Low-Frequency Neural Oscillations Support Dynamic Attending in Temporal Context
In: Timing & Time PerceptionSearch for other papers by Molly J. Henry in
Current site
Google Scholar
Search for other papers by Björn Herrmann in
Current site
Google Scholar
Behaviorally relevant environmental stimuli are often characterized by some degree of temporal regularity. Dynamic attending theory provides a framework for explaining how perception of stimulus events is affected by the temporal context within which they occur. However, the precise neural implementation of dynamic attending remains unclear. Here, we provide a suggestion for a potential neural implementation of dynamic attending by appealing to low-frequency neural oscillations. The current review will familiarize the reader with the basic theoretical tenets of dynamic attending theory, and review empirical work supporting predictions derived from the theory. The potential neural implementation of dynamic attending theory with respect to low-frequency neural oscillations will be outlined, covering stimulus processing in regular and irregular contexts. Finally, we will provide some more speculative connections between dynamic attending and neural oscillations, and suggest further avenues for future research.
Purchase
Buy instant access (PDF download and unlimited online access):
Institutional Login
Log in with Open Athens, Shibboleth, or your institutional credentials
Personal login
Log in with your brill.com account
-
Allman M. J., Meck W. H. (2012). Pathophysiological distortions in time perception and timed performance. Brain, 135, 656–677.
-
Barnes R., Jones M. R. (2000). Expectancy, attention, and time. Cognitive Psychol., 41, 254–311.
-
Bishop G. H. (1933). Cyclic changes in the excitability of the optic pathway of the rabbit. Am. J. Physiol., 103, 213–224.
-
Block R. A., Zakay D., Hancock P. A. (1999). Developmental changes in human duration judgments: A meta-analytic review. Dev. Rev., 19, 183–211.
-
Bolger D., Trost W., Schön D. (2013). Rhythm implicitly affects orienting of attention across modalities. Acta Psychol., 142, 238–244.
-
Brochard R., Abecasis D., Potter D., Ragot R., Drake C. (2003). The “ticktock” of our interval clock: Direct brain evidence of subjective accents in isochronous sequences. Psychol. Sci., 14, 362–366.
-
Brochard R., Tassin M., Zagar D. (2013). Got rhythm… for better and for worse. Cross-modal effects of auditory rhythm on visual word recognition. Cognition, 127, 214–219.
-
Buhusi C., Meck W. H. (2005). What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci., 6, 755–765.
-
Buhusi C., Meck W. H. (2009). Relative time sharing: New findings and an extension of the resource allocation model of temporal processing. Trans. R. Soc. Lond., 364, 1875–1885.
-
Busch N. A., Dubois J., VanRullen R. (2009). The phase of ongoing EEG oscillations predicts visual perception. J. Neurosci., 29, 7869–7876.
-
Busch N. A., vanRullen R. (2012). Spontaneous EEG oscillations reveal periodic sampling of visual attention. Proc. Natl Acad. Sci. USA, 109, 16048–16053.
-
Buzsaki G., Draguhn A. (2004). Neuronal oscillations in cortical networks. Science, 25, 1926–1929.
-
Canolty R. T., Knight R. T. (2012). The functional role of cross-frequency coupling. Trends Cogn. Sci., 14, 506–515.
-
Church R. M. (2003). A concise introduction to scalar timing theory. In Meck W. H. (Ed.), Functional and neural mechanisms of interval timing (pp. 3–22). Boca Raton, FL: CRC Press.
-
Coull J. T., Cheng R. K., Meck W. H. (2011). Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology, 36, 3–25.
-
Cravo A. M., Rohenkohl G., Wyart V., Nobre A. C. (2013). Temporal expectation enhances contrast sensitivity by phase entrainment of low-frequency oscillations in visual cortex. J. Neurosci., 33, 4002–4010.
-
Drake C., Botte M. C. (1993). Tempo sensitivity in auditory sequences: Evidence for a multiple-look model. Percept. Psychophys., 54, 277–286.
-
Drake C., Jones M. R., Baruch C. (2000). The development of rhythmic attending in auditory sequences: Attunement, referent period, focal attending. Cognition, 77, 251–288.
