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Temporal context calibrates interval timing


We use our sense of time to identify temporal relationships between events and to anticipate actions. The degree to which we can exploit temporal contingencies depends on the variability of our measurements of time. We asked humans to reproduce time intervals drawn from different underlying distributions. As expected, production times were more variable for longer intervals. However, production times exhibited a systematic regression toward the mean. Consequently, estimates for a sample interval differed depending on the distribution from which it was drawn. A performance-optimizing Bayesian model that takes the underlying distribution of samples into account provided an accurate description of subjects' performance, variability and bias. This finding suggests that the CNS incorporates knowledge about temporal uncertainty to adapt internal timing mechanisms to the temporal statistics of the environment.

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Figure 1: The ready-set-go time-reproduction task.
Figure 2: Time reproduction in different temporal contexts.
Figure 3: The observer model for time reproduction.
Figure 4: MLE, MAP and BLS estimators.
Figure 5: Time-reproduction behavior in humans and model observers.
Figure 6: Time reproduction behavior in humans and model observers: model comparison.


  1. 1

    Mauk, M.D. & Buonomano, D.V. The neural basis of temporal processing. Annu. Rev. Neurosci. 27, 307–340 (2004).

    CAS  Article  Google Scholar 

  2. 2

    Gallistel, C.R. & Gibbon, J. Time, rate, and conditioning. Psychol. Rev. 107, 289–344 (2000).

    CAS  Article  Google Scholar 

  3. 3

    Rakitin, B.C. et al. Scalar expectancy theory and peak-interval timing in humans. J. Exp. Psychol. Anim. Behav. Process. 24, 15–33 (1998).

    CAS  Article  Google Scholar 

  4. 4

    Brannon, E.M., Libertus, M.E., Meck, W.H. & Woldorff, M.G. Electrophysiological measures of time processing in infant and adult brains: Weber's Law holds. J. Cogn. Neurosci. 20, 193–203 (2008).

    Article  Google Scholar 

  5. 5

    Gibbon, J. & Church, R.M. Comparison of variance and covariance patterns in parallel and serial theories of timing. J. Exp. Anal. Behav. 57, 393–406 (1992).

    CAS  Article  Google Scholar 

  6. 6

    Reutimann, J., Yakovlev, V., Fusi, S. & Senn, W. Climbing neuronal activity as an event-based cortical representation of time. J. Neurosci. 24, 3295–3303 (2004).

    CAS  Article  Google Scholar 

  7. 7

    Matell, M.S. & Meck, W.H. Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Brain Res. Cogn. Brain Res. 21, 139–170 (2004).

    Article  Google Scholar 

  8. 8

    Ahrens, M. & Sahani, M. Inferring elapsed time from stochastic neural processes. in Advances in Neural Information Processing Systems (eds. Platt, J.C. Koller, D. Singer, Y. & Roweis, S.) (MIT Press, Cambridge, Massachusetts, 2008).

  9. 9

    Casella, G. & Berger, R.L. (Duxbury Resource Center, Pacific Grove, California, 2002).

  10. 10

    Lewis, P.A. & Miall, R.C. The precision of temporal judgment: milliseconds, many minutes, and beyond. Phil. Trans. R. Soc. Lond. B 364, 1897–1905 (2009).

    CAS  Article  Google Scholar 

  11. 11

    Treisman, M. Temporal discrimination and the indifference interval. Implications for a model of the “internal clock”. Psychol. Monogr. 77, 1–31 (1963).

    CAS  Article  Google Scholar 

  12. 12

    Hollingworth, H.L. The central tendency of judgement. Arch. Psychol. 4, 44–52 (1913).

    Google Scholar 

  13. 13

    Parducci, A. Category judgment: a range-frequency model. Psychol. Rev. 72, 407–418 (1965).

    CAS  Article  Google Scholar 

  14. 14

    Helson, H. Adaptation-level as a basis for a quantitative theory of frames of reference. Psychol. Rev. 55, 297–313 (1948).

    CAS  Article  Google Scholar 

  15. 15

    Kersten, D., Mamassian, P. & Yuille, A. Object perception as Bayesian inference. Annu. Rev. Psychol. 55, 271–304 (2004).

    Article  Google Scholar 

  16. 16

    Körding, K.P. & Wolpert, D.M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004).

    Article  Google Scholar 

  17. 17

    Knill, D.C. & Richards, W. Perception as Bayesian Inference (Cambridge University Press, Cambridge, 1996).

  18. 18

    Mamassian, P., Landy, M.S. & Maloney, L.T. Bayesian modeling of visual perception. in Probabilistic Models of the Brain: Perception and Neural Function (eds. Rao, R.P.N., Olshausen, B.A. & Lewicki, M.S.) 239–286 (MIT Press, Cambridge, Massachusetts, 2002).

    Google Scholar 

  19. 19

    Miyazaki, M., Nozaki, D. & Nakajima, Y. Testing Bayesian models of human coincidence timing. J. Neurophysiol. 94, 395–399 (2005).

    Article  Google Scholar 

  20. 20

    Hudson, T.E., Maloney, L.T. & Landy, M.S. Optimal compensation for temporal uncertainty in movement planning. PLOS Comput. Biol. 4, e1000130 (2008).

    Article  Google Scholar 

  21. 21

    Bernardo, J.M. & Smith, A.F.M. Bayesian Theory (Wiley, New York, 1994).

  22. 22

    Stocker, A.A. & Simoncelli, E.P. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 9, 578–585 (2006).

    CAS  Article  Google Scholar 

  23. 23

    Trommershäuser, J., Maloney, L.T. & Landy, M.S. Statistical decision theory and the selection of rapid, goal-directed movements. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 20, 1419–1433 (2003).

    Article  Google Scholar 

  24. 24

    Mamassian, P. Overconfidence in an objective anticipatory motor task. Psychol. Sci. 19, 601–606 (2008).

    Article  Google Scholar 

  25. 25

    Ernst, M.O. & Banks, M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    CAS  Article  Google Scholar 

  26. 26

    Jacobs, R.A. Optimal integration of texture and motion cues to depth. Vision Res. 39, 3621–3629 (1999).

    CAS  Article  Google Scholar 

  27. 27

    Tassinari, H., Hudson, T.E. & Landy, M.S. Combining priors and noisy visual cues in a rapid pointing task. J. Neurosci. 26, 10154–10163 (2006).

    CAS  Article  Google Scholar 

  28. 28

    Graf, E.W., Warren, P.A. & Maloney, L.T. Explicit estimation of visual uncertainty in human motion processing. Vision Res. 45, 3050–3059 (2005).

    Article  Google Scholar 

  29. 29

    Körding, K.P. & Wolpert, D.M. The loss function of sensorimotor learning. Proc. Natl. Acad. Sci. USA 101, 9839–9842 (2004).

    Article  Google Scholar 

  30. 30

    Raphan, M. & Simoncelli, E.P. Learning to be Bayesian without supervision. in Neural Information Processing Systems 1145–1152 (MIT Press, Cambridge, Massachusetts, 2006).

  31. 31

    Toth, L.J. & Assad, J.A. Dynamic coding of behaviorally relevant stimuli in parietal cortex. Nature 415, 165–168 (2002).

    CAS  Article  Google Scholar 

  32. 32

    Lauwereyns, J. et al. Feature-based anticipation of cues that predict reward in monkey caudate nucleus. Neuron 33, 463–473 (2002).

    CAS  Article  Google Scholar 

  33. 33

    Shadlen, M.N. & Newsome, W.T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001).

    CAS  Article  Google Scholar 

  34. 34

    Gold, J.I., Law, C.T., Connolly, P. & Bennur, S. The relative influences of priors and sensory evidence on an oculomotor decision variable during perceptual learning. J. Neurophysiol. 100, 2653–2668 (2008).

