing accuracy, people use prior information in estimating stimulus values that are represented inexactly. Prior information is incorporated into decision making in the form of an explicit prior distribution, and the inexactness of the fine-grained information is incorporated as a sampling distribution. Given a category (an explicit prior distribution) and an inexact stimulus value (a sampling distribution describing the uncertainty of current data), Bayes’s theorem provides a method for combining the information to provide estimates with certain optimal properties. A posterior distribution summarizes uncertainty after combining the uncertain data and the prior information. The mean of this posterior distribution is called the Bayesian estimate; it has the property of being the “most accurate” estimate in the sense that it minimizes average error.

  4. We also note that “verified” is a word that appears to be inconsistent with Bayesian inference following an experiment with a limited number of participants performing judgments on a limited set of stimuli.

  5. The average of the lengths of lines from the previous trials.

  6. The dangers of analyzing averaged data have been known in the psychology literature for some time (Sidman 1952; Hayes 1953; Estes 1956; Siegler 1987) and such concerns even appear in the recent judgments literature (Cassey et al. 2016; Hemmer et al. 2015).

  7. It seems that the Bayesian judgment literature is unaware of the insights of Savage (1954) and Blackwell and Dubins (1962). However, these references have been in the psychology literature since Edwards et al. (1963). On page 201, the authors state, “From a practical point of view, then, the untrammeled subjectivity of opinion about a parameter ceases to apply as soon as much data become available. More generally, two people with widely divergent prior opinions but reasonably open minds will be forced into arbitrarily close agreement about future observations by a sufficient amount of data. An advanced mathematical expression of this phenomenon is in Blackwell and Dubins (1962)”.

  8. Also see Crawford (2019) and Duffy and Smith (2020).

  9. Windows keyboard properties include 4 different repeat delay settings that range from “Long” (1) to “Short” (4). These experiments were conducted on setting 3. Windows keyboard properties also include 32 different repeat rate settings that range from “Slow” to “Fast”. These experiments were conducted on the fastest setting. As these details are not reported in HHV, we do not know the corresponding conditions in the original experiment.

  10. HHV offered a $5 show-up fee.

  11. HHV had 10 participants in each of the four treatments.

  12. On page 228, HHV describe their criterion, “…we calculated quartiles of the distribution of responses for each stimulus value, and we deleted responses deviating from the median by more than three interquartile ranges (IQRs)”. On page 232, HHV report excluding “0.63% in the uniform condition, 0.36% in the normal, 0.63% in the short half, and 0.35% in the long half”.

  13. On page 232, HHV write, “In the normal conditions, the distribution of stimuli within each block was as follows: once at 45 and 390; twice at 60 and 375; three times at 75, 90, 345, and 360; four times at 105, 120, 315, and 330; five times at 135, 150, 285, and 300; six times at 165, 180, 255, and 270; and seven times at 195, 210, 225, and 240”.

  14. We also note that the HHV lines had a thickness of 0.23 cm, rather than 0.36 cm in our experiment.

  15. HHV refer to this variable simply as bias. However, we also examine biases with different definitions, so we employ the term response bias.

  16. Given their reported degrees of freedom, it seems as if HHV conducted the tests assuming an equal variance between the samples. The reader might be concerned about the appropriateness of this. Our results are not changed when we conduct paired t tests or unpaired t tests that do not assume an equal variance.

  17. We find a significant difference between the normal and the uniform treatments (t(18) = 2.75, p = 0.013) and a significant difference between the short treatment and the shortest lines in the uniform treatment (t(22) = 6.73, p < 0.001). However, we do not find a significant difference between the long treatment and the longest lines in the uniform treatment (t(22) = 0.29, p = 0.77).

  18. We also note that we excluded 1.54% of trials whereas HHV excluded 0.49%.

  19. Page 229.

  20. We use the term specification to refer to the complete set of assumptions in the analysis, including the functional form, the choice of explanatory variables, the assumptions regarding the error term, and the set of data under consideration.