-
Drewing K., Aschersleben G., Li S.-C. (2006). Sensorimotor synchronization across the lifespan. Int. J. Behav. Dev., 30, 280–287.
-
Fraisse P. (1982). Rhythm and tempo. In Deutsch D. (Ed.), The psychology of music (pp. 149–180). New York: Academic Press.
-
Fujioka T., Trainor L. J., Large E. W., Ross B. (2009). Beta and gamma rhythms in human auditory cortex during musical beat processing. Ann. NY Acad. Sci., 1169, 89–92.
-
Fujioka T., Trainor L. J., Large E. W., Ross B. (2012). Internalized timing of isochronous sounds is represented in neuromagnetic Beta oscillations. J. Neurosci., 32, 1791–1802.
-
Ghitza O. (2011). Linking speech perception and neurophysiology: Speech decoding guided by cascaded oscillators locked to the input rhythm. Front. Psychol., 2. doi:10.3389/fpsyg.2011.00103.
-
Ghitza O., Giraud A.-L., Poeppel D. (2013). Neuronal oscillations and speech perception: Critical-band temporal envelopes are the essence. Front. Hum. Neurosci., 6. doi:10.3389/fnhum.2012.00340.
-
Gibbon J. (1977). Scalar expectancy theory and Weber’s law in animal timing. Psychol. Rev., 84, 279–325.
-
Gibbon J., Church R. M., Meck W. H. (1984). Scalar timing in memory. Ann. NY Acad. Sci., 423, 52–77.
-
Gill P., Zhang J., Woolley S. M. N., Fremouw T., Theunissen F. E. (2006). Sound representation methods for spectro-temporal receptive field estimation. J. Comput. Neurosci., 21, 5–20.
-
Giraud A.-L., Poeppel D. (2012). Cortical oscillations and speech processing: Emerging computational principles and operations. Nat. Neurosci., 15, 511–517.
-
Grondin S. (2001). From physical time to the first and second moments of psychological time. Psychol. Bull., 127, 22–44.
-
Grondin S. (2010). Timing and time perception: A review of recent behavioral and neuroscience findings and theoretical directions. Atten. Percept. Psychophys., 72, 561–582.
-
Gu B. M., Cheng R. K., Yin B., Meck W. H. (2011). Quinpirole-induced sensitization to noisy/sparse periodic input: Temporal synchronization as a component of obsessive-compulsive disorder. Neuroscience, 179, 143–150.
-
Haegens S., Nacher V., Luna R., Romo R., Jensen O. (2011). Alpha-oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl Acad. Sci. USA, 108, 19377–19382.
-
Henry M. J., Herrmann B. (2012). A precluding role of low-frequency oscillations for auditory perception in a continuous processing mode. J. Neurosci., 32, 17525–17527.
-
Henry M. J., Obleser J. (2012). Frequency modulation entrains slow neural oscillations and optimizes human listening behavior. Proc. Natl Acad. Sci. USA, 109, 20095–20100.
-
Ivry R. B., Spencer R. M. C. (2004). The neural representation of time. Curr. Opin. Neurobiol., 14, 225–232.
-
Jensen O., Colgin L. (2007). Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci., 11, 267–269.
-
Jones M. R. (1976). Time, our lost dimension: Toward a new theory of perception, attention, and memory. Psychol. Rev., 83, 323–355.
-
Jones M. R. (2004). Attention and timing. In Neuhoff J. G. (Ed.), Ecological psychoacoustics (pp. 49–85). San Diego, CA, USA: Elsevier, Inc.
-
Jones M. R. (2008). Musical time. In Hallam S., Cross I., Thaut M. (Eds.), Oxford handbook of music psychology (pp. 81–92). Oxford, UK: Oxford University Press.
-
Jones M. R., Boltz M. (1989). Dynamic attending and responses to time. Psychol. Rev., 96, 459–491.
-
Jones M. R., Johnston H. M., Puente J. (2006). Effects of auditory pattern structure on anticipatory and reactive attending. Cognitive Psychol., 53, 59–96.
-
Jones M. R., Moynihan H., MacKenzie N., Puente J. (2002). Temporal aspects of stimulus-driven attending in dynamic arrays. Psychol. Sci., 13, 313–319.
