Abstract
Time perception and prediction errors are essential for everyday life. We hypothesized that their putative shared circuitry in the striatum might enable these two functions to interact. We show that positive and negative prediction errors bias time perception by increasing and decreasing perceived time, respectively. Imaging and behavioral modeling identify this interaction to occur in the putamen. Depending on context, this interaction may have beneficial or adverse effects.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data supporting the findings of this study are available from the corresponding author upon reasonable request.
Code availability
Custom code for behavioral and imaging tests is available from the corresponding author upon reasonable request.
References
Mauk, M. D. & Buonomano, D. V. The neural basis of temporal processing. Annu. Rev. Neurosci. 27, 307–340 (2004).
Dirnberger, G. et al. Give it time: neural evidence for distorted time perception and enhanced memory encoding in emotional situations. Neuroimage 63, 591–599 (2012).
Droit-Volet, S. & Meck, W. H. How emotions colour our perception of time. Trends Cogn. Sci. 11, 504–513 (2007).
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
Niv, Y. & Schoenbaum, G. Dialogues on prediction errors. Trends Cogn. Sci. 12, 265–272 (2008).
Petter, E. A., Gershman, S. J. & Meck, W. H. Integrating models of interval timing and reinforcement learning. Trends Cogn. Sci. 22, 911–922 (2018).
Mikhael, J. G. & Gershman, S. J. Adapting the flow of time with dopamine. J. Neurophysiol. 121, 1748–1760 (2019).
Coull, J. & Nobre, A. Dissociating explicit timing from temporal expectation with fMRI. Curr. Opin. Neurobiol. 18, 137–144 (2008).
Jahanshahi, M., Jones, C. R., Dirnberger, G. & Frith, C. D. The substantia nigra pars compacta and temporal processing. J. Neurosci. 26, 12266–12273 (2006).
D'ardenne, K., McClure, S. M., Nystrom, L. E. & Cohen, J. D. BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science 319, 1264–1267 (2008).
Soares, S., Atallah, B. V. & Paton, J. J. Midbrain dopamine neurons control judgment of time. Science 354, 1273–1277 (2016).
Allman, M. J. & Meck, W. H. Pathophysiological distortions in time perception and timed performance. Brain 135, 656–677 (2012).
Jones, C. R. & Jahanshahi, M. Motor and perceptual timing in Parkinson’s disease. Adv. Exp. Med. Biol. 829, 265–290 (2014).
Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. & Palminteri, S. Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nat. Commun. 9, 1–12 (2018).
Klein, T. A., Ullsperger, M. & Jocham, G. Learning relative values in the striatum induces violations of normative decision making. Nat. Commun. 8, 16033 (2017).
Fayolle, S., Gil, S. & Droit-Volet, S. Fear and time: fear speeds up the internal clock. Behav. Processes 120, 135–140 (2015).
Smith, S. D., McIver, T. A., Di Nella, M. S. & Crease, M. L. The effects of valence and arousal on the emotional modulation of time perception: evidence for multiple stages of processing. Emotion 11, 1305–1313 (2011).
Eagleman, D. M. Human time perception and its illusions. Curr. Opin. Neurobiol. 18, 131–136 (2008).
Failing, M. & Theeuwes, J. Reward alters the perception of time. Cognition 148, 19–26 (2016).
Tse, P. U. Attention underlies subjective temporal expansion. in Attention and Time (eds Nobre, A. C. & Coull, J. T.) 137–150 (Oxford University Press, 2010).
Brainard, D. H. & Vision, S. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997).
Rammsayer, T. & Ulrich, R. The greater temporal acuity in the reminder task than in the 2AFC task is independent of standard duration and sensory modality. Can. J. exp. Psychol. 66, 26–31 (2012).
Ulrich, R. & Vorberg, D. Estimating the difference limen in 2AFC tasks: pitfalls and improved estimators. Atten. Percept. Psychophys. 71, 1219–1227 (2009).
Kahneman, D. Prospect theory: an analysis of decisions under risk. Econometrica 47, 263–292 (1979).
Tom, S. M., Fox, C. R., Trepel, C. & Poldrack, R. A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).
Levitt, H. Transformed up‐down methods in psychoacoustics. J. Acoust. Soc. Am. 49, 467–477 (1971).
Allan, L. G. The time-order error in judgments of duration. Can. J. Psychol. 31, 24–31 (1977).
Hellström, Å. The time-order error and its relatives: mirrors of cognitive processes in comparing. Psychol. Bull. 97, 35–61 (1985).