  21. Mean response bias is significantly less than zero in the normal treatment (M = − 10.40, SD = 38.09, t(1891) = − 11.88, p < 0.001), the uniform treatment (M = − 10.72, SD = 45.41, t(1687) = − 9.69, p < 0.001), and the long treatment (M = − 21.97, SD = 42.67, t(2065) = − 23.41, p < 0.001), but not in the short treatment (M = − 0.55, SD = 23.40, t(2104) = − 1.08, p = 0.28).

  22. Here we only include targets 224 and 240 in the normal and uniform treatments, targets 320 and 336 in the long treatment, and targets 128 and 144 in the short treatment.

  23. Restricted to the central two values, mean response bias is significantly less than zero in the normal treatment (M = − 10.27, SD = 27.65, t(278) = − 6.20, p < 0.001), the uniform treatment (M = − 9.61, SD = 32.89, t(144) = − 3.51, p < 0.001), and the long treatment (M = − 20.80, SD = 38.17, t(342) = − 10.10, p < 0.001), but not in the short treatment (M = 1.31, SD = 21.73, t(351) = 1.13, p = 0.26).

  24. We conduct a paired t test (t(11) = − 10.85, p < 0.001), an unpaired t test that does not assume an equal variance (t(18) = − 3.99, p < 0.001), and a t test that does not assume an equal variance over all observations (t(3191.3) = − 20.05, p < 0.001), and the results are not changed.

  25. We include the repeated measures because it is a better model. However, the results without repeated measures are qualitatively similar to those with repeated measures, in this and in subsequent analyses.

  26. The pooled analysis appears in the “None” specification of Table 5.

  27. Regressions that analyze data from trials 2 through 192 have 7713 observations. The total 7751 minus 41 participants making judgments on the first trial, however 3 first trial judgments are excluded due to their inaccuracy.

  28. Table 3 and the regression tables that follow are not consistent with the American Psychological Association (APA) format for regressions. However, the APA format makes it difficult to display multiple specifications because the coefficient estimates and the standard errors are listed in separate columns. Since we prefer to display multiple specifications in each table, we present the regressions in a format, standard in other fields, with a regression in each column.

  29. This is robust to the specification of the error term. See Table A1 in the Supplemental Online Appendix. This is robust to a quadratic specification. See Table A10 in the Supplemental Online Appendix.

  30. See Jesteadt et al. (1977), Staddon et al. (1980), Petzold (1981), Laming (1984), DeCarlo and Cross (1990), Choplin and Hummel (2002), Stewart et al. (2002), Petzold and Haubensak (2004), Wilder et al. (2009), Yu and Cohen (2009) and Jones et al. (2013).

  31. The pooled analysis appears in the “Prec 1” specification of Table 5.

  32. This is robust to the specification of the error term. See Table A2 in the Supplemental Online Appendix. This is robust to a quadratic specification. See Table A11 in the Supplemental Online Appendix.

  33. This is robust to the specification of the error term. See Table A3 in the Supplemental Online Appendix. This is robust to quadratic specifications. See Tables A12, A13, and A14 in the Supplemental Online Appendix.

  34. See Table A4 in the Supplemental Online Appendix.

  35. For the reader worried about multicollinearity driving the results in Table 5 (and Table A3), we note that the identical multicollinearity exists in Table 6 (and Table A4). However, with the simulated data we find the relationship predicted by CAM but in the non-simulated data we do not find such a relationship. Therefore, we reject this potential objection.

  36. For the uniform, normal, and long treatments the maximum is 416. For the short treatment the maximum is 224.

  37. For the uniform, normal, and short treatments the minimum is 48. For the long treatment the minimum is 240.

  38. See Table A5 in the Supplemental Online Appendix.

  39. See Table A6 in the Supplemental Online Appendix.

  40. See Table A7 in the Supplemental Online Appendix.

  41. We note that most of the results that we report in this paper are similar to those reported in Duffy and Smith (2018). The exception is the relationship between running mean bias and trials. Duffy and Smith do not find a relationship in the Duffy et al. (2010) data, but here we find a strong relationship.

