scholarly journals Audio-Tokens: a toolbox for rating, sorting and comparing audio samples in the browser.

2021 ◽  
Author(s):  
Peter Donhauser ◽  
Denise Klein
Keyword(s):  
Machine Learning ◽  
Music Perception ◽  
Psychological Research ◽  
Online Rating ◽  
Similarity Ratings

Here we describe a Javascript toolbox to perform online rating studies with auditory material. The main feature of the toolbox is that audio samples are associated with visual tokens on the screen that control audio playback and can be manipulated depending on the type of rating. This allows the collection of single- and multi-dimensional feature ratings, as well as categorical and similarity ratings. The toolbox (github.com/pwdonh/audio_tokens) can be used via a plugin for the widely-used jsPsych, as well as using plain Javascript for custom applications. We expect the toolbox to be useful in psychological research on speech and music perception, as well as for the curation and annotation of datasets in machine learning.

scholarly journals Machine Learning in Psychometrics and Psychological Research

2020 ◽  
Vol 10 ◽  
Author(s):  
Graziella Orrù ◽  
Merylin Monaro ◽  
Ciro Conversano ◽  
Angelo Gemignani ◽  
Giuseppe Sartori
Keyword(s):  
Machine Learning ◽  
Psychological Research

scholarly journals Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

2017 ◽  
Vol 12 (6) ◽  
pp. 1100-1122 ◽  
Author(s):  
Tal Yarkoni ◽  
Jacob Westfall
Keyword(s):  
Machine Learning ◽  
Gold Standard ◽  
Psychological Research ◽  
Controlled Experiments ◽  
Moderating Variables ◽  
Research Programs ◽  
Causal Mechanisms ◽  
Psychological Mechanism ◽  
Research Questions ◽  
Predictive Research

Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.

scholarly journals Supervised machine learning methods in psychology: A practical introduction with annotated R code

2019 ◽  
Author(s):  
Hannes Rosenbusch ◽  
Felix Soldner ◽  
Anthony M Evans ◽  
Marcel Zeelenberg
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Psychological Research ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Comprehensive Overview ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Out Of Sample

Machine learning methods for pattern detection and prediction are increasingly prevalent in psychological research. We provide a comprehensive overview of machine learning, its applications, and how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize four standard prediction algorithms: linear regressions, ridge regressions, decision trees, and random forests (plus k-nearest neighbors, Naïve Bayes classifiers, and support vector machines in the supplementary material). This selection provides a set of powerful models that are implemented regularly in machine learning projects. We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science.

scholarly journals Machine Learning and Psychological Research: The Unexplored Effect of Measurement

2020 ◽  
Author(s):  
Ross Jacobucci ◽  
Kevin Grimm
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithm ◽  
Psychological Research ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Statistical Tools ◽  
Measurement Quality ◽  
Psychological Science

Machine learning (i.e., data mining, artificial intelligence, big data) has seen an increase in application in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We offer an explanation for this phenomena: namely that poor measurement prevents machine learning algorithms from accurately modeling nonlinear relationships, if they exist. This is showcased across a set of simulated examples, demonstrating that model selection between a machine learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.

scholarly journals Machine Learning and Psychological Research: The Unexplored Effect of Measurement

2020 ◽  
Vol 15 (3) ◽  
pp. 809-816 ◽  
Author(s):  
Ross Jacobucci ◽  
Kevin J. Grimm
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Measurement Errors ◽  
Learning Algorithm ◽  
Psychological Research ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Statistical Tools ◽  
Measurement Quality ◽  
Psychological Science

Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We argue that this phenomenon results from measurement errors that prevent machine-learning algorithms from accurately modeling nonlinear relationships, if indeed they exist. This shortcoming is showcased across a set of simulated examples, demonstrating that model selection between a machine-learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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