Laufer, O. & Paz, R. Monetary loss alters perceptual thresholds and compromises future decisions via amygdala and prefrontal networks. J. Neurosci. 32, 6304–6311 (2012).
Taatgen, N. A., Van Rijn, H. & Anderson, J. An integrated theory of prospective time interval estimation: the role of cognition, attention and learning. Psychol. Rev. 114, 577–598 (2007).
Deichmann, R., Gottfried, J. A., Hutton, C. & Turner, R. Optimized EPI for fMRI studies of the orbitofrontal cortex. Neuroimage 19, 430–441 (2003).
Dale, A. M. Optimal experimental design for event-related fMRI. Hum. Brain Mapp. 8, 109–114 (1999).
Goebel, R., Esposito, F. & Formisano, E. Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: from single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis. Hum. Brain Mapp. 27, 392–401 (2006).
Talairach, J. & Tournoux, P. Co-planar Stereotaxic Atlas of the Human Brain (Thieme Medical Publishers, 1988).
Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016).
Woo, C.-W., Krishnan, A. & Wager, T. D. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage 91, 412–419 (2014).
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J. & Friston, K. J. Bayesian model selection for group studies. Neuroimage 46, 1004–1017 (2009).
Acknowledgements
We thank E. Furman-Haran and F. Attar for MRI procedures. This work was supported by a Joy-Ventures grant, Israel Science Foundation grant no.2352/19 and a European Research Council grant (ERC-2016-CoG no. 724910) to R.P.
Author information
Authors and Affiliations
Contributions
I.T. and R.P. designed the study. I.T. performed the experiments and analyzed the data. K.C.A. contributed ideas for analysis. I.T., K.C.A. and R.P. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Behavior-only group’s performance, separated into five levels of PE.
Data presented is similar to main Fig. 1, but only for participants in the behavior-only group, when separating trials into 5 levels of PE (PE0, small (<|3|) and large (>=|3|) PE+/PE-). a. Proportion of discrimination errors as a function of objective time difference between the two images (Δt), averaged across all trials separately for each PE-type. Shown is the main effect of Δt (F4,68 = 26.9, p <10−12, η2 = 0.61), but no difference between PE types (F4,68 = 0.7, p = 0.59). b. Proportion of discrimination errors as a function of PE-type and Trial-type. An interaction between PE-type and Trial-type is evident as PE+ and PE- bias performance in opposite directions relative to PE0 trials (F4,68 = 9.79, p < 10−5, η2 = 0.36). Inset: after correction for individual Time-Order Error (TOE). c. Proportion of discrimination errors as a function of time difference between images (Δt), separately for LS and SL trials. PE+ and PE- bias performance in opposite directions relative to PE0, for all values of Δt. Different outcome magnitudes are not significantly different (p>0.12), and no main effect of Trial-type (F1,17 = 4.1, p = 0.06). d. Proportion of discrimination errors as a function of time difference between images (Δt), separately for LS and SL trials and corrected for TOE. PE+ and PE- bias performance in opposite directions relative to PE0, for all values of Δt. e. Proportion of discrimination errors as a function of individual Just-Noticeable-Difference (JND), corrected for TOE, fitted to a logistic function after JND normalization. Error bars represent SEM (n=18).
Extended Data Fig. 2 Behavior-only group’s performance, separated into three levels of PE.
Data is presented for participants in the behavior-only group, when separating trials into three levels of PE. a. Proportion of discrimination errors as a function of objective time difference between the two images (Δt), averaged across all trials separately for each PE-type. No difference between PE-types (F2,34 = 1.5, p = 0.24). b. Proportion of discrimination errors as a function of PE-type and Trial-type. An interaction between PE-type and Trial-type (F2,34 = 10.89, p = 0.0002, η2 = 0.39). SL trials: mean PE+ = 0.3, mean PE- = 0.49, mean PE0 = 0.37. Better performance in PE+ vs. PE- (p=0.008, Cohen’s d = 0.81), worse performance in PE- compared to PE0 (p = 0.05, Cohen’s d = 0.6). LS trials: mean PE+ = 0.39, mean PE- = 0.21, mean PE0 = 0.275. Worse performance during PE+ vs. PE- (p = 0.005, Cohen’s d = 0.86) and PE0 (p = 0.02, Cohen’s d = 0.68). There was no significant difference between PE- and PE0 trials (p = 0.11). Inset: Performance after correcting for individual Time-Order Error (TOE). c. Proportion of discrimination errors as a function of the time difference between images (Δt), separately for LS and SL trials. PE+ and PE- bias performance in opposite directions relative to PE0 for all values of Δt. No main effect of Trial-type (F1,17 = 4.03, p = 0.06). d. Proportion of discrimination errors as a function of the time difference between images (Δt), separately for LS and SL trials and corrected for TOE. PE+ and PE- bias performance in opposite directions relative to PE0 for all values of Δt. e. Proportion of discrimination errors as a function of the individual Just-Noticeable-Difference (JND), corrected for TOE, fitted to a logistic function after JND normalization. f. Model fit to individual behavioral data, averaged over subjects. Error bars and error bands represent SEM (n=18).