  42. See Table A8 in the Supplemental Online Appendix.

  43. Interestingly, we note a positive correlation between the response time and previous bias (r(7711) = 0.038, p = 0.002) but no such relationship between response time and running mean bias (r(7711) = − 0.015, p = 0.19).

  44. See Table A9 in the Supplemental Online Appendix.

  45. We use the notation of HHV. However, it is not clear to us why the authors chose to employ a non-standard notation for the partial derivatives. Standard notation would be \(\frac{\partial S(R)}{{\partial \sigma_{P} }}\).

  46. Again, we use the notation of HHV. Standard notation would be \(\frac{\partial B(R)}{{\partial \sigma_{P} }}\).

  47. Although we note that HHV (page 233) refer to “dark” trials in the discussion of Experiment 3. Recall that Experiment 2 in HHV involved judgments of color shade but Experiment 3 involved judgments of length.

  48. See Barth et al. (2015), Bowers and Davis (2012a, b), Cassey et al. (2016), Chater et al. (2006, 2011), Duffy and Smith (2018), Elqayam and Evans (2011), Goodman et al. (2015), Griffiths et al. (2012), Griffiths and Tenenbaum (2006), Hahn (2014), Jones and Love (2011a, b), Hemmer and Steyvers (2009a, b), Lewandowsky et al. (2009), Marcus and Davis (2013, 2015), Mozer et al. (2008), Perfors et al. (2011), Petzschner et al. (2015), Rahnev and Denison (2018), Sailor and Antoine (2005), Tauber et al. (2017) and Tenenbaum et al. (2006).

  49. For instance, we note two violations of the chain rule on page 239.

References

  • Allred S, Crawford LE, Duffy S, Smith J (2016) Working memory and spatial judgments: cognitive load increases the central tendency bias. Psychon Bull Rev 23(6):1825–1831

    Article  Google Scholar 

  • Ashourian P, Loewenstein Y (2011) Bayesian inference underlies the contraction bias in delayed comparison tasks. PLoS ONE 6(5):e19551

    Article  Google Scholar 

  • Bae GY, Olkkonen M, Allred SR, Flombaum JI (2015) Why some colors appear more memorable than others: a model combining categories and particulars in color working memory. J Exp Psychol Gen 144(4):744–763

    Article  Google Scholar 

  • Barth H, Lesser E, Taggart J, Slusser E (2015) Spatial estimation: a non-Bayesian alternative. Dev Sci 18(5):853–862

    Article  Google Scholar 

  • Blackwell D, Dubins L (1962) Merging of opinions with increasing information. Ann Math Stat 33:882–886

    Article  Google Scholar 

  • Bowers JS, Davis CJ (2012a) Bayesian just-so stories in psychology and neuroscience. Psychol Bull 138(3):389–414

    Article  Google Scholar 

  • Bowers JS, Davis CJ (2012b) Is that what Bayesians believe? Reply to Griffiths, Chater, Norris, and Pouget (2012). Psychol Bull 138(3):423–426

    Article  Google Scholar 

  • Cassey P, Hawkins GE, Donkin C, Brown SD (2016) Using alien coins to test whether simple inference is Bayesian. J Exp Psychol Learn Mem Cognit 42(3):497–503

    Article  Google Scholar 

  • Chater N, Tenenbaum JB, Yuille A (2006) Probabilistic models of cognition: conceptual foundations. Trends Cognit Sci 10(7):287–291

    Article  Google Scholar 

  • Chater N, Goodman N, Griffiths TL, Kemp C, Oaksford M, Tenenbaum JB (2011) The imaginary fundamentalists: the unshocking truth about Bayesian cognitive science. Behav Brain Sci 34(4):194–196

