Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Response latencies and eye gaze provide insight on how toddlers gather evidence under uncertainty

Nature Human Behaviour volume 4, pages 928–936 (2020)Cite this article

Subjects

Abstract

Toddlers exhibit behaviours that suggest judicious responses to states of uncertainty (for example, turning to adults for help), but little is known about the informational basis of these behaviours. Across two experiments, of which experiment 2 was a preregistered replication, 160 toddlers (aged 25 to 32 months) identified a target from two partially occluded similar (for example, elephant versus bear) or dissimilar (for example, elephant versus broccoli) images. Accuracy was lower for the similar trials than for the dissimilar trials. By fitting drift–diffusion models to response times, we found that toddlers accumulated evidence more slowly but required less evidence for similar trials compared with dissimilar trials. By analysing eye movements, we found that toddlers took longer to settle on the selected image during inaccurate trials and switched their gaze between response options more frequently during inaccurate trials and accurately identified similar items. Exploratory analyses revealed that the evidence-accumulation parameter correlated positively with the use of uncertainty language. Overall, these findings inform theories on the emergence of evidence accumulation under uncertainty.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Response latencies and parameter estimates for the touchscreen task of experiment 1.
Fig. 2: The proportion of looking times and switch counts for the eye-tracker task of experiment 1.
Fig. 3: Response latencies and parameter estimates for the touchscreen task of experiment 2.
Fig. 4: The proportion of looking times and switch counts for the eye-tracker task of experiment 2.
Fig. 5: Example of dissimilar and similar trials.

Data availability

The datasets generated and analysed during the current studies are available at the Open Science Framework repository (https://osf.io/t8p4g/).

Code availability

The code generated and used during the current studies are available at the Open Science Framework repository (https://osf.io/t8p4g/).

References

  1. Coughlin, C., Hembacher, E., Lyons, K. E. & Ghetti, S. Introspection on uncertainty and judicious help‐seeking during the preschool years. Dev. Sci. 18, 957–971 (2015).

    PubMed  Google Scholar 

  2. Jaswal, V. K., Croft, A. C., Setia, A. R. & Cole, C. A. Young children have a specific, highly robust bias to trust testimony. Psychol. Sci. 21, 1541–1547 (2010).

    PubMed  PubMed Central  Google Scholar 

  3. Schulz, L. E. & Bonawitz, E. B. Serious fun: preschoolers engage in more exploratory play when evidence is confounded. Dev. Psychol. 43, 1045–1050 (2007).

    PubMed  Google Scholar 

  4. Stahl, A. E. & Feigenson, L. Observing the unexpected enhances infants’ learning and exploration. Science 348, 91–94 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Balcomb, F. K. & Gerken, L. Three‐year‐old children can access their own memory to guide responses on a visual matching task. Dev. Sci. 11, 750–760 (2008).

    PubMed  Google Scholar 

  6. Lyons, K. E. & Ghetti, S. The development of uncertainty monitoring in early childhood. Child Dev. 82, 1778–1787 (2011).

    PubMed  Google Scholar 

  7. Nelson, T. O. & Narens, L. in Metacognition: Knowing About Knowing vol. 13 (eds Metcalfe, J. & Shimamura, A. P.) 1–25 (MIT Press, 1994).

  8. Dehaene, S., Lau, H. & Kouider, S. What is consciousness, and could machines have it? Science 358, 486–492 (2017).

    CAS  PubMed  Google Scholar 

  9. Hampton, R. R. Multiple demonstrations of metacognition in nonhumans: converging evidence or multiple mechanisms? Comp. Cogn. Behav. Rev. 4, 17–28 (2009).

    PubMed  PubMed Central  Google Scholar 

  10. Smith, J. D. & Washburn, D. A. Uncertainty monitoring and metacognition by animals. Curr. Dir. Psychol. Sci. 14, 19–24 (2005).

