The compatibility of theoretical frameworks with machine learning analyses in psychological research

People deeply value their social bonds with companion animals, yet routinely devalue other animals, considering them mere commodities to satisfy human interests and desires. Despite the inherently social and intergroup nature of these complexities, social psychology is long overdue in integrating human-animal relations in its theoretical frameworks. The present body of work brings together social psychological research advancing our understanding of: 1) the factors shaping our perceptions and thinking about animals as social groups, 2) the complexities involved in valuing (caring) and devaluing (exploiting) animals, and 3) the implications and importance of human-animal relations for human intergroup relations. In this article, we survey the diversity of research paradigms and theoretical frameworks developed within the intergroup relations literature that are relevant, perchance critical, to the study of human-animal relations. Furthermore, we highlight how understanding and rethinking human-animal relations will eventually lead to a more comprehensive understanding of many human intergroup phenomena.

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Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

Perspectives on Psychological Science ◽

10.1177/1745691617693393 ◽

2017 ◽

Vol 12 (6) ◽

pp. 1100-1122 ◽

Cited By ~ 315

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.

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

10.31234/osf.io/s72vu ◽

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.

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Psychology’s Stewardship of Gender/Sex

Perspectives on Psychological Science ◽

10.1177/17456916211018462 ◽

2021 ◽

Vol 16 (6) ◽

pp. 1105-1112

Author(s):

Zach C. Schudson

Keyword(s):

Psychological Research ◽

Social Judgments ◽

Theoretical Frameworks ◽

Institutional Power ◽

Self Perceptions ◽

New Methods ◽

Psychological Theories ◽

Intellectual Labor ◽

Socially Relevant ◽

And Behavior

Psychological theories of gender and/or sex (gender/sex) have the capacity to shape people’s self-perceptions, social judgments, and behaviors. The institutional power of psychology to affect cognition and behavior—not just to measure them—necessitates a serious consideration of our social responsibility to manage the products of our intellectual labor. Therefore, I propose that psychological research should be understood as stewardship of gender/sex (and socially relevant concepts in general). In this issue, four articles collectively serve as a demonstrative slice of the diversity of current directions in psychological research on gender/sex. I use these articles as springboards for articulating key elements of psychologists’ stewardship of gender/sex and strategies for improving our stewardship. First, I examine how psychology’s historical stewardship of gender/sex has yielded both new methods for self-understanding and harmful consequences for marginalized people. Next, I explore promising current approaches that center minoritized perspectives. I also discuss roadblocks to effective stewardship, including narrowly disciplinary approaches. Finally, I consider strategies for improving psychology’s stewardship of gender/sex, such as mitigating gender/sex essentialism and employing generative theoretical frameworks built from interdisciplinary insights.

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Machine Learning Approaches for Psychological Research Review

Data Analytics and Management - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-981-15-8335-3_69 ◽

2021 ◽

pp. 911-921

Author(s):

Marta R. Jabłońska ◽

Zdzisław Pólkowski

Keyword(s):

Machine Learning ◽

Psychological Research ◽

Research Review ◽

Learning Approaches

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Hazard Perception–Response: A Theoretical Framework to Explain Drivers’ Interactions with Roadway Hazards

Safety ◽

10.3390/safety7020029 ◽

2021 ◽

Vol 7 (2) ◽

pp. 29

Author(s):

Daniela Barragan ◽

Matthew S. Peterson ◽

Yi-Ching Lee

Keyword(s):

Psychological Research ◽

Theoretical Framework ◽

Driving Behavior ◽

Crash Risk ◽

Fundamental Aspect ◽

Future Research ◽

Limited Evidence ◽

Theoretical Frameworks ◽

Novice Drivers ◽

Related Research

Research suggests that novice drivers are most susceptible to errors when detecting and responding to hazards. If this were true, then hazard training should be effective in improving novice drivers’ performance. However, there is limited evidence to support this effectiveness. Much of this research has overlooked a fundamental aspect of psychological research: theory. Although four theoretical frameworks were developed to explain this process, none have been validated. We proposed a theoretical framework to more accurately explain drivers’ behavior when interacting with hazardous situations. This framework is novel in that it leverages support from visual attention and driving behavior research. Hazard-related constructs are defined and suitable metrics to evaluate the stages in hazard processing are suggested. Additionally, individual differences which affect hazard-related skills are also discussed. This new theoretical framework may explain why the conflicts in current hazard-related research fail to provide evidence that training such behaviors reduces crash risk. Future research is necessary to empirically test this framework.

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Machine Learning and Psychological Research: The Unexplored Effect of Measurement

10.31234/osf.io/pvhsc ◽

2020 ◽

Cited By ~ 1

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.

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A review of machine learning applications in human resource management

International Journal of Productivity and Performance Management ◽

10.1108/ijppm-08-2020-0427 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Swati Garg ◽

Shuchi Sinha ◽

Arpan Kumar Kar ◽

Mauricio Mani

Keyword(s):

Machine Learning ◽

Resource Management ◽

Human Resource Management ◽

Performance Management ◽

Human Resource ◽

Systematic Approach ◽

Early Stage ◽

Theoretical Frameworks ◽

Content Type ◽

And Performance

PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

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

10.31234/osf.io/3j58q ◽

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.

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