-
Jongsma M., Desain P., Honing H. (2004). Rhythmic context influences the auditory evoked potentials of musicians and nonmusicians. Biol. Psychol., 66, 129–152.
-
Klein J. M., Jones M. R. (1996). Effects of attentional set and rhythmic complexity on attending. Percept. Psychophys., 58, 34–46.
-
Kotz S. A., Schwartze M. (2010). Cortical speech processing unplugged: A timely subcortico-cortical framework. Trends Cogn. Sci., 14, 392–399.
-
Lakatos P., Chen C.-M., O’Connell M. N., Mills A., Schroeder C. E. (2007). Neuronal oscillations and multisensory interactions in primary auditory cortex. Neuron, 53, 272–292.
-
Lakatos P., Karmos G., Mehta A. D., Ulbert I., Schroeder C. E. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science, 320, 110–113.
-
Lakatos P., Musacchia G., O’Connell M. N., Falcher A. Y., Javitt D. C., Schroeder C. E. (2013). The spectrotemporal filter mechanism of auditory selective attention. Neuron, 77, 750–761.
-
Lakatos P., O’Connell M. N., Barczak A., Mills A., Javitt D. C., Schroeder C. E. (2009). The leading sense: Supramodal control of neurophysiological context by attention. Neuron, 64, 419–430.
-
Lakatos P., Shah A. S., Knuth K. H., Ulbert I., Karmos G., Schroeder C. E. (2005). An oscillatory hierarchy controlling neuronal excitability and stimulus processing in auditory cortex. J. Neurophysiol., 94, 1904–1911.
-
Lange K. (2009). Brain correlates of early auditory processing are attenuated by expectations for time and pitch. Brain Cogn., 69, 127–137.
-
Lange K. (2010). Can a regular context induce temporal orienting to a target sound? Int. J. Psychophysiol., 78, 231–238.
-
Large E. W. (2008). Resonating to musical rhythm: Theory and experiment. In Grondin S. (Ed.), Psychology of time (pp. 189–231). Amsterdam: Emerald.
-
Large E. W. (2010). Neurodynamics of music. In Jones M. R., Fay R. R., Popper A. N. (Eds.), Music perception (pp. 201–231). New York, NY, USA: Springer.
-
Large E. W., Jones M. R. (1999). The dynamics of attending: How people track time-varying events. Psychol. Rev., 106, 119–159.
-
Leaver A. M., Rauschecker J. P. (2010). Cortical representation of natural complex sounds: Effects of acoustic features and auditory object category. J. Neurosci., 30, 7604–7612.
-
Lisman J. E., Jensen O. (2013). The theta–gamma neural code. Neuron, 77, 1002–1016.
-
London J. (1995). Some examples of complex meters and their implications for models of metric perception. Music Percept., 13, 59–77.
-
Machens C. K., Wehr M. S., Zador A. M. (2004). Linearity of cortical receptive fields measured with natural sounds. J. Neurosci., 24, 1089–1100.
-
Martin T., Egly R., Houck J. M., Bish J. P., Barrera B. D., Lee C. D., Tesche C. D. (2005). Chronometric evidence for entrained attention. Percept. Psychophys., 67, 168–184.
-
Mathewson K. E., Gratton G., Fabiani M., Beck D. M., Ro T. (2009). To see or not to see: Prestimulus alpha phase predicts visual awareness. J. Neurosci., 29, 2725–2732.
-
Matell M. S., Meck W. H. (2004). Cortico-striatal circuits and interval timing: Coincidence-detection of oscillatory processes. Cogn. Brain Res., 21, 139–170.
-
McAuley J. D. (1995). Perception of time as phase: Toward an adaptive-oscillator model of rhythmic pattern processing. Indiana University.
-
McAuley J. D. (2010). Tempo and rhythm. In Jones M. R., Fay R. R., Popper A. N. (Eds.), Music perception (pp. 165–199). New York, NY, USA: Springer.
-
McAuley J. D., Jones M. R. (2003). Modeling effects of rhythmic context on perceived duration: A comparison of interval and entrainment approaches to short-interval timing. J. Exp. Psychol.-Human, 29, 1102–1125.