    Article  Google Scholar 

  • Choplin JM, Hummel JE (2002) Magnitude comparisons distort mental representations of magnitude. J Exp Psychol Gen 131(2):270–286

    Article  Google Scholar 

  • Corbin JC, Crawford LE, Vavra DT (2017) Misremembering emotion: inductive category effects for complex emotional stimuli. Mem Cognit 45(5):691–698

    Article  Google Scholar 

  • Corneille O, Huart J, Becquart E, Brédart S (2004) When memory shifts toward more typical category exemplars: accentuation effects in the recollection of ethnically ambiguous faces. J Personal Soc Psychol 86(2):236–250

    Article  Google Scholar 

  • Crawford LE (2019) Reply to Duffy and Smith’s (2018) reexamination. Psychon Bull Rev 26(2):693–698

    Article  Google Scholar 

  • Crawford LE, Duffy S (2010) Sequence effects in estimating spatial location. Psychon Bull Rev 17(5):725–730

    Article  Google Scholar 

  • Crawford LE, Huttenlocher J, Engebretson PH (2000) Category effects on estimates of stimuli: perception or reconstruction? Psychol Sci 11(4):280–284

    Article  Google Scholar 

  • Crosetto P, Filippin A, Katuščák P, Smith J (2020) Central tendency bias in belief elicitation. J Econ Psychol 78:102273

    Article  Google Scholar 

  • DeCarlo LT, Cross DV (1990) Sequential effects in magnitude scaling: models and theory. J Exp Psychol Gen 119(4):375–396

    Article  Google Scholar 

  • Duffy S, Smith J (2018) Category effects on stimulus estimation: shifting and skewed frequency distributions—a reexamination. Psychon Bull Rev 25(5):1740–1750

    Article  Google Scholar 

  • Duffy S, Smith J (2020) Omitted-variable bias and other matters in the defense of the category adjustment model: a reply to Crawford. J Behav Exp Econ 85:101501

    Article  Google Scholar 

  • Duffy S, Huttenlocher J, Hedges LV, Crawford LE (2010) Category effects on stimulus estimation: shifting and skewed frequency distributions. Psychon Bull Rev 17:224–230

    Article  Google Scholar 

  • Edwards W, Lindman H, Savage LJ (1963) Bayesian statistical inference for psychological research. Psychol Rev 70(3):193–242

    Article  Google Scholar 

  • Elqayam S, Evans JSB (2011) Subtracting “ought” from “is”: descriptivism versus normativism in the study of human thinking. Behav Brain Sci 34(5):233–248

    Article  Google Scholar 

  • Eriksson K (2012) The nonsense math effect. Judgm Decis Mak 7(6):746–749

    Google Scholar 

  • Estes WK (1956) The problem of inference from curves based on group data. Psychol Bull 53(2):134–140

    Article  Google Scholar 

  • Feldman NH, Griffiths TL, Morgan JL (2009) The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference. Psychol Rev 116(4):752–782

    Article  Google Scholar 

  • Fugate JM (2013) Categorical perception for emotional faces. Emot Rev 5(1):84–89

    Article  Google Scholar 

  • Goodman ND, Frank MC, Griffiths TL, Tenenbaum JB, Battaglia PW, Hamrick JB (2015) Relevant and robust: a response to Marcus and Davis (2013). Psychol Sci 26(4):539–541

    Article  Google Scholar 

  • Griffiths TL, Tenenbaum JB (2006) Optimal predictions in everyday cognition. Psychol Sci 17(9):767–773

    Article  Google Scholar 

  • Griffiths TL, Chater N, Norris D, Pouget A (2012) How the Bayesians got their beliefs (and what those beliefs actually are): comment on Bowers and Davis (2012). Psychol Bull 138(3):415–422

    Article  Google Scholar 

  • Hahn U (2014) The Bayesian boom: good thing or bad? Front Psychol 5:765

    Article  Google Scholar 

  • Hayes KJ (1953) The backward curve: a method for the study of learning. Psychol Rev 60(4):269–275

