Skip to main content
Frontiers in Integrative Neuroscience logoLink to Frontiers in Integrative Neuroscience
. 2011 Oct 31;5:67. doi: 10.3389/fnint.2011.00067

Time and Number: The Privileged Status of Small Values in the Brain

Catalin V Buhusi 1,*, Sara Cordes 2
PMCID: PMC3204429  PMID: 22065383

When dancing, one follows the rhythm without much conscious control, while also singing, entertaining a conversation, planning intricate sequences of steps, or estimating the time until the end of the song. Processing of rapid beats, in the sub-second range seems to be done automatically and doesn't appear to interfere with timekeeping in the range of seconds or minutes, required for planning the steps, or estimating the duration left until the end of the song. This may be because the processing of rapid beats and slow sequences is split between different timing mechanisms and/or the left and the right hemispheres of our brain (Hancock, 2011).

Relatively distinct brain circuits process time in the millisecond, seconds-to-minutes, or circadian range (Buhusi and Meck, 2005), but the distinction blurs around 1-s intervals. Circadian timing, which operates over roughly 24 h, and controls functions such as the sleep–wake cycle and the metabolic processes, is based on a molecular clock in the suprachiasmatic nucleus (Gallego and Virshup, 2007; Allman and Meck, 2011). Millisecond timing engages a variety of specialized local circuits in the cerebellum (De Zeeuw et al., 2011), for fast and fine movement control, or in the auditory cortex, for speech processing (Nourski and Brugge, 2011). Finally, planning and motor control in the seconds range engages the cortico-striatal circuits, motor, parietal, and prefrontal cortices, both in animals (Meck et al., 2008; Buhusi and Meck, 2009) and humans (Coull et al., 2004, 2011; Stevens et al., 2007). The relative separation of these circuits explains why one can deal with different attributes simultaneously, but also raises the possibility of conflicts or cooperation between these circuits for time intervals around 1 s.

The question of whether sub- and supra-second timing engages distinct brain circuits was recently investigated in patients with unilateral hemispheric lesions (Gooch et al., 2011). Rather than selecting patients by lesion, the study used voxel-based lesion-symptom mapping (Bates et al., 2003) in patients with various lesions, and evaluated the contribution of each voxel for the overall performance in all patients, thus tapping into the circuits critical for timing without using a priori assumptions regarding the location of interest.

Three findings bear noting. First, patients with lesions in the frontal or parietal cortices were less accurate than controls, thus supporting neurobiological models of timing suggesting that cortico-striatal circuits (Matell and Meck, 2004; Buhusi and Meck, 2005; Oprisan and Buhusi, 2011) and parietal circuits (Leon and Shadlen, 2003) develop neural representations of time. Second, the right hemisphere was involved in timing both sub- and supra-second timing, consistent with previous studies implicating right cortical regions in interval timing (Schubotz et al., 2000; Rubia et al., 2003; Smith et al., 2003; Coull et al., 2004; Meck and Malapani, 2004; Lewis and Miall, 2006; Bueti et al., 2008), and supporting the hypothesis that right dorsolateral prefrontal cortex is crucial for timekeeping (Lewis and Miall, 2006; Meck et al., 2008). Instead, the left temporal lobe was involved in timing sub-second durations only, consistent with its implication in processing fast, auditory information. Thus, whereas all durations required the same circuitry in the right hemisphere, only the shortest intervals (<1 s) involved additional left-hemisphere structures, suggesting millisecond timing may have a special status in the brain.

