Summary:
-The Nucleus accumbens lights up when a good song is heard. This article provides a predictive model based on certain parts of the brain lighting up that can lead to identifying 80% of songs that are non-hits. This can be valuable information to a business to predict the things that aren't going to work.
Key lines:
"Neuroeconomic research suggests that activity in reward-related regions of the brain, notably the orbitofrontal cortex and ventral striatum 1-4 , is predictive of future purchasing decisions"
"This indicates that simple subjective reports of focus groups may not be good predictors of commercial success."
"Although subjective ratings of songs did not correlate with future sales, the activation within the NACC did (Fig. 2b)."
"With thresholds in the range of 15,000 to 35,000 units sold, the logistic model achieved reasonable accuracy in correctly classifying hits and non-hits. For example, with a hit-threshold of 15,000 units, the logistic model correctly classified 80% of the non-hits; however, this came at a cost of missing true hits (but still correctly classified 30% of the hits). "
Key Questions:
The Experiment:
popularity of songs rated for likability. (self-reporting augmented w/ fMRI studies!)
Results:
To test whether the musical tastes of our cohort were representative of the population, we compared our cohort’s pre-scan genre rankings to the 2009 Nielsen sales by category and found a significant correlation (Kendall’s τ=-0.733, p=0.0556; assuming that our hip-hop category is equivalent to Nielsen’s R&B category), showing that our cohort was not significantly different than the national population).
Sample Size: n= 32
Recruited how?
Issues with the study?
-Sample Size
-the claim
-The predictive model
Problems we need to solve?
1. The simplicity of the study: We have many more variables or how do we parse down to the most important factors?
How can we use this for cerebrum?
1. Creating a one-year study where a group of high school students are asked where they going to college and fMRI studies are done looking at the orbitofrontal cortex, cuneus, nucleus accumbens/ventral striatum. After the talk about the list of schools that they have been accepted to, then we measure the activity, seeing the predictive behavior of the schools they go to.
2. Get grant-funding to do this kind of research, big dollars.
3. Using studies and a predictive statistic model, we can tell people which campaigns to use and which not to use. (NOTE: that's too many moving variables.)
Citation:
Berns, G. S., & Moore, S. E. (2012). A neural predictor of cultural popularity. Journal of Consumer Psychology, 22(1), 154-160. doi:10.1016/j.jcps.2011.05.001
-The Nucleus accumbens lights up when a good song is heard. This article provides a predictive model based on certain parts of the brain lighting up that can lead to identifying 80% of songs that are non-hits. This can be valuable information to a business to predict the things that aren't going to work.
Key lines:
"Neuroeconomic research suggests that activity in reward-related regions of the brain, notably the orbitofrontal cortex and ventral striatum 1-4 , is predictive of future purchasing decisions"
"This indicates that simple subjective reports of focus groups may not be good predictors of commercial success."
"Although subjective ratings of songs did not correlate with future sales, the activation within the NACC did (Fig. 2b)."
"With thresholds in the range of 15,000 to 35,000 units sold, the logistic model achieved reasonable accuracy in correctly classifying hits and non-hits. For example, with a hit-threshold of 15,000 units, the logistic model correctly classified 80% of the non-hits; however, this came at a cost of missing true hits (but still correctly classified 30% of the hits). "
Key Questions:
The Experiment:
popularity of songs rated for likability. (self-reporting augmented w/ fMRI studies!)
Results:
To test whether the musical tastes of our cohort were representative of the population, we compared our cohort’s pre-scan genre rankings to the 2009 Nielsen sales by category and found a significant correlation (Kendall’s τ=-0.733, p=0.0556; assuming that our hip-hop category is equivalent to Nielsen’s R&B category), showing that our cohort was not significantly different than the national population).
Sample Size: n= 32
Recruited how?
Issues with the study?
-Sample Size
-the claim
-The predictive model
Problems we need to solve?
1. The simplicity of the study: We have many more variables or how do we parse down to the most important factors?
How can we use this for cerebrum?
1. Creating a one-year study where a group of high school students are asked where they going to college and fMRI studies are done looking at the orbitofrontal cortex, cuneus, nucleus accumbens/ventral striatum. After the talk about the list of schools that they have been accepted to, then we measure the activity, seeing the predictive behavior of the schools they go to.
2. Get grant-funding to do this kind of research, big dollars.
3. Using studies and a predictive statistic model, we can tell people which campaigns to use and which not to use. (NOTE: that's too many moving variables.)
Citation:
Berns, G. S., & Moore, S. E. (2012). A neural predictor of cultural popularity. Journal of Consumer Psychology, 22(1), 154-160. doi:10.1016/j.jcps.2011.05.001