    Article  Google Scholar 

  • Hemmer P, Steyvers M (2009a) Integrating episodic memories and prior knowledge at multiple levels of abstraction. Psychon Bull Rev 16(1):80–87

    Article  Google Scholar 

  • Hemmer P, Steyvers M (2009b) A Bayesian account of reconstructive memory. Top Cognit Sci 1:189–202

    Article  Google Scholar 

  • Hemmer P, Tauber S, Steyvers M (2015) Moving beyond qualitative evaluations of Bayesian models of cognition. Psychon Bull Rev 22(3):614–628

    Article  Google Scholar 

  • Hertwig R, Pachur T, Kurzenhäuser S (2005) Judgments of risk frequencies: tests of possible cognitive mechanisms. J Exp Psychol Learn Mem Cognit 31(4):621–642

    Article  Google Scholar 

  • Holden MP, Curby KM, Newcombe NS, Shipley TF (2010) A category adjustment approach to memory for spatial location in natural scenes. J Exp Psychol Learn Mem Cognit 36(3):590–604

    Article  Google Scholar 

  • Hollingworth HL (1910) The central tendency of judgment. J Philos Psychol Sci Methods 7(17):461–469

    Google Scholar 

  • Hund AM, Spencer JP (2003) Developmental changes in the relative weighting of geometric and experience-dependent location cues. J Cognit Dev 4(1):3–38

    Article  Google Scholar 

  • Huttenlocher J, Hedges LV, Vevea JL (2000) Why do categories affect stimulus judgment? J Exp Psychol Gen 129:220–241

    Article  Google Scholar 

  • Jesteadt W, Luce RD, Green DM (1977) Sequential effects in judgments of loudness. J Exp Psychol Hum Percept Perform 3(1):92–104

    Article  Google Scholar 

  • Jones M, Love BC (2011a) Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behav Brain Sci 34(4):169–188

    Article  Google Scholar 

  • Jones M, Love BC (2011b) Pinning down the theoretical commitments of Bayesian cognitive models. Behav Brain Sci 34(4):215–231

    Article  Google Scholar 

  • Jones M, Curran T, Mozer MC, Wilder MH (2013) Sequential effects in response time reveal learning mechanisms and event representations. Psychol Rev 120(3):628–666

    Article  Google Scholar 

  • Kahneman D, Frederick S (2002) Representativeness revisited: attribute substitution in intuitive judgment. In: Gilovich T, Griffin D, Kahneman D (eds) Heuristics and biases: the psychology of intuitive judgment. Cambridge University Press, Cambridge, pp 49–81

    Chapter  Google Scholar 

  • Kahneman D, Tversky A (1973) On the psychology of prediction. Psychol Rev 80(4):237–251

    Article  Google Scholar 

  • Laming D (1984) The relativity of ‘absolute’ judgements. Br J Math Stat Psychol 37(2):152–183

    Article  Google Scholar 

  • Lewandowsky S, Griffiths TL, Kalish ML (2009) The wisdom of individuals: exploring people’s knowledge about everyday events using iterated learning. Cognit Sci 33(6):969–998

    Article  Google Scholar 

  • Marcus GF, Davis E (2013) How robust are probabilistic models of higher-level cognition? Psychol Sci 24(12):2351–2360

    Article  Google Scholar 

  • Marcus GF, Davis E (2015) Still searching for principles: a response to Goodman et al. (2015). Psychol Sci 26(4):542–544

    Article  Google Scholar 

  • McCullough S, Emmorey K (2009) Categorical perception of affective and linguistic facial expressions. Cognition 110(2):208–221

    Article  Google Scholar 

  • Moore DA, Healy PJ (2008) The trouble with overconfidence. Psychol Rev 115(2):502–517