    CAS  Google Scholar 

  11. Metcalfe, J. in Handbook of Metamemory and Memory (eds Dunlosky, J. & Bjork, R. A.) 29–46 (Psychology Press, 2008).

  12. Goupil, L., Romand-Monnier, M. & Kouider, S. Infants ask for help when they know they don’t know. Proc. Natl Acad. Sci. USA 113, 3492–3496 (2016).

    CAS  PubMed  Google Scholar 

  13. Goupil, L. & Kouider, S. Behavioral and neural indices of metacognitive sensitivity in preverbal infants. Curr. Biol. 26, 3038–3045 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Desender, K., Boldt, A. & Yeung, N. Subjective confidence predicts information seeking in decision making. Psychol. Sci. 29, 761–778 (2018).

    PubMed  Google Scholar 

  15. Fandakova, Y. et al. Changes in ventromedial prefrontal and insular cortex support the development of metamemory from childhood into adolescence. Proc. Natl Acad. Sci. USA 114, 7582–7587 (2017).

    CAS  PubMed  Google Scholar 

  16. Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two choice decision tasks. Neural Comput. 20, 873–922 (2008).

    PubMed  PubMed Central  Google Scholar 

  17. Roderer, T. & Roebers, C. M. Can you see me thinking (about my answers)? Using eye-tracking to illuminate developmental differences in monitoring and control skills and their relation to performance. Metacogn. Learn. 9, 1–23 (2014).

    Google Scholar 

  18. Folke, T., Jacobsen, C., Fleming, S. M. & De Martino, B. Explicit representation of confidence informs future value-based decisions. Nat. Hum. Behav. 1, 0002 (2016).

    Google Scholar 

  19. Ackerman, R. & Koriat, A. Response latency as a predictor of the accuracy of children’s reports. J. Exp. Psychol. Appl. 17, 406–417 (2011).

    PubMed  Google Scholar 

  20. Lyons, K. E. & Ghetti, S. I don’t want to pick! Introspection on uncertainty supports early strategic behavior. Child Dev. 84, 726–736 (2013).

    PubMed  Google Scholar 

  21. Pleskac, T. J. & Busemeyer, J. R. Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. Psychol. Rev. 117, 864–901 (2010).

    PubMed  Google Scholar 

  22. Heitz, R. P. The speed-accuracy tradeoff: history, physiology, methodology, and behavior. Front. Neurosci. 8, 150 (2014).

    PubMed  PubMed Central  Google Scholar 

  23. Ratcliff, R., Gomez, P. & McKoon, G. A diffusion model account of the lexical decision task. Psychol. Rev. 111, 159–182 (2004).

    PubMed  PubMed Central  Google Scholar 

  24. Metin, B. et al. ADHD performance reflects inefficient but not impulsive information processing: a diffusion model analysis. Neuropsychology 27, 193–200 (2013).

    Google Scholar 

  25. Mulder, M. J. et al. Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder. Biol. Psychiatry 68, 1114–1119 (2010).

    PubMed  Google Scholar 

  26. Ratcliff, R., Love, J., Thompson, C. A. & Opfer, J. E. Children are not like older adults: a diffusion model analysis of developmental changes in speeded responses. Child Dev. 83, 367–381 (2012).

    PubMed  Google Scholar 

  27. Roderer, T. & Roebers, C. M. Explicit and implicit confidence judgments and developmental differences in metamemory: an eye-tracking approach. Metacogn. Learn. 5, 229–250 (2010).

    Google Scholar 

  28. Gehring, W. J., Coles, M. G., Meyer, D. E. & Donchin, E. A brain potential manifestation of error-related processing. Electroencephalogr. Clin. Neurophysiol. Suppl. 44, 261–272 (1995).

    CAS  PubMed  Google Scholar 

  29. Aitken, P. P. & Hutt, C. The effects of stimulus incongruity upon children’s attention, choice, and expressed preference. J. Exp. Child Psychol. 19, 79–87 (1975).

