Embodied Cognition and Kinesthetic Motion Literature Review

Slow, fast, fluid – these adjectives can be applied to the facets of rhythm, tempo, and articulation of either movement or music. In fact, there is not much dispute that the auditory and vestibular systems are, in fact, linked. Human movement studies have been involved in everything from pedagogical approaches to memory and entrainment. This literature review addresses how physical body movements can be linked to music, touching upon embodied cognition, physical movement and motion capture technology, how movement to music affects beat perception, developmental studies about rhythmic performance, and substrates behind rhythm affection motor behavior. Not only are brain areas traditionally assumed to only be associated with performing kinesthetic actions now being linked to auditory beat perception, these neuroscience studies are being used alongside behavioral studies that show how body movements can help parse the metric structure of music (Toivianen 2010).

Leman (2008) focuses on the presence of goal-directed action in music perception, embodied cognition thus assuming interaction between an organism and its environment. Leman also mentions Hanslick’s theory of moving sonic forms; just as dance is an undefined structure of form relationships, so is music. An organism’s reaction to the moving sonic form of music is corporeal, providing support to embodied cognition being shaped by aspects of the body. Under the impression that movement can enhance listening, a study attempting to measure vestibular influence on auditory metrical interpretation (Phillips-Silver Phillips-Silver & Trainor, 2008) found that movement of the head, but not legs, affects meter perception. Drawing upon previous work that showed that body movement could help distinguish between metrically ambiguous rhythmic sound patterns, Phillips-Silver & Trainor (2005, 2007) were able to both isolate the vestibular system and test without any vestibular input with the end result of proving that the vestibular system and auditory information are indeed integrated in perception.

Ranging from spontaneous to deliberate body movements, dance is a form of corporeal interpretation of music that can be captured by various technological methods. Eerola et al. (2006) investigated the corporeal movement of toddlers to music using a high-resolution motion capture system. Toiviainen et al. (2010) applied kinetic analysis, body modeling, dimensionality reduction, and signal processing to data acquired by attaching reflectors to 28 joint markers on participants’ bodies. Eigenmovements, according to Alexandrov et al. (2001), are “movements along eigenvectors of the motion equation.”

A high-resolution motion capture system was used in the 2010 Toiviainen study to identify the most typical movement patterns, or eigenmovements, synchronized to different metrical, or beat, levels. PCs (principal components) are a reduced group of uncorrelated variables transformed into a large group of variables, the first five pertaining to the rotation of the upper torso, lateral swaying of the body, mediolateral arm movement, (four does not vary significantly) and vertical arm movement. The beat-level data can be summarized as follows: The one-beat level corresponded with mediolateral and vertical arm movements, the two-beat level with mediolateral arm movements and rotation of the upper torso, and the four-beat level with lateral swaying of the body and rotation of the upper torso. This observation was in line with their hypothesis that “faster metric levels are embodied in the extremities, and slower ones in the central parts of the body.” The torso’s significant mass, and thus kinetic energy, can be thought of in terms of the previously mentioned study’s focus on vestibular motion (in connection to the torso). Even a relatively dated study using motion capture like a virtual dance and music environment at UC Irvine (Beliaqua et al., 2001) used a data stream from placement of acceleration sensors on strategic body parts in order to transform motion into sound.

Mitchell et al. (2001) postulates that similar emotions generated from music and dance can be a means of matching them, accordingly suggesting that their simultaneous presentation might increase the chances of a match even with few similarities. The cross-modality mainly taken into account is emotion, presented as a representation of visual, auditory, or kinesthetic imagery that could potentially serve as a connector in memory between “temporally dissociated visual observations of a dance and auditory experience of the music that inspired it.” There may be a correlation of movement to ‘groove,’ as well, keeping in mind that some rhythms may only be inhibited by adding the additional stimuli of movement (Petr et al., 2011).

The auditory and dorsal premotor cortices were activated for longer tap times (louder tones) in a study (Zatorre et al., 2006). First hypothesized was that the more salient meters would most affect movement entrainment, also modulating brain regions driven by these auditory–motor interactions. Five parametric levels of metric saliency were created to test the hypothesis by increasing the contrast in sound intensity between accented and unaccented notes. Ultimately, the posterior STG and dPMC showed the most function connectivity in auditory-motor interactions. However, these findings can also be applied to neural components such as “mirror neurons,” due to the muscle memory enhanced by repetition of movement, for example by drummers reproducing the exact same sound at the same tempo.

In conclusion, a clear distinction needs to be made between what kind of movement is being integrated with auditory stimuli. Movements follow a hierarchical organization depending on the proximal/distal characteristic of the limb used (Peckel et al., 2014), and can even depend on loudness of tone as well. Music “has a pervasive tendency to rhythmically engage our body,” (Dalla Bella et al., 2013), but we still are not able to fully pin down the neural substrates involved, not only because the cross-modality of areas like the pre-motor cortex are involved in so many bodily functions. Current studies are focusing on modeling hierarchically organized temporal patterns induced by external rhythms. Questions to take away from this can include that if new temporal patterns are presented, do they have basis in past, known, patterns, and can movements be applied to this same exploration?



One thought on “Embodied Cognition and Kinesthetic Motion Literature Review

  1. This is a fascinating topic and it was certainly a lot of materials to chew on. Here are a few suggestions/questions:

    1. Note that you need a comma between the author information and the date in parenthetical references. Also, in-text references to multiple authors must use full words (e.g., “and colleagues”) rather than abbreviations such as “et al.” and the symbol “&”.

    2. As you go through the lit review, re-use the same category “labels” you introduced at the end of your introduction in the first sentence of the corresponding paragraph; this works best in a general informative statement about the concept/phenomenon, i.e., it is best to avoid starting a paragraph with and author-date reference (e.g., the first sentence of the third paragraph is better than that of the second or the fifth).

    3. In general you do a good job of defining terms used in specific studies. The materials related to the motio-capture studies could have been a bit more focused and syntactically clearer (e.g., last sentence of fourth paragraph). The text of the final section of the lit review could also have used a second reading.

    4. Did you touch on all the topics laid out in the introduction (e.g., developmental studies)? In that case, I think it was covered as part of another topic, in which case, you might not present it in the introduction as a category “label”, but mention it as an sub-area of relevance.

    5. Make sure to review the format for citations, and be consistent. This is a nice collection of references, though!

    I am not sure I understand the final question, but I am eager to see where your research will go from here!

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