Extended Data Fig. 3 Imaging-group’s performance.
Data is presented for participants in the imaging (fMRI) group, similar to main Fig. 1 and Supplementary Fig. 1, 2. a. Proportion of discrimination errors as a function of objective time difference between the two images (Δt), averaged across all trials separately for each PE-type. Shown is the main effect of Δt (F4,136 = 59.05, p < 10−5, η2 = 0.63), as well as significant interaction between Trial type and Δt (F4,136 = 6.6, p < 10−5, η2 = 0.16). b. Proportion of discrimination errors as a function of PE-type and Trial-type. An interaction between PE-type and Trial-type (F2,68 = 9.1, p = 0.003, η2 = 0.21). SL trials: mean PE+ = 0.33, mean PE- = 0.5, mean PE0 = 0.46. Better performance in PE+ vs. PE- (p<0.001, Cohen’s d = 0.77) and PE0 (p<0.001, Cohen’s d = 0.64), no difference between PE- and PE0. LS trials: mean PE+ = 0.31, mean PE- = 0.23, mean PE0 = 0.28, worse performance in for PE+ vs. PE- (p=0.05, Cohen’s d = 0.4), no difference in performance between PE+ or PE- and PE0. Inset: Performance after correcting for individual Time-Order Error (TOE). c. Proportion of discrimination errors as a function of time difference between images (Δt), separately for LS and SL trials. PE+ and PE- bias performance in opposite directions relative to PE0, for all values of Δt. d. Proportion of discrimination errors as a function of time difference between images (Δt), separately for LS and SL trials and corrected for TOE. PE+ and PE- bias performance in opposite directions relative to PE0, for all values of Δt. e. Proportion of discrimination errors as a function of individual Just-Noticeable-Difference (JND), corrected for TOE, fitted to a logistic function after JND normalization. f. Model fit to individual behavioral data, averaged over subjects. Error bars and error bands represent SEM (n=35). Notice that in this group (imaging), PE0 induced a slight time perception bias, because the distance between PE+ and PE0 is not similar to the distance between PE- and PE0 (see b, c, d, e). This is explained by the different values used in this experiment (−2,4), that create an expected value of 0.2 on average, and hence PE0 also has a small negative PE. For a full explanation and direct experimental evidence, see Supplementary Note subsection “A behavioral difference between the imaging and the behavior-only groups and its explanation (or: why does PE0 also have a slight bias in the fMRI group)”.
Extended Data Fig. 4 Comparing performance between the behavior-only and the imaging group.
a. Time order error (TOE) as a function of Δt, measured from the behavior. TOE is positive in both groups across all Δt, indicating that the first stimulus is perceived as longer. b. Mean expected value from the model in the last 50 trials. Overall, a higher expected value can be seen in the imaging group (two-samples t-test; t49 = 2.4, p = 0.02). This difference is caused by a larger difference in magnitude between the positive and negative outcomes used for the imaging group (+4/−2), as compared to the behavior-only group (+2.5/−2.5). c. Proportion of discrimination errors grouped by PE-type. No difference in performance is seen between the groups for PE+/- (t51 = 0.8/0.9, p = 0.4/0.37, Bayes-Factor >2.5). However, higher proportion of errors in PE0 was found in the imaging group (t51 = 2.52, p = 0.01, Cohen’s d = 0.185), suggesting a non-zero value assigned to these trials during the main paradigm. Right: Proportion of discrimination errors for PE0 trials in the first and second half of main paradigm in the imaging group. The shallower slope (that is indicating worse discrimination) for later trials may result from the fact that the expected value of a trial becomes non-zero as a result of the imbalance between positive and negative outcomes (+4/−2, see b and c, same Figure). Error bars and error bands represent SEM (n=18, 35 for behavior-only and imaging groups, respectively).
Extended Data Fig. 5 Proportion of discrimination errors and TOE correction.
Proportion of discrimination errors as a function of trial type (Long-Short; LS or Short-Long; SL) and (Δt). a. Left: For the raw data, there is a larger difference in performance between LS and SL trials for small Δt (“hard” trials, Δt < 67 ms) compared with large Δt (“easy” trials; mean difference = 0.2 in “hard” and 0.08 in “easy” trials), resulted from significant interaction between Trial-type and Δt (F4,208 = 11.3, p < 10−7, η2 = 0.175). Right: Data corrected for TOE shows similar performance differences between the different levels of Δt for LS and SL trials (F4,208 = 0.75, p = 0.55). b. Separated according to group (before correction to TOE). TOE occurred in all groups, and was therefore corrected for all (see top-right). Error bars represent SEM (n=18, 35 for behavior-only and imaging groups, respectively).
Extended Data Fig. 6 Stability of perceptual thresholds and value control.
a. Comparison of four different methods for computing individual Just-Noticeable-Difference (JND), estimated on the behavior-only group, indicating no difference between the approaches (see Methods for the different approaches; F2,46 = 0.7, p = 0.5, Bayes-Factor > 1.5) which validates the use of staircase JND to normalize the results (n=18). b. Comparison between JND before and after main paradigm. Left plot demonstrates no difference between different measures of JND after main paradigm, whereas right plot demonstrates that perceptual threshold did not change (after minus before) due to main paradigm and was identical across different images (n=47 for first three violins, n=22 for last two violins). The two sets of left violins measure JND when the two consecutive images were overlaid with the same number (positive or negative), to control for value and visual differences. The middle (PE0) and right set of violins (PE+/PE-) measure JND when the two consecutive images are either [0 0],[0 +4],[0–2], correspondingly. Therefore, although we denote it as PE here for clarity, there is no real prediction-error because the outcome is fully predicted within the block. Before the paradigm numbers did not have any meaning (that is did not provide reward/loss), while at the end they did (like in the main paradigm). Overall, the different conditions show that perceptual thresholds did not change: before to after the main paradigm, with or w/o real outcome, and with outcome that is predicted (that is no PE). Error bars represent SEM.
Extended Data Fig. 7 Performance in the Gain-only group.
a. Proportion of discrimination errors as a function of objective time difference between the two images (Δt), averaged across all trials separately for each PE-type. Shown is main effect of Δt (F5,90 = 21.1, p <10−13, η2 = 0.5) but there was no difference between PE-types (F2,36 = 0.59, p = 0.55) or Trial-type (F1,18 = 3.4, p = 0.078), as seen in the main experiment. b. Proportion of discrimination errors as a function of PE-type and Trial-type. An interaction between PE-type and Trial-type (PE-Type x Trial-type: F2,36 = 6.77, p = 0.003, η2 = 0.273). SL trials: mean PE+ = 0.38, mean PE- = 0.48, mean PE0 = 0.41. Better performance in PE+ vs. PE- (p=0.03, Cohen’s d = 0.64). LS trials: mean PE+ = 0.4, mean PE- = 0.3, mean PE0 = 0.32. Worse performance in PE+ vs. PE- (p=0.07, Cohen’s d = 0.54). Inset: Performance after correcting for individual Time-Order Error (TOE). c. Proportion of discrimination errors as a function of time difference between images (Δt), separately for LS and SL trials. PE+ and PE- bias performance in opposite directions. d. Proportion of discrimination errors as a function of individual Just-Noticeable-Difference (JND), fitted to a logistic function after JND normalization. Error bars represent SEM (n=19).
Extended Data Fig. 8 ROIs where activation correlates with trial-to-trial model-derived PE+ signals.
Shown are activations in the dACC and PCC (n=35). Displayed on average brain. Time courses represent mean % signal change extracted from the ROIs ± SEM. Black vertical lines represent trial onset, trial offset and average onset of next trial, respectively. Activation was set to statistical threshold of q = 0.055 for visualization.
Supplementary information
Supplementary Information
Supplementary Note and Supplementary Tables 1–4.
Rights and permissions
About this article
Cite this article
Toren, I., Aberg, K.C. & Paz, R. Prediction errors bidirectionally bias time perception. Nat Neurosci 23, 1198–1202 (2020). https://doi.org/10.1038/s41593-020-0698-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-020-0698-3
This article is cited by
-
Increased elasticity of sucrose demand during hyperdopaminergic states in rats
Psychopharmacology (2022)
-
Using adaptive psychophysics to identify the neural network reset time in subsecond interval timing
Experimental Brain Research (2021)