This distinction between short (sub-second) and longer intervals (supra-second) is not unique to time. Remarkable parallels exist between counting and timing, such that it has long been thought that counting may tap into similar cognitive and neural mechanisms as that of time (Meck and Church, 1983; Walsh, 2003; Feigenson, 2007; Cantlon et al., 2009). Timing and counting abilities are found in a diverse range of non-human animal species, from honeybees and rats to dolphins and monkeys (Meck and Church, 1983; Cantlon and Brannon, 2007; Cordes et al., 2007; Dacke and Srinivasan, 2008), and they share striking similarities, including Weber's law: The ease with which two durations or numbers are discriminated is based upon their ratio, not their absolute difference (Meck and Church, 1983; Cantlon and Brannon, 2007). All species share a system for representing time and number that must have arisen early in evolutionary history and is present early in development (Xu and Spelke, 2000; vanMarle and Wynn, 2006; Brannon et al., 2007, 2008 – see Gallistel, 1990). In fact, time and number may even be represented using a common metric, in which the representation of one count is equivalent to 200 ms of time (Meck and Church, 1983; but see Balci and Gallistel, 2006). Support for the claim that representations of time and number are derived from the same mechanism is also provided by neurobiological studies of numerical processing, which like those of temporal processing, implicate parietal areas and, at least early in development, this activation is unique to the right hemisphere (Rivera et al., 2005; Cantlon et al., 2006) as in the case of time.

Furthermore, whereas both behavioral and neural evidence suggests a distinction between sub-second and supra-second timing, a similar distinction exists between representations of small (<4 or 5) sets and larger sets. Behavioral data from adults, infants, non-human primates, and even mosquitofish reveal that small sets are treated differently than large sets (e.g., Trick and Pylyshyn, 1994; Hauser and Carey, 2003; Agrillo et al., 2008; Cordes and Brannon, 2009a,b). For example, when asked to rapidly identify the number of items in a set, adults reveal little to no reaction time cost for each additional item in a small set (termed “subitizing”), but once set sizes exceed 4 or 5 items, the slope of the reaction time function markedly increases. More strikingly, despite strict adherence to Weber's law for large set discriminations, infants and non-human animals reveal sharper discrimination abilities when sets are exclusively small (e.g., discriminating 2 from 3 but not 4 from 6, despite similar ratios; Feigenson et al., 2002) yet consistently fail to discriminate small from large sets despite a favorable ratio (e.g., failing to discriminate 2 from 4 or 3 from 6, despite successfully detecting twofold changes in number for larger sets, e.g., 4 vs 8 or 8 vs 16; Xu, 2003; Cordes and Brannon, 2009b). Lastly, infants with Williams syndrome discriminate small sets (2 from 3) but fail in large set discrimination (4 vs 8), suggesting these distinct numerical systems can be selectively affected (Van Herwegen et al., 2008). Whereas, unlike in the case of time where sub-second and supra-second durations are presumably both represented via a common currency of continuous mental magnitudes, evidence suggests that small numerical values may be represented in a distinctly different fashion from large ones (discrete object files for small sets and noisy analog magnitudes for large). Regardless, striking similarities in discontinuities are observed across both systems suggesting there may be something special about these small quantities.

Neural activation patterns for small numbers also selectively involve secondary brain areas distinct from those for large sets. Like time, small and large numerosities alike activate similar neural circuitry, however, again as in the case of time, additional activation is found in distinct structures (the right temporo-parietal junction) when small sets are encountered (Ansari et al., 2006). Interestingly, activation in this additional small-number structure is negatively correlated with reaction times during large number judgments suggesting that it is through the inhibition of this small-number system that large sets are processed. Therefore, much like the case of sub-second timing, additional neural circuitry appears to be dedicated to small numerosities, indicating the ability to process small values may have been favored throughout evolution.

Why is this the case? Processing short durations and small sets are critical to survival. In the case of time, a number of important skills, including speech production and comprehension, motor planning and even musical performance, are dependent upon sub-second timing (e.g., Shannon et al., 1995; Merchant and Georgopoulos, 2006; Tallal and Gaab, 2006). Differences in the order of tens of milliseconds can lead to dramatic differences in phonological processing (in the case of language), motor coordination, and even rhythm perception. In fact, millisecond timing is so crucial for speech that basic training in rhythm and intonation has been found to help patients with non-fluent aphasia regain their speech through singing words they cannot speak (Melodic Intonation Therapy: Naeser and Helm-Estabrooks, 1985; Popovici, 1995; Norton et al., 2009). Similarly, the ability to track small sets of objects can also make the difference between life and death when those objects are predators, prey, or even offspring. Presumably, attending to more than one item at a time should also sub-serve proper functioning in more complex situations such as social interactions and multi-tasking.

