Matching Items (5)
Description
Science is a formalized method for acquiring information about the world. In
recent years, the ability of science to do so has been scrutinized. Attempts to reproduce
findings in diverse fields demonstrate that many results are unreliable and do not
generalize across contexts. In response to these concerns, many proposals for reform have
emerged. Although promising, such reforms have not addressed all aspects of scientific
practice. In the social sciences, two such aspects are the diversity of study participants
and incentive structures. Most efforts to improve scientific practice focus on replicability,
but sidestep issues of generalizability. And while researchers have speculated about the
effects of incentive structures, there is little systematic study of these hypotheses. This
dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural
study of social discounting – the purportedly fundamental human tendency to sacrifice
more for socially-close individuals – conducted among three diverse populations (U.S.,
rural Indonesia, rural Bangladesh). This study finds no independent effect of social
distance on generosity among Indonesian and Bangladeshi participants, providing
evidence against the hypothesis that social discounting is universal. It also illustrates the
importance of studying diverse human populations for developing generalizable theories
of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test
the effect of incentive structures on research accuracy, in an instantiation of the scientific
process where the key decision is how much data to collect before submitting one’s
findings. The results demonstrate that rewarding novel findings causes respondents to
make guesses with less information, thereby reducing their accuracy. Chapter 3 presents
an evolutionary agent-based model that tests the effect of competition for novel findings
on the sample size of studies that researchers conduct. This model demonstrates that
competition for novelty causes the cultural evolution of research with smaller sample
sizes and lower statistical power. However, increasing the startup costs to conducting
single studies can reduce the negative effects of competition, as can rewarding
publication of secondary findings. These combined chapters provide evidence that
aspects of current scientific practice may be detrimental to the reliability and
generalizability of research and point to potential solutions.
recent years, the ability of science to do so has been scrutinized. Attempts to reproduce
findings in diverse fields demonstrate that many results are unreliable and do not
generalize across contexts. In response to these concerns, many proposals for reform have
emerged. Although promising, such reforms have not addressed all aspects of scientific
practice. In the social sciences, two such aspects are the diversity of study participants
and incentive structures. Most efforts to improve scientific practice focus on replicability,
but sidestep issues of generalizability. And while researchers have speculated about the
effects of incentive structures, there is little systematic study of these hypotheses. This
dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural
study of social discounting – the purportedly fundamental human tendency to sacrifice
more for socially-close individuals – conducted among three diverse populations (U.S.,
rural Indonesia, rural Bangladesh). This study finds no independent effect of social
distance on generosity among Indonesian and Bangladeshi participants, providing
evidence against the hypothesis that social discounting is universal. It also illustrates the
importance of studying diverse human populations for developing generalizable theories
of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test
the effect of incentive structures on research accuracy, in an instantiation of the scientific
process where the key decision is how much data to collect before submitting one’s
findings. The results demonstrate that rewarding novel findings causes respondents to
make guesses with less information, thereby reducing their accuracy. Chapter 3 presents
an evolutionary agent-based model that tests the effect of competition for novel findings
on the sample size of studies that researchers conduct. This model demonstrates that
competition for novelty causes the cultural evolution of research with smaller sample
sizes and lower statistical power. However, increasing the startup costs to conducting
single studies can reduce the negative effects of competition, as can rewarding
publication of secondary findings. These combined chapters provide evidence that
aspects of current scientific practice may be detrimental to the reliability and
generalizability of research and point to potential solutions.
ContributorsTiokhin, Leonid (Author) / Hruschka, Daniel J (Thesis advisor) / Morgan, Thomas JH (Thesis advisor) / Boyd, Robert (Committee member) / Frankenhuis, Willem E. (Committee member) / Bergstrom, Carl T. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In competitive Taekwondo, Electronic Body Protectors (EBPs) are used to register hits made by players during sparring. EBPs are comprised of three main components: chest guard, foot sock, and headgear. This equipment interacts with each other through the use of magnets, electric sensors, transmitters, and a receiver. The receiver is connected to a computer programmed with software to process signals from the transmitter and determine whether or not a competitor scored a point. The current design of EBPs, however, have numerous shortcomings, including sensing false positives, failing to register hits, costing too much, and relying on human judgment. This thesis will thoroughly delineate the operation of the current EBPs used and discuss research performed in order to eliminate these weaknesses.
ContributorsSpell, Valerie Anne (Author) / Kozicki, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
ContributorsRangaswami, Sriram Madhav (Author) / Lalitha, Sankar (Thesis director) / Jayasuriya, Suren (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Modern audio datasets and machine learning software tools have given researchers a deep understanding into Music Information Retrieval (MIR) applications. In this paper, we investigate the accuracy and viability of using a machine learning based approach to perform music genre recognition using the Free Music Archive (FMA) dataset. We compare the classification accuracy of popular machine learning models, implement various tuning techniques including principal components analysis (PCA), as well as provide an analysis of the effect of feature space noise on classification accuracy.
ContributorsKhondoker, Farib (Co-author) / Wildenstein, Diego (Co-author) / Spanias, Andreas (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05