Skip to main content
Log in

Confident or familiar? The role of familiarity ratings in adults’ confidence judgments when estimating fraction magnitudes

  • Published:
Metacognition and Learning Aims and scope Submit manuscript

Abstract

Understanding fraction magnitudes is especially important in daily life, but fraction reasoning is quite difficult. To accurately reason about fraction magnitudes, adults need to monitor what they know and what they do not know. However, little is known about which cues adults use to monitor fraction performance. Across two studies, we examined adults’ trial-by-trial fraction estimates, confidence judgments, and ratings of fraction familiarity. Adults were more confident when their estimates were more precise as well as when estimating fractions they rated as more familiar. However, adults judged their confidence in estimating fraction magnitudes, in part, based on their familiarity with each fraction. The role familiarity cues play in judgments of confidence with fractions suggests that people may be less likely to check for errors when reasoning about highly-familiar fractions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. We acknowledge that there is an ongoing debate as to which measure of metacognitive accuracy is best (Fleming and Lau 2014; Higham and Higham 2019). However, we chose to calculate gamma given the similarities between gamma and other signal-detection measures of metacognitive sensitivity (Higham and Higham 2019). Furthermore, gamma is appropriate for our continuous measure of number line estimation precision, whereas signal-detection analyses such as AROC require a dichotomized response (e.g., correct/incorrect), and PAE is a continuous measure of performance.

References

  • Ackerman, R., & Koriat, A. (2011). Response latency as a predictor of the accuracy of children's reports. Journal of Experimental Psychology: Applied, 17(4), 406–417.

    Google Scholar 

  • Alibali, M. W., & Sidney, P. G. (2015). Variability in the natural number bias: Who, when, how, and why. Learning and Instruction, 37, 56–61.

    Google Scholar 

  • Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13(3), 219–235.

    Google Scholar 

  • Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.

    Article  Google Scholar 

  • Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444.

    Google Scholar 

  • Braithwaite, D. W., & Siegler, R. S. (2017). Developmental changes in the whole number bias. Developmental Science, 21(2).

  • Braithwaite, D. W., & Siegler, R. S. (2018). Children learn spurious associations in their math textbooks: Examples from fraction arithmetic. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(11), 1765–1777.

    Google Scholar 

  • Braithwaite, D. W., Pyke, A. A., & Siegler, R. S. (2017). A computational model of fraction arithmetic. Psychological Review, 124(5), 603–625.

    Google Scholar 

  • Braithwaite, D. W., Leib, E. R., Siegler, R. S., & McMullen, J. (2019). Individual differences in fraction arithmetic learning. Cognitive Psychology, 112, 81–98.

    Google Scholar 

  • Dehaene, S., & Mehler, J. (1992). Cross-linguistic regularities in the frequency of number words. Cognition, 43(1), 1–29.

    Google Scholar 

  • Dumas, J. E., Johnson, M., & Lynch, A. M. (2002). Likableness, familiarity, and frequency of 844 person-descriptive words. Personality and Individual Differences, 32(3), 523–531.

    Google Scholar 

  • Dunlosky, J., & Metcalfe, J. (2008). Metacognition. Sage Publications.

  • Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self evaluations undermine students’ learning and retention. Learning and Instruction, 22(4), 271–280.

    Google Scholar 

  • Eason, S. H., & Ramani, G. B. (2018). Parent–child math talk about fractions during formal learning and guided play activities. Child Development., 91, 546–562. https://doi.org/10.1111/cdev.13199.

    Article  Google Scholar 

  • Fazio, L. K., DeWolf, M., & Siegler, R. S. (2016). Strategy use and strategy choice in fraction magnitude comparison. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(1), 1.

    Google Scholar 

  • Finn, B., & Tauber, S. K. (2015). When confidence is not a signal of knowing: How students’ experiences and beliefs about processing fluency can lead to miscalibrated confidence. Educational Psychology Review, 27(4), 567–586.

    Google Scholar 

  • Fitzsimmons, C. J., Thompson, C. A., & Sidney, P. G. (in press). Do adults treat equivalent fractions equally? Adults’ strategies and errors during fraction reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition.

  • Fleming, S. M., & Lau, H. C. (2014). How to measure metacognition. Frontiers in Human Neuroscience, 8, 443.

    Google Scholar 

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170.

    Google Scholar 

  • Gunderson, E. A., & Levine, S. C. (2012). Some types of parent number talk count more than others: Relations between parents’ input and children’s cardinal-number knowledge. Developmental Science, 14(5), 1021–1032.