Although much has been learned regarding the behavioral and neural signatures across the time and number spectrums, many questions remain unanswered. How does the brain negotiate timing and counting conflicts at these small/large boundaries? Since evidence suggests large numbers are efficiently processed through the inhibition of the small-number brain area, does a similar inhibitory mechanism underlie the processing of durations longer than 1 s? Despite distinct localizations and functions of small set processing and sub-second timing, are there common features to their behavioral or neural make-up? Understanding the competition and cooperation between brain regions involved in interval timing and counting (Lewis and Meck, 2011) may lead to a better understanding of the mechanisms disregulated in disorders such as schizophrenia, dyslexia, Parkinson's disease, Williams syndrome, and dyscalculia – all characterized by timing and/or counting deficits – and the development and refinement of behavioral therapies to alleviate them (e.g., Sparks et al., 1974; Overy, 2003; Wilson et al., 2006; Breier et al., 2010; Wan et al., 2010; Vines et al., 2011).

Acknowledgments

This work was supported by the National Institutes of Health grants MH65561 and MH73057 to Catalin V. Buhusi, and an Alfred P. Sloan Research Fellowship to Sara Cordes. We would like to thank Warren H. Meck for insightful comments on an earlier version of this manuscript.

References

  1. Agrillo C., Dadda M., Serena G., Bisazza A. (2008). Do fish count? Spontaneous discrimination of quantity in female mosquitofish. Anim. Cogn. 11, 495–503 10.1007/s10071-008-0140-9 [DOI] [PubMed] [Google Scholar]
  2. Allman M. J., Meck W. H. (2011). Pathophysiological distortions in time perception and timed performance. Brain. 10.1093/brain/awr210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ansari D., Lyons I. M., van Eimeren L., Xu F. (2006). Linking visual attention and number processing in the brain: the role of the temporo-parietal junction in small and large symbolic and nonsymbolic number comparison. J. Cogn. Neurosci. 19, 1845–1853 10.1162/jocn.2007.19.11.1845 [DOI] [PubMed] [Google Scholar]
  4. Balci F., Gallistel C. R. (2006). Cross-domain transfer of quantitative discriminations: is it all a matter of proportion? Psychon. Bull. Rev. 13, 636–642 10.3758/BF03193974 [DOI] [PubMed] [Google Scholar]
  5. Bates E., Wilson S. M., Saygin A. P., Dick F., Sereno M. I., Knight R. T., Dronkers N. F. (2003). Voxel-based lesion-symptom mapping. Nat. Neurosci. 6, 448–450 [DOI] [PubMed] [Google Scholar]
  6. Brannon E. M., Libertus M. E., Meck W. H., Woldorff M. G. (2008). Electrophysiological measures of time processing in infant and adult brains: Weber's law holds. J. Cogn. Neurosci. 20, 193–203 10.1162/jocn.2008.20016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brannon E. M., Suanda S., Libertus K. (2007). Temporal discrimination increases in precision over development and parallels the development of numerosity discrimination. Dev. Sci. 10, 770–777 10.1111/j.1467-7687.2007.00635.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Breier J. I., Randle S., Maher L. M., Papanicolaou A. C. (2010). Changes in maps of language activity activation following melodic intonation therapy using magnetoencephalography: two case studies. J. Clin. Exp. Neuropsychol. 32, 309–314 10.1080/13803390903029293 [DOI] [PubMed] [Google Scholar]
