By Ritika Miryala
The purpose of this study was to investigate the relevance of neural circuits in regulating physical behaviors. The human brain is an intricate network that constantly forms new neural connections which encode various physical behaviors. The more frequently a physical behavior is performed, the stronger its corresponding neural connection becomes. As a result, the operational definition of neural connection was applied to hypothesize that individuals with more experience in a physical activity have stronger neural connections for the corresponding physical behaviors. To investigate this hypothesis, an experiment was designed at Kuchipudi Dance Academy (KDA), a dance school in Germantown, Maryland, where a group of eight dancers were required to learn and perform a newly choreographed dance within 20 minutes. The dance they learned embodied both conventional and unconventional dance movements, which allowed the study of the strength of both pre-existing and newly formed neural connections. The percent accuracy of the steps for each dancer was calculated using video recordings of their final performances, which determined how easily the dancer encoded and performed the physical behaviors and related it to the strength of the behaviors’ neural connections. The results of this investigation reinforce the psychological concept that repetition of a behavior strengthens corresponding neural connections.
The human brain is composed of thousands of neurons that form intricate neural connections, which define the very structure and function of the brain and regulate numerous physical behaviors. A neural connection is generated when the body is exposed to a stimulus and reaches the stimulus threshold (Stegemöller, E., L.). At that point, an electrical message is transmitted from sensory neurons to motor neurons to elicit the physical response.
Each neuron is composed of three distinct parts: the cell body, the dendrite and the axon. The dendrite receives the electrical message via neurotransmitters. If a threshold is reached, an action potential/ electrical impulse is transmitted down the axon, which forms the body of the neuron. Once the electrical message reaches the axon terminal, it is converted into a chemical message (in the form of neurotransmitters) that travels across the synaptic gap to reach the dendrite of the adjacent neuron (“Brain Basics”). Through this process, neurons transmit electrical messages from sensory neurons to motor neurons, which elicit the intended physical behavioral response (Boyd, Jeffrey H.). Below is a diagram of a neuron for reference.
Figure 1: Diagram of a Neuron
If a stimulus is repeatedly triggered, the neural connections that transmit its electrical impulse grow stronger as the synaptic gap between adjacent neurons closes. This allows the response behavior to be performed quickly and efficiently. In the same sense, if a response behavior is repeatedly performed, the neural connections strengthen as neurons encode for the learning of the behavior (Stollings, N.). As a result, experience and practice in physical activities can enhance the strength of corresponding neural connections and improve one’s ability to perform the associated physical behaviors efficiently. This process is known as long term potentiation as the brain encodes the behaviors into long term memory through its neural circuits.
This concept was investigated in conjunction with the physical movements involved in dance. Dance like any other physical activity encompasses complex steps and behaviors that can be mastered through years of experience. Through their years, dancers form numerous neural connections that encode for the physical steps involved in dance (Hanna, J. L.). This is all possible due to neuroplasticity, the brain’s exceptional ability to build new neural connections and develop intricate neural circuits (Sabaawi, M.). Therefore, individuals with many years of experience in dance tend to have hardwired brains for dance and stable neural circuits, enabling them to easily execute the steps they commonly perform in dance. However, they may face difficulty executing new steps as they must generate new neural connections for new behaviors, and possibly override strong pre-existing neural connections if the behaviors overlap. In essence, this article investigates these concepts through an experiment designed to assess the strength of neural connections in the physical behaviors involved in the dance of Kuchipudi.
An experiment was organized at Kuchipudi Dance Academy, where the effect of neuroplasticity and neural connections on the physical behavior of dancers was studied. The research encompasses a new approach to studying the human mind and neural functions through the arts. Dr. Monica Lopez- Gonzalez, a professor at Johns Hopkins University, takes on a similar approach of study in her company La Petite Noiseuse, where she investigates the mental processes, including the interaction between the subconscious and conscious mind, in staged performances. Her interview has inspired me to take an observational approach into investigating the involvement of neural connections on the physical movements part of classical Indian dance (M. Lopez- Gonzalez, personal communication, July 5, 2017). Such an approach allows me to discover the potential relationship between the dancers’ behaviors and corresponding neural connections when they are in a natural state, eliminating the effect a laboratory or fMRI’s could have on the dancers’ behaviors.
In this observational investigation, a 4 minute 14 second classical Indian dance was choreographed, and it embodied both conventional and unconventional Kuchipudi movements. The dance itself was technique oriented and did not include expressive portions, which allowed physical behavior to be isolated from emotional behavior in order to be studied exclusively. The tillana music for this dance was edited to include an amplified drum beat, thus creating a defined auditory stimulus.