    Google Scholar 

  • Hall, C. C., Ariss, L., & Todorov, A. (2007). The illusion of knowledge: When more information reduces accuracy and increases confidence. Organizational Behavior and Human Decision Processes, 103(2), 277–290.

    Google Scholar 

  • Handel, M. J. (2016). What do people do at work? Journal for Labour Market Research, 49(2), 177–197.

    Google Scholar 

  • Hertzog, C., Dunlosky, J., Robinson, A. E., & Kidder, D. P. (2003). Encoding fluency is a cue used for judgments about learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(1), 22.

  • Higham, P. A., & Higham, D. P. (2019). New improved gamma: Enhancing the accuracy of Goodman–Kruskal’s gamma using ROC curves. Behavior Research Methods, 51(1), 108–125.

    Google Scholar 

  • Jaeger, A. J., & Wiley, J. (2014). Do illustrations help or harm metacomprehension accuracy? Learning and Instruction, 34, 58–73.

    Google Scholar 

  • Koriat, A. (1997). Monitoring one's own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4), 349–370.

    Google Scholar 

  • Koriat, A. (2008). When confidence in a choice is independent of which choice is made. Psychonomic Bulletin & Review, 15(5), 997–1001.

    Google Scholar 

  • Koriat, A., & Levy-Sadot, R. (2001). The combined contributions of the cue-familiarity and accessibility heuristics to feelings of knowing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(1), 34.

  • Koriat, A., Ackerman, R., Adiv, S., Lockl, K., & Schneider, W. (2014). The effects of goal-driven and data-driven regulation on metacognitive monitoring during learning: A developmental perspective. Journal of Experimental Psychology: General, 143(1), 386–403.

    Google Scholar 

  • Levine, S. C., Suriyakham, L. W., Rowe, M. L., Huttenlocher, J., & Gunderson, E. A. (2010). What counts in the development of young children's number knowledge? Developmental Psychology, 46(5), 1309–1319.

    Google Scholar 

  • Metcalfe, J. (2009). Metacognitive judgments and control of study. Current Directions in Psychological Science, 18(3), 159–163.

    Google Scholar 

  • Mueller, M. L., & Dunlosky, J. (2017). How beliefs can impact judgments of learning: Evaluating analytic processing theory with beliefs about fluency. Journal of Memory and Language, 93, 245–258.

    Google Scholar 

  • Mueller, M. L., Dunlosky, J., Tauber, S. K., & Rhodes, M. G. (2014). The font-size effect on judgments of learning: Does it exemplify fluency effects or reflect people’s beliefs about memory? Journal of Memory and Language, 70, 1–12.

    Google Scholar 

  • Nelson, T. O. (1984). A comparison of current measures of the accuracy of feeling-of-knowing predictions. Psychological Bulletin, 95(1), 109–133.

    Google Scholar 

  • Nelson, L. J., & Fyfe, E. R. (2019). Metacognitive monitoring and help-seeking decisions on mathematical equivalence problems. Metacognition and Learning, 14, 1–21. https://doi.org/10.1007/s11409-019-09203-w.

    Article  Google Scholar 

  • Ni, Y., & Zhou, Y. D. (2005). Teaching and learning fraction and rational numbers: The origins and implications of whole number bias. Educational Psychologist, 40(1), 27–52.

    Google Scholar 

  • O'Leary, A. P., & Sloutsky, V. M. (2016). Carving metacognition at its joints: Protracted development of component processes. Child Development, 88(3), 1015–1032.

    Google Scholar 

  • O'Leary, A. P., & Sloutsky, V. M. (2018). Components of metacognition can function independently across development. Developmental Psychology, 55(2), 315.

    Google Scholar 

  • Opfer, J. E., & DeVries, J. M. (2008). Representational change and magnitude estimation: Why young children can make more accurate salary comparisons than adults. Cognition, 108(3), 843–849.

    Google Scholar 

  • Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006). Numeracy and decision making. Psychological Science, 17(5), 407–413.

    Google Scholar 

  • Peters, E., Hibbard, J., Slovic, P., & Dieckmann, N. (2007). Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Affairs, 26(3), 741–748.

    Google Scholar 

  • Peters, E., Tompkins, M. K., Knoll, M. A., Ardoin, S. P., Shoots-Reinhard, B., & Meara, A. S. (2019). Despite high objective numeracy, lower numeric confidence relates to worse financial and medical outcomes. Proceedings of the National Academy of Sciences, 116(39), 19386–19391.

    Google Scholar 

  • R Core Team. (2015). R: A language and environment for statistical computing. In R Foundation for statistical computing. Vienna: Austria. URL http://www.R-project.org/.