  9. Bueti D., Walsh V., Frith C., Rees G. (2008). Different brain circuits underlie motor and perceptual representations of temporal intervals. J. Cogn. Neurosci. 20, 204–214 10.1162/jocn.2008.20060 [DOI] [PubMed] [Google Scholar]
  10. Buhusi C. V., Meck W. H. (2005). What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6, 755–765 10.1038/nrn1764 [DOI] [PubMed] [Google Scholar]
  11. Buhusi C. V., Meck W. H. (2009). Relative time sharing: new findings and an extension of the resource allocation model of temporal processing. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1875–1885 10.1098/rstb.2009.0022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cantlon J. F., Brannon E. M. (2007). Basic math in monkeys and college students. PLoS Biol. 5, e328. 10.1371/journal.pbio.0050328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cantlon J. F., Brannon E. M., Carter E. J., Pelphrey K. A. (2006). Functional imaging of numerical processing in adults and 4-y-Old Children. PLoS Biol. 4, e125. 10.1371/journal.pbio.0040125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cantlon J. F., Platt M. L., Brannon E. M. (2009). Beyond the number domain. Trends Cogn. Sci. (Regul. Ed.) 13, 83–91 10.1016/j.tics.2008.11.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cordes S., Brannon E. M. (2009a). The relative salience of discrete and continuous quantities in infants. Dev. Sci. 12, 453–463 10.1111/j.1467-7687.2008.00781.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cordes S., Brannon E. M. (2009b). Crossing the divide: infants discriminate small from large numerosities. Dev. Psychol. 45, 1583–1594 10.1037/a0015666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cordes S., King A. P., Gallistel C. R. (2007). Time left in the mouse. Behav. Processes 74, 142–151 10.1016/j.beproc.2006.10.007 [DOI] [PubMed] [Google Scholar]
  18. Coull J. T., Cheng R. K., Meck W. H. (2011). Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology 36, 3–25 10.1038/npp.2010.113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Coull J. T., Vidal F., Nazarian B., Macar F. (2004). Functional anatomy of the attentional modulation of time estimation. Science 303, 1506–1508 10.1126/science.1091573 [DOI] [PubMed] [Google Scholar]
  20. Dacke M., Srinivasan M. V. (2008). Evidence for counting in insects. Anim. Cogn. 11, 683–689 10.1007/s10071-008-0159-y [DOI] [PubMed] [Google Scholar]
  21. De Zeeuw C. I., Hoebeek F. E., Bosman L. W., Schonewille M., Witter L., Koekkoek S. K. (2011). Spatiotemporal firing patterns in the cerebellum. Nat. Rev. Neurosci. 12, 327–344 10.1038/nrn3011 [DOI] [PubMed] [Google Scholar]
  22. Feigenson L. (2007). The equality of quantity. Trends Cogn. Sci. (Regul. Ed.) 11, 185–187 10.1016/j.tics.2007.01.006 [DOI] [PubMed] [Google Scholar]
  23. Feigenson L., Carey S., Hauser M. (2002). The representations underlying infants’ choice of more: object files versus analog magnitudes. Psychol. Sci. 13, 150–156 10.1111/1467-9280.00427 [DOI] [PubMed] [Google Scholar]
  24. Gallego M., Virshup D. M. (2007). Post-translational modifications regulate the ticking of the circadian clock. Nat. Rev. Mol. Cell Biol. 8, 139–148 10.1038/nrm2106 [DOI] [PubMed] [Google Scholar]
  25. Gallistel C. R. (1990). The Organization of Behavior. Cambridge, MA: MIT Press [Google Scholar]
  26. Gooch C. M., Wiener M., Hamilton A., Coslett H. B. (2011). Temporal discrimination of sub- and suprasecond time intervals: a voxel-based lesion mapping analysis. Front. Integr. Neurosci. 5:59. 10.3389/fnint.2011.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hancock P. A. (2011). On the left hand of time. Am. J. Psychol. 124, 177–188 10.5406/amerjpsyc.124.2.0177 [DOI] [PubMed] [Google Scholar]