-
McAuley J. D., Jones M. R., Holub S., Johnston H. M., Miller N. S. (2006). The time of our lives: Life span development of timing and event tracking. J. Exp. Psychol. Gen., 135, 348.
-
McAuley J. D., Kidd G. R. (1998). Effect of deviations from temporal expectations on tempo discrimination of isochronous tone sequences. J. Exp. Psychol.-Human, 24, 1786–1800.
-
Meck W. H. (1996). Neuropharmacology of timing and time perception. Cogn. Brain Res., 3, 227–242.
-
Meck W. H., Penney T. B., Pouthas V. (2008). Cortico-striatal representation of time in animals and humans. Curr. Opin. Neurobiol., 18, 145–152.
-
Michon J. (1964). Studies on subjective duration I. Differential sensitivity in the perception of repeated temporal intervals. Acta Psychol., 22, 441–450.
-
Miller J. E., Carlson L. A., McAuley J. D. (2013). When what you hear influences when you see: Listening to an auditory rhythm influences the temporal allocation of visual attention. Psychol. Sci., 24, 11–18.
-
Miller N. S., McAuley J. D. (2005). Tempo sensitivity in isochronous tone sequences: The multiple-look model revisited. Percept. Psychophys., 67, 1150–1160.
-
Nelken I., Rotman Y., Yosef O. B. (1999). Responses of auditory-cortex neurons to structural features of natural sounds. Nature, 397, 154–157.
-
Ng B. S. W., Schroeder T., Kayser C. (2012). A precluding but not ensuring role of entrained low-frequency oscillations for auditory perception. J. Neurosci., 32, 12268–12276.
-
Palmer C. (1997). Music performance. Annu. Rev. Psychol., 48, 115–138.
-
Parncutt R. (1994). A perceptual model of pulse salience and metrical accent in musical rhythms. Music Percept., 11, 409–464.
-
Picton T. W., John M. S., Dimitrijevic A., Purcell D. (2003). Human auditory steady-state responses. Int. J. Audiol., 42, 177–219.
-
Quene H., Port R. F. (2005). Effects of timing regularity and metrical expectancy on spoken-word perception. Phonetica, 62, 1–13.
-
Redford M. A. (1999). An articulatory basis for the syllable. PhD thesis, The University of Texas at Austin.
-
Rees A., Green G. G. R., Kay R. H. (1986). Steady-state evoked responses to sinusoidally amplitude-modulated sounds recorded in man. Hearing Res., 23, 123–133.
-
Repp B. H. (2003). Rate limits in sensorimotor synchronization with auditory and visual sequences: the synchronization threshold and the benefits and costs of interval subdivision. J. Motor Behav., 35, 355.
-
Repp B. H. (2005). Sensorimotor synchronization: A review of the tapping literature. Psychol. Bull. & Review, 12, 969–992.
-
Repp B. H. (2006). Rate limits of sensorimotor synchronization. Cognitive Psychol., 2, 163–181.
-
Rohenkohl G., Cravo A. M., Wyart V., Nobre A. C. (2012). Temporal expectation improves the quality of sensory processing. J. Neurosci., 32, 8424–8428.
-
Saleh M., Reimer J., Penn R., Ojakangas C. L., Hatsapoulos N. G. (2010). Fast and slow oscillations in human primary motor cortex predict oncoming behaviorally relevant cues. Neuron, 65, 461–471.
-
Sarter M., Givens B., Bruno J. P. (2001). The cognitive neuroscience of sustained attention: Where top-down meets bottom-up. Brain Res. Rev., 35, 146–160.
-
Schroeder C. E., Lakatos P. (2009). Low-frequency neuronal oscillations as instruments of sensory selection. Tr. Neurosci., 32, 9–18.
-
Schroeder C. E., Lakatos P., Kajikawa Y., Partan S., Puce A. (2008). Neuronal oscillations and visual amplification of speech. Trends Cogn. Sci., 12, 106–113.
-
Schroeder C. E., Wilson D. A., Randman T., Scharfman H., Lakatos P. (2010). Dynamics of active sensing and perceptual selection. Curr. Opin. Neurobiol., 20, 172–176.