    Google Scholar 

  • Ramani, G. B., & Siegler, R. S. (2008). Promoting broad and stable improvements in low-income children’s numerical knowledge through playing number board games. Child Development, 79(2), 375–394.

    Google Scholar 

  • Ramani, G. B., Siegler, R. S., & Hitti, A. (2012). Taking it to the classroom: Number board games as a small group learning activity. Journal of Educational Psychology, 104(3), 661–672.

    Google Scholar 

  • Reder, L. M., & Ritter, F. E. (1992). What determines initial feeling of knowing? Familiarity with question terms, not with the answer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(3), 435.

  • Sidney, P. G., Thalluri, R., Buerke, M. L., & Thompson, C. A. (2018). Who uses more strategies? Linking mathematics anxiety to adults’ strategy variability and performance on fraction magnitude tasks. Thinking & Reasoning, 1–38.

  • Siegler, R. S., & Booth, J. L. (2004). Development of numerical estimation in young children. Child Development, 75(2), 428–444.

    Google Scholar 

  • Siegler, R. S., & Opfer, J. E. (2003). The development of numerical estimation: Evidence for multiple representations of numerical quantity. Psychological Science, 14(3), 237–250.

    Google Scholar 

  • Siegler, R. S., & Pyke, A. A. (2013). Developmental and individual differences in understanding of fractions. Developmental Psychology, 49(10), 1994–2004.

    Google Scholar 

  • Siegler, R. S., & Ramani, G. B. (2008). Playing linear numerical board games promotes low-income children's numerical development. Developmental Science, 11(5), 655–661.

    Google Scholar 

  • Siegler, R. S., Thompson, C. A., & Schneider, M. (2011). An integrated theory of whole number and fractions development. Cognitive Psychology, 62(4), 273–296.

    Google Scholar 

  • Siegler, R. S., Duncan, G. J., Davis-Kean, P. E., Duckworth, K., Claessens, A., Engel, M., Susperreguy, M. I., & Chen, M. (2012). Early predictors of high school mathematics achievement. Psychological Science, 23(7), 691–697.

    Google Scholar 

  • Tekin, E., & Roediger, H. L. (2017). The range of confidence scales does not affect the relationship between confidence and accuracy in recognition memory. Cognitive Research: Principles and Implications, 2(1), 49.

    Google Scholar 

  • Tekin, E., Lin, W., & Roediger, H. L. (2018). The relationship between confidence and accuracy with verbal and verbal+ numeric confidence scales. Cognitive Research: Principles and Implications, 3(1), 41.

    Google Scholar 

  • Thiede, K. W., Griffin, T. D., Wiley, J., & Anderson, M. C. (2010). Poor metacomprehension accuracy as a result of inappropriate cue use. Discourse Processes, 47(4), 331–362.

    Google Scholar 

  • Torbeyns, J., Schneider, M., Xin, Z., & Siegler, R. S. (2015). Bridging the gap: Fraction understanding is central to mathematics achievement in students from three different continents. Learning and Instruction, 37, 5–13.

    Google Scholar 

  • Wall, J., Thompson, C., & Morris, B. J. (2015). Confidence judgments and eye fixations reveal adults' fractions knowledge. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.), Proceedings of the 37th annual meeting of the cognitive science society (pp. 2571–2576). Austin: Cognitive Science Society.

    Google Scholar 

  • Wall, J. L., Thompson, C. A., Dunlosky, J., & Merriman, W. E. (2016). Children can accurately monitor and control their number-line estimation performance. Developmental Psychology, 52(10), 1493–1502.

    Google Scholar 

Download references

Funding

Support for this research was provided in part by the U.S. Department of Education, Institute of Education Sciences grant R305A160295 to Dr. Clarissa A. Thompson and the Kent State University Judie Fall Lasser Award to Charles J. Fitzsimmons.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charles J. Fitzsimmons.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Part of this report was accepted as a conference proceeding to the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA, 2019). The authors retain full copyrights of the submission. Pre-registrations and project information can be viewed here: https://osf.io/4uygd/.

Electronic supplementary material

ESM 1

(PDF 210 kb)

Appendix

Appendix

Table 5 Smaller and larger component stimuli presented in Experiment 1 (top) and 2 (bottom)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fitzsimmons, C.J., Thompson, C.A. & Sidney, P.G. Confident or familiar? The role of familiarity ratings in adults’ confidence judgments when estimating fraction magnitudes. Metacognition Learning 15, 215–231 (2020). https://doi.org/10.1007/s11409-020-09225-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11409-020-09225-9

Keywords

Navigation