  28. Hauser M., Carey S. (2003). Spontaneous representations of small numbers of objects by rhesus macaques: examinations of content and format. Cogn. Psychol. 47, 367–401 10.1016/S0010-0285(03)00050-1 [DOI] [PubMed] [Google Scholar]
  29. Leon M. I., Shadlen M. N. (2003). Representation of time by neurons in the posterior parietal cortex of the macaque. Neuron 38, 317–327 10.1016/S0896-6273(03)00185-5 [DOI] [PubMed] [Google Scholar]
  30. Lewis P. A., Meck W. H. (2011). Does sleep contribute to degeneracy in neural timing? The Psychologist (in press). [Google Scholar]
  31. Lewis P. A., Miall R. C. (2006). A right hemispheric prefrontal system for cognitive time measurement. Behav. Processes 71, 226–234 10.1016/j.beproc.2005.12.009 [DOI] [PubMed] [Google Scholar]
  32. Matell M. S., Meck W. H. (2004). Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Brain Res. Cogn. Brain Res. 21, 139–170 10.1016/j.cogbrainres.2004.06.012 [DOI] [PubMed] [Google Scholar]
  33. Meck W. H., Church R. M. (1983). A mode control model of counting and timing processes. J. Exp. Psychol. Anim. Behav. Process. 9, 320–334 10.1037/0097-7403.9.2.171 [DOI] [PubMed] [Google Scholar]
  34. Meck W. H., Malapani C. (2004). Neuroimaging of interval timing. Brain Res. Cogn. Brain Res. 21, 133–137 10.1016/j.cogbrainres.2004.07.010 [DOI] [PubMed] [Google Scholar]
  35. Meck W. H., Penney T. B., Pouthas V. (2008). Cortico-striatal representation of time in animals and humans. Curr. Opin. Neurobiol. 18, 145–152 10.1016/j.conb.2008.08.002 [DOI] [PubMed] [Google Scholar]
  36. Merchant H., Georgopoulos A. P. (2006). Neurophysiology of perceptual and motor aspects of interception. J. Neurophysiol. 95, 1–13 10.1152/jn.00422.2005 [DOI] [PubMed] [Google Scholar]
  37. Naeser M. A., Helm-Estabrooks N. (1985). CT scan lesion localization and response to melodic intonation therapy with nonfluent aphasia cases. Cortex 21, 203–223 [DOI] [PubMed] [Google Scholar]
  38. Norton A., Zipse L., Marchina S., Schlaug G. (2009). Melodic intonation therapy: shared insights on how it is done and why it might help. Ann. N. Y. Acad. Sci. 1169, 431–436 10.1111/j.1749-6632.2009.04859.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Nourski K. V., Brugge J. F. (2011). Representation of temporal sound features in the human auditory cortex. Nat. Rev. Neurosci. 22, 187–203 [DOI] [PubMed] [Google Scholar]
  40. Oprisan S. A., Buhusi C. V. (2011). Modeling pharmacological clock and memory patterns of interval timing in a striatal beat-frequency model with realistic, noisy neurons. Front. Integr. Neurosci. 5:52. 10.3389/fnint.2011.00052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Overy K. (2003). Dyslexia and music. From timing deficits to musical intervention. Ann. N. Y. Acad. Sci. 999, 497–505 10.1196/annals.1284.060 [DOI] [PubMed] [Google Scholar]
  42. Popovici M. (1995). Melodic intonation therapy in the verbal decoding of aphasics. Rom. J. Neurol. Psychiatry 33, 57–97 [PubMed] [Google Scholar]
  43. Rivera S. M., Reiss A. L., Eckert M. A., Menon V. (2005). Developmental changes in mental arithmetic: evidence for increased functional specialization of the left inferior parietal cortex. Cereb. Cortex 15, 1779–1790 10.1093/cercor/bhi055 [DOI] [PubMed] [Google Scholar]
