6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Both movement and neural activity in humans can be entrained by the regularities of an external stimulus, such as the beat of musical rhythms. Neural entrainment to auditory rhythms supports temporal perception, and is enhanced by selective attention and by hierarchical temporal structure imposed on rhythms. However, it is not known how neural entrainment to rhythms is related to the subjective experience of groove (the desire to move along with music or rhythm), the perception of a regular beat, the perception of complexity, and the experience of pleasure. In two experiments, we used musical rhythms (from Steve Reich’s Clapping Music) to investigate whether rhythms that are performed by humans (with naturally variable timing) and rhythms that are mechanical (with precise timing), elicit differences in (1) neural entrainment, as measured by inter-trial phase coherence, and (2) subjective ratings of the complexity, preference, groove, and beat strength of rhythms. We also combined results from the two experiments to investigate relationships between neural entrainment and subjective perception of musical rhythms. We found that mechanical rhythms elicited a greater degree of neural entrainment than performed rhythms, likely due to the greater temporal precision in the stimulus, and the two types only elicited different ratings for some individual rhythms. Neural entrainment to performed rhythms, but not to mechanical ones, correlated with subjective desire to move and subjective complexity. These data, therefore, suggest multiple interacting influences on neural entrainment to rhythms, from low-level stimulus properties to high-level cognition and perception.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: not found
          • Article: not found

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human.

            Considerable interest has been raised by non-phase-locked episodes of synchronization in the gamma-band (30-60 Hz). One of their putative roles in the visual modality is feature-binding. We tested the stimulus specificity of high-frequency oscillations in humans using three types of visual stimuli: two coherent stimuli (a Kanizsa and a real triangle) and a noncoherent stimulus ("no-triangle stimulus"). The task of the subject was to count the occurrences of a curved illusory triangle. A time-frequency analysis of single-trial EEG data recorded from eight human subjects was performed to characterize phase-locked as well as non-phase-locked high-frequency activities. We found in early phase-locked 40 Hz component, maximal at electrodes Cz-C4, which does not vary with stimulation type. We describe a second 40 Hz component, appearing around 280 msec, that is not phase-locked to stimulus onset. This component is stronger in response to a coherent triangle, whether real or illusory: it could reflect, therefore, a mechanism of feature binding based on high-frequency synchronization. Because both the illusory and the real triangle are more target-like, it could also correspond to an oscillatory mechanism for testing the match between stimulus and target. At the same latencies, the low-frequency evoked response components phase-locked to stimulus onset behave differently, suggesting that low- and high-frequency activities have different functional roles.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Tagging the neuronal entrainment to beat and meter.

              Feeling the beat and meter is fundamental to the experience of music. However, how these periodicities are represented in the brain remains largely unknown. Here, we test whether this function emerges from the entrainment of neurons resonating to the beat and meter. We recorded the electroencephalogram while participants listened to a musical beat and imagined a binary or a ternary meter on this beat (i.e., a march or a waltz). We found that the beat elicits a sustained periodic EEG response tuned to the beat frequency. Most importantly, we found that meter imagery elicits an additional frequency tuned to the corresponding metric interpretation of this beat. These results provide compelling evidence that neural entrainment to beat and meter can be captured directly in the electroencephalogram. More generally, our results suggest that music constitutes a unique context to explore entrainment phenomena in dynamic cognitive processing at the level of neural networks.
                Bookmark

                Author and article information

                Contributors
                (226) 378-1946 , camerd7@mcmaster.ca
                Journal
                Exp Brain Res
                Exp Brain Res
                Experimental Brain Research
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0014-4819
                1432-1106
                31 May 2019
                31 May 2019
                2019
                : 237
                : 8
                : 1981-1991
                Affiliations
                [1 ]ISNI 0000 0004 1936 8227, GRID grid.25073.33, Department of Psychology, Neuroscience and Behaviour, , McMaster University, ; Hamilton, ON Canada
                [2 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, School of Biological and Chemical Sciences, , Queen Mary University of London, ; London, UK
                [3 ]ISNI 0000 0001 2191 6040, GRID grid.15874.3f, Department of Psychology, , Goldsmiths, University of London, ; London, UK
                [4 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Center for Music in the Brain, Department of Clinical Medicine, , Aarhus University, ; Aarhus, Denmark
                [5 ]ISNI 0000 0001 2290 8069, GRID grid.8767.e, AI Lab, , Vrije Universiteit Brussel, ; Brussels, Belgium
                [6 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, School of Electronic Engineering and Computer Science, , Queen Mary University of London, ; London, UK
                [7 ]ISNI 0000 0001 2191 6040, GRID grid.15874.3f, Department of Music, , Goldsmiths, University of London, ; London, UK
                Author information
                http://orcid.org/0000-0001-5543-9836
                http://orcid.org/0000-0002-1587-112X
                http://orcid.org/0000-0003-3443-9049
                Article
                5557
                10.1007/s00221-019-05557-4
                6647194
                31152188
                0342e4b9-86d5-4a20-bb13-01f3028c5ee4
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 14 September 2018
                : 7 May 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP-H01294X
                Categories
                Research Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Neurosciences
                musical rhythm,neural entrainment,groove,complexity,timing
                Neurosciences
                musical rhythm, neural entrainment, groove, complexity, timing

                Comments

                Comment on this article