    Google Scholar 

  30. Lerche, V., Voss, A. & Nagler, M. How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria. Behav. Res. Methods 49, 513–537 (2017).

    PubMed  Google Scholar 

  31. Harris, P. L., Bartz, D. T. & Rowe, M. L. Young children communicate their ignorance and ask questions. Proc. Natl Acad. Sci. USA 114, 7884–7891 (2017).

    CAS  PubMed  Google Scholar 

  32. Vandekerckhove, J., Tuerlinckx, F. & Lee, M. D. Hierarchical diffusion models for two choice response times. Psychol. Methods 16, 44–62 (2011).

    PubMed  Google Scholar 

  33. Goupil, L. & Kouider, S. Developing a reflective mind: from core metacognition to explicit self-reflection. Curr. Dir. Psychol. Sci. 28, 403–408 (2019).

    Google Scholar 

  34. Arias-Trejo, N. & Plunkett, K. The effects of perceptual similarity and category membership on early word-referent identification. J. Exp. Child Psychol. 105, 63–80 (2010).

    PubMed  Google Scholar 

  35. Geurten, M. & Bastin, C. Behaviors speak louder than explicit reports: implicit metacognition in 2.5‐year‐old children. Dev. Sci. 22, e12742 (2018).

    PubMed  Google Scholar 

  36. Voss, A., Rothermund, K. & Voss, J. Interpreting the parameters of the diffusion model: an empirical validation. Mem. Cogn. 32, 1206–1220 (2004).

    Google Scholar 

  37. Voss, A., Nagler, M. & Lerche, V. Diffusion models in experimental psychology: a practical introduction. Exp. Psychol. 60, 385–402 (2013).

    PubMed  Google Scholar 

  38. Koriat, A. & Ackerman, R. Choice latency as a cue for children’s subjective confidence in the correctness of their answers. Dev. Sci. 13, 441–453 (2010).

    PubMed  Google Scholar 

  39. Carruthers, P. Basic questions. Mind Lang. 33, 130–147 (2018).

    Google Scholar 

  40. Urgolites, Z. J., Smith, C. N. & Squire, L. R. Eye movements support the link between conscious memory and medial temporal lobe function. Proc. Natl Acad. Sci. USA 115, 7599–7604 (2018).

    CAS  PubMed  Google Scholar 

  41. Fleming, S. M. & Daw, N. D. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation. Psychol. Rev. 124, 91–114 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Yeung, N. & Summerfield, C. Metacognition in human decision-making: confidence and error monitoring. Philos. Trans. R. Soc. B 367, 1310–1321 (2012).

    Google Scholar 

  43. Yassa, M. A. et al. Pattern separation deficits associated with increased hippocampal CA3 and dentate gyrus activity in nondemented older adults. Hippocampus 21, 968–979 (2011).

    PubMed  Google Scholar 

  44. Kuperman, V., Stadthagen-Gonzalez, H. & Brysbaert, M. Age-of-acquisition ratings for 30,000 English words. Behav. Res. Methods 44, 978–990 (2012).

    PubMed  Google Scholar 

  45. Fenson, L. et al. Reply: measuring variability in early child language: don’t shoot the messenger. Child Dev. 71, 323–328 (2000).

    CAS  PubMed  Google Scholar 

  46. Wabersich, D., & Vandekerckhove, J. The RWiener Package: an R package providing distribution functions for the Wiener diffusion model. R J. 6, 49–56 (2014).

  47. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: linear and nonlinear mixed effects models. R package version 3.1-137 https://cran.r-project.org/web/packages/nlme/nlme.pdf (2018).

  48. Droit-Volet, S. Stop using time reproduction tasks in a comparative perspective without further analyses of the role of the motor response: the example of children. Eur. J. Cogn. Psychol. 22, 130–148 (2010).

