152
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Detecting how time is subjectively perceived based on event-related potentials (ERPs): a machine learning approach

, , &
Pages 372-380 | Received 30 Apr 2022, Accepted 11 Jul 2022, Published online: 25 Jul 2022

References

  • Meck WH, Doyère V, Gruart A, editors. Interval timing and time-based decision making. Lausanne: Frontiers Media SA; 2012.
  • Näätänen R, Syssoeva O, Takegata R. Automatic time perception in the human brain for intervals ranging from milliseconds to seconds. Psychophysiology. 2004;41(4):660–663.
  • Mioni G, Grondin S, Bardi L, et al. Understanding time perception through non-invasive brain stimulation techniques: a review of studies. Behav Brain Res. 2020;377:112232.
  • Pradhan RK, Tripathy A. Subjective time in neuropsychology vis-a-vis the objective time of physics. NeuroQuantology. 2018;16(11):1303–5150.
  • Chen L, Bao Y, Wittmann M. Sub-and supra-second timing: brain, learning and development. Front Psychol. 2016;7:747.
  • Pariyadath V, Eagleman D. The effect of predictability on subjective duration. PLoS One. 2007;2(11):e1264.
  • Desimone R. Neural mechanisms for visual memory and their role in attention. Proc Natl Acad Sci U S A. 1996;93(24):13494–13499.
  • Pariyadath V, Eagleman DM. Subjective duration distortions mirror neural repetition suppression. PLoS One. 2012;7(12):e49362.
  • Luck SJ. An introduction to the event-related potential technique. Cambridge: MIT Press; 2014.
  • Fromboluti EK, McAuley JD. Perceived duration of auditory oddballs: test of a novel pitch-window hypothesis. Psychol Res. 2020;84(4):915–931.
  • Bendixen A, Grimm S, Schröger E. Human auditory event-related potentials predict duration judgments. Neurosci Lett. 2005;383(3):284–288.
  • Macar F, Vidal F, Casini L. The supplementary motor area in motor and sensory timing: evidence from slow brain potential changes. Exp Brain Res. 1999;125(3):271–280.
  • Pfeuty M, Ragot R, Pouthas V. When time is up: CNV time course differentiates the roles of the hemispheres in the discrimination of short tone durations. Exp Brain Res. 2003;151(3):372–379.
  • Walter WG, Cooper R, Aldridge VJ, et al. Contingent negative variation: an electric sign of sensori-motor association and expectancy in the human brain. Nature. 1964;203(4943):380–384.
  • Macar F, Vidal F. The CNV peak: an index of decision making and temporal memory. Psychophysiology. 2003;40(6):950–954.
  • Ernst B, Reichard SM, Riepl RF, et al. The P3 and the subjective experience of time. Neuropsychologia. 2017;103:12–19.
  • Bernardinis M, Atashzar SF, Jog MS, et al. Differential temporal perception abilities in parkinson’s disease ­patients based on timing magnitude. Sci Rep. 2019;9(1):1–6.
  • Tokushige SI, Terao Y, Matsuda S, et al. Does the clock tick slower or faster in Parkinson’s disease? Insights gained from the synchronized tapping task. Front Psychol. 2018;9:1178.
  • Thoenes S, Oberfeld D. Meta-analysis of time perception and temporal processing in schizophrenia: differential effects on precision and accuracy. Clin Psychol Rev. 2017;54:44–64.
  • Ueda N, Maruo K, Sumiyoshi T. Positive symptoms and time perception in schizophrenia: a meta-analysis. Schizophr Res Cogn. 2018;13:3–6.
  • Ptacek R, Weissenberger S, Braaten E, et al. Clinical implications of the perception of time in attention deficit hyperactivity disorder (ADHD): a review. Med Sci Monit. 2019;25:3918–3924.
  • Nejati V, Yazdani S. Time perception in children with attention deficit–hyperactivity disorder (ADHD): does task matter? A meta-analysis study. Child Neuropsychol. 2020;26(7):900–916.
  • Thönes S, Oberfeld D. Time perception in depression: a meta-analysis. J Affect Disord. 2015;175:359–372.
  • Casassus M, Poliakoff E, Gowen E, et al. Time perception and autistic spectrum condition: a systematic review. Autism Res. 2019;12(10):1440–1462.
  • Allman MJ, Falter CM. Abnormal timing and time perception in autism spectrum disorder? A review of the evidence. In: Allman MJ, Vatakis A, editors. Time distortions in mind; 2015, p. 37–56.
  • Beattie RL, Manis FR. Rise time perception in children with reading and combined reading and language difficulties. J Learn Disabil. 2013 May;46(3):200–209.
  • Szymaszek A, Wolak T, Szelag E. The treatment based on temporal information processing reduces speech comprehension deficits in aphasic subjects. Front Aging Neurosci. 2017;9:98.
  • SMOTE: Synthetic Minority Over-sampling Technique DOI:.
  • Kononenko I, Sˇikonja MR. Non-myopic feature quality evaluation with (R) ReliefF. London: Chapman and Hall/CRC; 2007 Oct 29.
  • Todorov A. An overview of the RELIEF algorithm and advancements. In: Windle M, editor. Statistical approaches to gene X environment interactions for complex phenotypes; 2016, p. 95–116.
  • Nazari MA, Jalalkamali H. Effect of repetition suppression phenomenon and pitch of the auditory stimulus on perceived duration and N1 and P2 auditory evoked potentials (AEP). Advances in Cognitive Science; 2017.
  • Jalalkamali H, Nazari MA. Event Related Potentials (ERP) evidence of predictive coding account of time perception in the sub-second range oddball tasks. Manuscript submitted for publication2022.
  • Jaramillo M, Paavilainen P, Näätänen R. Mismatch negativity and behavioural discrimination in humans as a function of the magnitude of change in sound duration. Neurosci Lett. 2000;290(2):101–104.
  • Tarantino V, Ehlis AC, Baehne C, et al. The time course of temporal discrimination: an ERP study. Clin Neurophysiol. 2010;121(1):43–52.
  • Picton TW, Woods DL, Proulx GB. Human auditory sustained potentials. II. Stimulus relationships. Electroencephalogr Clin Neurophysiol. 1978;45(2):198–210.
  • Roeber U, Widmann A, Schröger E. Auditory distraction by duration and location deviants: a behavioral and event-related potential study. Brain Res Cogn Brain Res. 2003;17(2):347–357.
  • Parvar H, Sculthorpe-Petley L, Satel J, et al. Detection of event-related potentials in individual subjects using support vector machines. Brain Inform. 2015;2(1):1–2.
  • Sturm I, Lapuschkin S, Samek W, et al. Interpretable deep neural networks for single-trial EEG classification. J Neurosci Methods. 2016;274:141–145.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.