The dance itself included 52 conventional steps and 42 unconventional steps, with a total of 94 steps. Conventional steps were defined as physical movements that existed in at least 10 Kuchipudi dances for which the dancers were hypothesized to have strong neural connections. The dancers were frequently exposed to these conventional steps, and therefore it was believed that the stimuli for these physical movements were frequently triggered; this indicates that the electrical signals for those stimuli were repeatedly generated and transmitted across the synaptic junctions of interconnected neurons. Over time, the multiple action potentials would be expected to reduce the synaptic gap between two neurons, allowing the stimulus message to be sent quickly and the response behavior (conventional step) to be executed within a reduced time frame, eventually becoming automatic. In contrast, unconventional steps were defined as relatively new dance movements which were either performed in only 1-3 Kuchipudi dances or were executed differently than normal. For these steps, it was hypothesized that dancers would have weaker neural connections if not none as the stimuli for these movements are rarely triggered; as a result, the neural connections for the behaviors are relatively very weak if not nonexistent due to the low frequency of action potential generation and the greater gap between synaptic junctions. This means that these unconventional behaviors must be encoded through traditional memory processing, which involves retrieving stimuli, and then consolidating and encoding it.
As for the experiment itself, there were a total of eight volunteers with varying levels of experience in dance, ranging from 1-12 years (specified in Figure 2 below). The experiment was conducted over the course of 4 days from July 19 to July 22 with two volunteers tested each day. Prior to the experiment, it was hypothesized that individuals with more experience will perform the newly choreographed dance more accurately because they have the strongest pre existing neural connections for the conventional steps and have had more experience forming new neural connections for unconventional steps as they have learned new dances before.
Figure 2: Volunteers who Participated in Experiment
For the experiment, I created a video of myself performing the dance I choreographed with the help of my dance teacher. The video was stored on a phone, and for each trial, the dancer was taken to the back room of the KDA dance studio. Then, the dancer was given the video of the dance on the phone and was told to learn as much of the dance as they possibly could in the 20 minute timeframe. The dancer was also instructed to learn the dance as they would any other dance by watching the video (dancers in KDA have learned dances by watching videos before). While the dancers were learning the dance, they were given time checks at 15 min., 10 min., and 5 min., and the number of times the dancer replayed the video were measured and recorded. After the 20 minutes, the video was taken away, and the dancer was required to perform as much of the dance as they remembered with just the audio music; they were given two trials to perform the dance with just the music. During this process, the dancer encoded what she learned during the 20 minutes into short term memory. After the two performances, the dancer was asked several questions listed below to distract them and allow their brains to consolidate their memory of the dance from their short term into their long term memory. These were the questions:
- How frequently do you practice dance?
- What other activities are you involved in?
- What school do you attend?
- Have you traveled to another country before?
After the dancer was asked these questions, they were required to perform the dance for the third and final time without the video, using just the audio music. This time, the performance of the dancer was recorded on a different phone for comparative analysis. After all eight dancers participated in the experiment, their individual dance performances were rewatched. Then, the number of conventional steps and unconventional steps performed accurately by each dancer were counted and recorded, and both the percentage of accuracy (relative to how much of the dance the dancer performed) and total number of steps performed accurately were used to analyze the data.
The video recordings of each dancer’s performance were scrutinized to determine the number of conventional steps, unconventional steps and total (conventional + unconventional) steps they performed accurately. Accurately was defined as performing the step on the correct beat, correct side and correct movement. Then conclusions regarding their neural connections were drawn from the accuracy of their behaviors.
To determine the percent accuracy of the steps, the number of conventional steps they did accurately was divided by the total number of conventional steps in the part of the dance they got to. For example, Dancer A (who learned 55 seconds of the dance in 20 minutes) performed a total of 5 conventional steps correctly out of the 6 that existed in the 55 second timeframe; so her percent accuracy would be 56=83.3% This same process was repeated to determine the percent accuracy for unconventional and total steps for all of the dancers as summarized in Figure 3 below.
Figure 3: Quantitative Data Table summarizing all the data collected during data analysis for each dancer
Through analysis of the data, two relationships were discovered and graphed. The first relationship between the experience of the dancer vs. how accurately they performed is summarized in Figure 4.1 and Figure 4.2 below. In figure 4.1, the eight dancers were grouped into 1 of 4 groups based on their experience (in number of years) in dance. Then, the percent accuracy for the steps for each group was calculated using the following equation:
This same process was used to determine percent accuracy for all of the four groups in each of the three categories (conventional, unconventional, total). The experience (x values) and percent accuracy (y values) were then used to determine three correlation coefficient (r) values, one for each of the categories. Then, the data was graphically represented with the experience in years as the independent variable and percent accuracy as the dependen