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

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.

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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/).

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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

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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.

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The authors declare no competing interests.

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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).

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

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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

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