-
Schulze H. H. (1978). The detectability of local and global displacements in regular rhythmic patterns. Psychol. Res., 40, 173–181.
-
Schwartze M., Keller P. E., Patel A. D., Kotz S. A. (2011a). The impact of basal ganglia lesions on sensorimotor synchronization, spontaneous motor tempo, and the detection of tempo change. Behav. Brain Res., 216, 685–691.
-
Schwartze M., Rothermich K., Schmidt-Kassow M., Kotz S. A. (2011b). Temporal regularity effects on pre-attentive and attentive processing of deviance. Biol. Psychol., 87, 146–151.
-
Snyder J. S., Large E. W. (2005). Gamma-band activity reflects the metric structure of rhythmic tone sequences. Cogn. Brain Res., 24, 117–126.
-
Stefanics G., Hangya B., Hernadi I., Winkler I., Lakatos P., Ulbert I. (2010). Phase entrainment of human delta oscillations can mediate the effects of expectation on reaction speed. J. Neurosci., 30, 13578–13585.
-
van Noorden L., Moelants D. (1999). Resonance in the perception of musical pulse. J. New Music Res., 28, 43–66.
-
VanRullen R., Busch N. A., Drewes J., Dubno J. R. (2011). Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability. Front. Psychol., 2, 1–9.
-
Volgushev M., Christiakova M., Singer W. (1998). Modification of discharge patterns of neocortical neurons by induced oscillations of the membrane potential. Neuroscience, 83, 15–25.
-
von Stein A., Samthein J. (2000). Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization. Int. J. Psychophysiol., 38, 301–313.
-
Will U., Berg E. (2007). Brain wave synchronization and entrainment to periodic acoustic stimuli. Neuroscience Lett., 424, 55–60.
-
Zaehle T., Lenz D., Ohl F. W., Herrmann C. S. (2010). Resonance phenomena in the human auditory cortex: Individual resonance frequencies of the cerebral cortex determine electrophysiological responses. Exp. Brain Res., 203, 629–635.
-
Zakay D., Block R. A. (1996). The role of attention in time estimation processes. Adv. Psychol., 115, 143–164.
-
Zanto T. P., Large E. W., Fuchs A., Kelso J. S. (2005). Gamma-band responses to perturbed auditory sequences: Evidence for synchronization of perceptual processes. Music Percept., 22, 531–547.
-
Zanto T. P., Snyder J. S., Large E. W. (2006). Neural correlates of rhythmic expectancy. Adv. Cogn. Psychol., 2, 221–231.
-
6
See Giraud & Poeppel (2012), Ghitza (2011) or Ghitza, Giraud, & Poeppel (2013) for such a suggestion in the speech domain.
| All Time | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 3241 | 549 | 30 |
| Full Text Views | 643 | 41 | 0 |
| PDF Views & Downloads | 379 | 85 | 0 |
Low-Frequency Neural Oscillations Support Dynamic Attending in Temporal Context
In: Timing & Time PerceptionSearch for other papers by Molly J. Henry in
Current site
Google Scholar
PubMed
Search for other papers by Björn Herrmann in
Current site
Google Scholar
PubMed
Behaviorally relevant environmental stimuli are often characterized by some degree of temporal regularity. Dynamic attending theory provides a framework for explaining how perception of stimulus events is affected by the temporal context within which they occur. However, the precise neural implementation of dynamic attending remains unclear. Here, we provide a suggestion for a potential neural implementation of dynamic attending by appealing to low-frequency neural oscillations. The current review will familiarize the reader with the basic theoretical tenets of dynamic attending theory, and review empirical work supporting predictions derived from the theory. The potential neural implementation of dynamic attending theory with respect to low-frequency neural oscillations will be outlined, covering stimulus processing in regular and irregular contexts. Finally, we will provide some more speculative connections between dynamic attending and neural oscillations, and suggest further avenues for future research.
| All Time | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 3241 | 549 | 30 |
| Full Text Views | 643 | 41 | 0 |
| PDF Views & Downloads | 379 | 85 | 0 |