    Google Scholar 

Download references

Acknowledgements

This research was supported by a grant from the National Science Foundation (NSF; BCS1424058) to S.G. Any opinions, findings and conclusions or recommendations expressed in this manuscript are those of the authors and do not necessarily reflect the views of the NSF. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Center for Mind and Brain, University of California, Davis, Davis, CA, USA

    Sarah Leckey, Diana Selmeczy, Alireza Kazemi, Elliott G. Johnson, Emily Hembacher & Simona Ghetti

  2. Department of Psychology, University of California, Davis, Davis, CA, USA

    Sarah Leckey, Diana Selmeczy, Alireza Kazemi, Emily Hembacher & Simona Ghetti

  3. Human Development Graduate Group, University of California, Davis, Davis, CA, USA

    Elliott G. Johnson

Authors
  1. Sarah Leckey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diana Selmeczy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alireza Kazemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elliott G. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emily Hembacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Simona Ghetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.G. developed the study concept. S.G., S.L. and E.H. finalized the study design. S.L. and E.H. performed data collection. S.L., D.S., A.K. and E.G.J. contributed to data analysis and interpretation under the supervision of S.G. S.L., D.S., E.H. and S.G. drafted the manuscript. All of the authors provided revisions and approved the final version of the manuscript for submission.

Corresponding authors

Correspondence to Sarah Leckey or Simona Ghetti.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Marike Schiffer.

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

Extended data

Extended Data Fig. 1 Quantile plots for drift–diffusion model.

Lines with x markers are plotted based on observed data and dashed lines with o markers are the simulated data produced by our complete model in Experiment 1 (a) and Experiment 2 (b). Graphs show the .1, .3, .5 (median), .7, and .9 quantiles (stacked vertically) plotted against response proportion for each of the two conditions (dissimilar and similar). Similar/Dissimilar labels are placed at the level on x-axis corresponding to the response proportion for that type of trial. Correct response proportions are plotted to the right, and incorrect response proportions are plotted to the left. Predicted values qualitatively resemble observed values, indicating good fit of our drift–diffusion models to the data.

Extended Data Fig. 2 Response latencies and mean proportion looktime in the eye tracker task.

Mean response latencies for dissimilar-accurate, similar-accurate, and inaccurate trials for Experiment 1 (a) and Experiment 2 (c). Mean proportion looktime for the time bin prior to the average response latency for dissimilar-accurate (2–3 seconds for Experiment 1, 4-5 seconds for Experiment 2), similar-accurate (3-4 seconds for Experiment 1, 4-5 seconds for Experiment 2), and inaccurate (4-5 seconds in Experiment 1, 5-6 seconds for Experiment 2) trials for Experiment 1 (b) and Experiment 2 (d). Points represent individual data points. Data points on both graphs are jittered on the horizontal axis to avoid stacking. Error bars are 95 percent confidence intervals.

Extended Data Fig. 3 Effect of Similarity on Reaction Time Inaccurate Trials.

Multilevel model results showing the effect of similarity on reaction times for inaccurate trials for Experiment 1 (a) and Experiment 2 (b). Results displayed here are for models dummy coded relative to dissimilar-inaccurate trial type.

Extended Data Fig. 4 Looking Time Multilevel Model.

Multilevel model results for Experiment 1 (a) and Experiment 2 (b). Results displayed here are for models dummy coded relative to inaccurate trial type and time bin 1. Models were evaluated for significance with a chi-squared difference test. Both models were statistically different from an intercept only model (Experiment 1: X2 (8) = 64.45, p < .001; Experiment 2: X2 (8) = 17.87, p = .02).

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Supplementary Tables 1–3, results and references.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leckey, S., Selmeczy, D., Kazemi, A. et al. Response latencies and eye gaze provide insight on how toddlers gather evidence under uncertainty. Nat Hum Behav 4, 928–936 (2020). https://doi.org/10.1038/s41562-020-0913-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-020-0913-y

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing