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Sensory stimulation can systematically bias the perceived passage of time [1Fraisse P. The Psychology of Time. Eyre and Spottiswoode, London1964Google Scholar, 2Brown S.W. Time, change, and motion: The effects of stimulus movement on temporal perception.Percept. Psychophys. 1995; 57: 105-116Crossref PubMed Scopus (225) Google Scholar, 3Eagleman D.M. Tse P.U. Buonomano D.V. Janssen P. Nobre A.C. Holcombe A.O. Time and the brain: How subjective time relates to neural time.J. Neurosci. 2005; 25: 10369-10371Crossref PubMed Scopus (134) Google Scholar, 4Xuan B. Zhang D. He S. Chen X. Larger stimuli are judged to last longer.J. Vis. 2007; 7: 2.1-5Crossref Scopus (306) Google Scholar, 5Pariyadath V. Eagleman D.M. The effect of predictability on subjective duration.PLoS ONE. 2007; 2: e1264Crossref PubMed Scopus (180) Google Scholar], but why and how this happens is mysterious. In this report, we provide evidence that such biases may ultimately derive from an innate and adaptive use of stochastically evolving dynamic stimuli to help refine estimates derived from internal timekeeping mechanisms [6Gibbon J. Scalar expectancy theory and Weber's law in animal timing.Psychol. Rev. 1977; 84: 279-325Crossref Scopus (1586) Google Scholar, 7Killeen P.R. Fetterman J.G. A behavioral theory of timing.Psychol. Rev. 1988; 95: 274-285Crossref PubMed Scopus (549) Google Scholar, 8Miall R.C. The storage of time intervals using oscillating neurons.Neural Comput. 1989; 1: 359-371Crossref Google Scholar, 9Buonomano D.V. Merzenich M.M. Temporal information transformed into a spatial code by a neural network with realistic properties.Science. 1995; 267: 1028-1030Crossref PubMed Scopus (333) Google Scholar, 10Bugmann G. Towards a neural model of timing.Biosystems. 1998; 48: 11-19Crossref PubMed Scopus (13) Google Scholar, 11Staddon J.E.R. Higa J.J. Time and memory: Towards a pacemaker-free theory of interval timing.J. Exp. Anal. Behav. 1999; 71: 215-251Crossref PubMed Scopus (288) Google Scholar, 12Shapiro J.L. Wearden J. Barone R. A simple model exhibiting scalar timing.in: French R.M. Sougn J.P. Connectionist Models of Learning, Development, and Evolution, Perspectives in Neural Computing. Springer-Verlag, London2001: 43-52Crossref Google Scholar, 13Durstewitz D. Self-organizing neural integrator predicts interval times through climbing activity.J. Neurosci. 2003; 23: 5342-5353PubMed Google Scholar, 14Buhusi C.V. Meck W.H. What makes us tick? Functional and neural mechanisms of internal timing.Nat. Rev. Neurosci. 2005; 6: 755-765Crossref PubMed Scopus (1357) Google Scholar, 15Karmarkar U.R. Buonomano D.V. Timing in the absence of clocks: Encoding time in neural network states.Neuron. 2007; 53: 427-438Abstract Full Text Full Text PDF PubMed Scopus (371) Google Scholar]. A simplified statistical model based on probabilistic expectations of stimulus change derived from the second-order temporal statistics of the natural environment [16Dong D.W. Atick J.J. Statistics of natural time-varying images.Network. 1995; 6: 345-358Crossref Scopus (194) Google Scholar, 17Attias H. Schreiner C.E. Temporal low-order statistics of natural sounds.Adv. Neural Inf. Process. Syst. 1996; 9: 27-33Google Scholar] makes three predictions. First, random noise-like stimuli whose statistics violate natural expectations should induce timing bias. Second, a previously unexplored obverse of this effect is that similar noise stimuli with natural statistics should reduce the variability of timing estimates. Finally, this reduction in variability should scale with the interval being timed, so as to preserve the overall Weber law of interval timing. All three predictions are borne out experimentally. Thus, in the context of our novel theoretical framework, these results suggest that observers routinely rely on sensory input to augment their sense of the passage of time, through a process of Bayesian inference based on expectations of change in the natural environment.
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