    Article  Google Scholar 

  35. 35

    Janssen, P. & Shadlen, M.N. A representation of the hazard rate of elapsed time in macaque area LIP. Nat. Neurosci. 8, 234–241 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Maimon, G. & Assad, J.A. A cognitive signal for the proactive timing of action in macaque LIP. Nat. Neurosci. 9, 948–955 (2006).

    CAS  Article  Google Scholar 

  37. 37

    Schultz, W. & Romo, R. Role of primate basal ganglia and frontal cortex in the internal generation of movements. I. Preparatory activity in the anterior striatum. Exp. Brain Res. 91, 363–384 (1992).

    CAS  Article  Google Scholar 

  38. 38

    Meck, W.H., Penney, T.B. & Pouthas, V. Cortico-striatal representation of time in animals and humans. Curr. Opin. Neurobiol. 18, 145–152 (2008).

    CAS  Article  Google Scholar 

  39. 39

    Cui, X., Stetson, C., Montague, P.R. & Eagleman, D.M. Ready...go: amplitude of the FMRI signal encodes expectation of cue arrival time. PLoS Biol. 7, e1000167 (2009).

    Article  Google Scholar 

  40. 40

    Nobre, A., Correa, A. & Coull, J. The hazards of time. Curr. Opin. Neurobiol. 17, 465–470 (2007).

    CAS  Article  Google Scholar 

  41. 41

    Rao, S.M., Mayer, A.R. & Harrington, D.L. The evolution of brain activation during temporal processing. Nat. Neurosci. 4, 317–323 (2001).

    CAS  Article  Google Scholar 

  42. 42

    Allan, L.G. Perception of time. Percept. Psychophys. 26, 340–354 (1979).

    Article  Google Scholar 

  43. 43

    Creelman, C.D. Human discrimination of auditory duration. J. Acoust. Soc. Am. 34, 582–593 (1962).

    Article  Google Scholar 

  44. 44

    Lee, I.H. & Assad, J.A. Putaminal activity for simple reactions or self-timed movements. J. Neurophysiol. 89, 2528–2537 (2003).

    Article  Google Scholar 

  45. 45

    Mita, A., Mushiake, H., Shima, K., Matsuzaka, Y. & Tanji, J. Interval time coding by neurons in the presupplementary and supplementary motor areas. Nat. Neurosci. 12, 502–507 (2009).

    CAS  Article  Google Scholar 

  46. 46

    Okano, K. & Tanji, J. Neuronal activities in the primate motor fields of the agranular frontal cortex preceding visually triggered and self-paced movement. Exp. Brain Res. 66, 155–166 (1987).

    CAS  Article  Google Scholar 

  47. 47

    Tanaka, M. Cognitive signals in the primate motor thalamus predict saccade timing. J. Neurosci. 27, 12109–12118 (2007).

    CAS  Article  Google Scholar 

  48. 48

    Tanaka, M. Inactivation of the central thalamus delays self-timed saccades. Nat. Neurosci. 9, 20–22 (2006).

    CAS  Article  Google Scholar 

  49. 49

    Buonomano, D.V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009).

    CAS  Article  Google Scholar 

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We are grateful to G. Horwitz for sharing resources and to both G. Horwitz and V. de Lafuente for their feedback on the manuscript. This work was supported by a fellowship from Helen Hay Whitney Foundation, the Howard Hughes Medical Institute and research grants EY11378 and RR000166 from the US National Institutes of Health.

Author information




M.J. designed the experiment, collected and analyzed the data and performed the computational modeling. M.N.S. helped in data analysis and provided intellectual support throughout the study. M.J. and M.N.S. wrote the manuscript.

Corresponding author

Correspondence to Mehrdad Jazayeri.

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The authors declare no competing financial interests.

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Supplementary Figures 1–6 and Supplementary Tables 1 and 2 (PDF 3650 kb)

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Jazayeri, M., Shadlen, M. Temporal context calibrates interval timing. Nat Neurosci 13, 1020–1026 (2010).

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