    Article  Google Scholar 

  • Mozer MC, Pashler H, Homaei H (2008) Optimal predictions in everyday cognition: the wisdom of individuals or crowds? Cognit Sci 32(7):1133–1147

    Article  Google Scholar 

  • Norris D, McQueen JM (2008) Shortlist B: a Bayesian model of continuous speech recognition. Psychol Rev 115(2):357–395

    Article  Google Scholar 

  • Olkkonen M, Allred SR (2014) Short-term memory affects color perception in context. PLoS ONE 9(1):e86488

    Article  Google Scholar 

  • Olkkonen M, McCarthy PF, Allred SR (2014) The central tendency bias in color perception: effects of internal and external noise. J Vis 14(11):1–15

    Article  Google Scholar 

  • Perfors A, Tenenbaum JB, Griffiths TL, Xu F (2011) A tutorial introduction to Bayesian models of cognitive development. Cognition 120(3):302–321

    Article  Google Scholar 

  • Persaud K, Hemmer P (2014) The influence of knowledge and expectations for color on episodic memory. Proc Cognit Sci Soc 36:1162–1167

    Google Scholar 

  • Petzold P (1981) Distance effects on sequential dependencies in categorical judgments. J Exp Psychol Hum Percept Perform 7(6):1371–1385

    Article  Google Scholar 

  • Petzold P, Haubensak G (2004) The influence of category membership of stimuli on sequential effects in magnitude judgment. Percept Psychophys 66(4):665–678

    Article  Google Scholar 

  • Petzschner FH, Glasauer S, Stephan KE (2015) A Bayesian perspective on magnitude estimation. Trends Cognit Sci 19(5):285–293

    Article  Google Scholar 

  • Poulton EC (1979) Models for biases in judging sensory magnitude. Psychol Bull 86(4):777–803

    Article  Google Scholar 

  • Psychology Software Tools, Inc. (2012) E-Prime 2.0. http://www.pstnet.com. Accessed Oct 2015

  • Rahnev D, Denison RN (2018) Suboptimality in perceptual decision making. Behav Brain Sci 41:1–66

    Article  Google Scholar 

  • Roberson D, Damjanovic L, Pilling M (2007) Categorical perception of facial expressions: evidence for a “category adjustment” model. Mem Cognit 35(7):1814–1829

    Article  Google Scholar 

  • Sailor KM, Antoine M (2005) Is memory for stimulus magnitude Bayesian? Mem Cognit 33:840–851

    Article  Google Scholar 

  • Sampson RJ, Raudenbush SW (2004) Seeing disorder: neighborhood stigma and the social construction of “broken windows”. Soc Psychol Q 67(4):319–342

    Article  Google Scholar 

  • Savage LJ (1954) The foundations of statistics. Wiley, New York. Reprinted in 1972 by Dover, New York

  • Schutte AR, Spencer JP (2009) Tests of the dynamic field theory and the spatial precision hypothesis: capturing a qualitative developmental transition in spatial working memory. J Exp Psychol Hum Percept Perform 35(6):1698–1725

    Article  Google Scholar 

  • Sidman M (1952) A note on functional relations obtained from group data. Psychol Bull 49(3):263–269

    Article  Google Scholar 

  • Siegler RS (1987) The perils of averaging data over strategies: an example from children’s addition. J Exp Psychol Gen 116(3):250–264

    Article  Google Scholar 

  • Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci 22(11):1359–1366

    Article  Google Scholar 

  • Spencer JP, Hund AM (2002) Prototypes and particulars: geometric and experience-dependent spatial categories. J Exp Psychol Gen 131(1):16–37

    Article  Google Scholar 

  • Spencer JP, Hund AM (2003) Developmental continuity in the processes that underlie spatial recall. Cognit Psychol 47(4):432–480

    Article  Google Scholar 

  • Sperber D (2010) The guru effect. Rev Philos Psychol 1(4):583–592

    Article  Google Scholar 

  • Staddon JE, King M, Lockhead GR (1980) On sequential effects in absolute judgment experiments. J Exp Psychol Hum Percept Perform 6(2):290–301

    Article  Google Scholar 

  • Steegen S, Tuerlinckx F, Gelman A, Vanpaemel W (2016) Increasing transparency through a multiverse analysis. Perspect Psychol Sci 11(5):702–712

    Article  Google Scholar 

  • Stevens SS, Greenbaum HB (1966) Regression effect in psychophysical judgment. Percept Psychophys 1(5):439–446

    Article  Google Scholar 

  • Stewart N, Brown GD, Chater N (2002) Sequence effects in categorization of simple perceptual stimuli. J Exp Psychol Learn Mem Cognit 28(1):3–11

    Article  Google Scholar 

  • Tauber S, Navarro DJ, Perfors A, Steyvers M (2017) Bayesian models of cognition revisited: setting optimality aside and letting data drive psychological theory. Psychol Rev 124(4):410–441

    Article  Google Scholar 

  • Tenenbaum JB, Griffiths TL, Kemp C (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends Cognit Sci 10(7):309–318

    Article  Google Scholar 

  • Twedt E, Crawford LE, Proffitt DR (2015) Judgments of others’ heights are biased toward the height of the perceiver. Psychon Bull Rev 22(2):566–571

    Article  Google Scholar 

  • Vines TH, Albert AY, Andrew RL, Débarre F, Bock DG, Franklin MT, Gilbert KJ, Moore JS, Renaut S, Rennison DJ (2014) The availability of research data declines rapidly with article age. Curr Biol 24(1):94–97

    Article  Google Scholar 

  • Wagenmakers EJ, Wetzels R, Borsboom D, Maas HLJ (2011) Why psychologists must change the way they analyze their data: the case of psi. J Personal Soc Psychol 100(3):426–432

    Article  Google Scholar 

  • Wicherts JM, Veldkamp CL, Augusteijn HE, Bakker M, van Aert RC, Van Assen MA (2016) Degrees of freedom in planning, running, analyzing, and reporting psychological studies: a checklist to avoid p-hacking. Front Psychol 7:1832

    Article  Google Scholar 

  • Wilder M, Jones M, Mozer MC (2009) Sequential effects reflect parallel learning of multiple environmental regularities. Adv Neural Inf Process Syst 22:2053–2061

    Google Scholar 

  • Woods AT, Poliakoff E, Lloyd DM, Dijksterhuis GB, Thomas A (2010) Flavor expectation: the effect of assuming homogeneity on drink perception. Chemosens Percept 3(3–4):174–181

    Article  Google Scholar 

  • Young SG, Hugenberg K, Bernstein MJ, Sacco DF (2009) Interracial contexts debilitate same-race face recognition. J Exp Soc Psychol 45(5):1123–1126

    Article  Google Scholar 

  • Yu AJ, Cohen JD (2009) Sequential effects: superstition or rational behavior? Adv Neural Inf Process Syst 21:1873–1880

    Google Scholar 

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Acknowledgements

We thank Roberto Barbera, Alex Brown, I-Ming Chiu, Caleb Cox, L. Elizabeth Crawford, Steven Gussman, Johanna Hertel, Matt Jones, Richard McLean, Rosemarie Nagel, Adam Sanjurjo, and Barry Sopher for helpful comments. This project was supported by Rutgers University Research Council Grants #202297 and #18-AA-00143. John Smith thanks Biblioteca de Catalunya.

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  1. Department of Psychology, Rutgers University-Camden, Camden, NJ, USA

    Sean Duffy

  2. Department of Economics, Rutgers University-Camden, 311 N. 5th Street, Camden, NJ, 08102, USA

    John Smith

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Duffy, S., Smith, J. On the category adjustment model: another look at Huttenlocher, Hedges, and Vevea (2000). Mind Soc 19, 163–193 (2020). https://doi.org/10.1007/s11299-020-00229-1

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