  44. Rubia K., Smith A. B., Brammer M. J., Taylor E. (2003). Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. Neuroimage 20, 351–358 10.1016/S1053-8119(03)00275-1 [DOI] [PubMed] [Google Scholar]
  45. Schubotz R. I., Friederici A. D., Von Cramon D. Y. (2000). Time perception and motor timing: a common cortical and subcortical basis revealed by fMRI. Neuroimage 11, 1–12 10.1006/nimg.1999.0514 [DOI] [PubMed] [Google Scholar]
  46. Shannon R. V., Zeng F. G., Kamath V., Wygonski J., Ekelid M. (1995). Speech recognition with primarily temporal cues. Science 270, 303–304 10.1126/science.270.5234.303 [DOI] [PubMed] [Google Scholar]
  47. Smith A., Taylor E., Lidzba K., Rubia K. (2003). A right hemispheric frontocerebellar network for time discrimination of several hundreds of milliseconds. Neuroimage 20, 344–350 10.1016/S1053-8119(03)00360-4 [DOI] [PubMed] [Google Scholar]
  48. Sparks R., Helm N., Albert M. (1974). Aphasia rehabilitation resulting from melodic intonation therapy. Cortex 10, 303–316 [DOI] [PubMed] [Google Scholar]
  49. Stevens M. C., Kiehl K. A., Pearlson G., Calhoun V. D. (2007). Functional neural circuits for mental timekeeping. Hum. Brain Mapp. 28, 394–408 10.1002/hbm.20285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Tallal P., Gaab N. (2006). Dynamic auditory processing, musical experience, and language development. Trends Neurosci. 29, 382–390 10.1016/j.tins.2006.06.003 [DOI] [PubMed] [Google Scholar]
  51. Trick L., Pylyshyn Z. (1994). Why are small and large numbers enumerated differently? A limited capacity preattentive stage in vision. Psychol. Rev. 101, 80–102 10.1037/0033-295X.101.1.80 [DOI] [PubMed] [Google Scholar]
  52. Van Herwegen J., Ansari D., Xu F., Karmiloff-Smith A. (2008). Small and large number processing in infants and toddlers with Williams syndrome. Dev. Sci. 11, 637–643 10.1111/j.1467-7687.2008.00711.x [DOI] [PubMed] [Google Scholar]
  53. vanMarle K., Wynn K. (2006). Six-month-old infants use analog magnitudes to represent duration. Dev. Sci. 9, F41–F49 10.1111/j.1467-7687.2006.00508.x [DOI] [PubMed] [Google Scholar]
  54. Vines B. W., Norton A. C., Schlaug G. (2011). Non-invasive brain stimulation enhances the effects of melodic intonation therapy Front. Psychol. 2:230. 10.3389/fpsyg.2011.00230 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Walsh V. (2003). A theory of magnitude: common cortical metrics of time, space, and quantity. Trends Cogn. Sci. (Regul. Ed.) 7, 483–488 10.1016/j.tics.2003.09.002 [DOI] [PubMed] [Google Scholar]
  56. Wan C. Y., Uber T. R., Hohmann A., Schlaug G. (2010). The therapeutic effects of singing in neurological disorders. Music Percept. 27, 287–295 10.1525/mp.2010.27.4.287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wilson A. J., Dehaene S., Pinel P., Revkin S. K., Cohen L., Cohen D. (2006). Principles underlying the design of “The Number Race,” an adaptive computer game for remediation of dyscalculia. Behav. Brain Funct. 2, 1–14 10.1186/1744-9081-2-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Xu F. (2003). Numerosity discrimination in infants: evidence for two systems of representations. Cognition 89, B15–B25 10.1016/S0010-0277(03)00050-7 [DOI] [PubMed] [Google Scholar]
  59. Xu F., Spelke E. S. (2000). Large number discrimination in 6-month-old infants. Cognition 74, B1–B11 10.1016/S0010-0277(99)00066-9 [DOI] [PubMed] [Google Scholar]

Articles from Frontiers in Integrative Neuroscience are provided here courtesy of Frontiers Media SA

RESOURCES