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"Behavioral Sciences - methods"
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Blockchain and crypto currency : building a high quality marketplace for crypto data
This open access book contributes to the creation of a cyber ecosystem supported by blockchain technology in which technology and people can coexist in harmony. Blockchains have shown that trusted records, or ledgers, of permanent data can be stored on the Internet in a decentralized manner. The decentralization of the recording process is expected to significantly economize the cost of transactions. Creating a ledger on data, a blockchain makes it possible to designate the owner of each piece of data, to trade data pieces, and to market them. This book examines the formation of markets for various types of data from the theory of market quality proposed and developed by M. Yano. Blockchains are expected to give data itself the status of a new production factor. Bringing ownership of data to the hands of data producers, blockchains can reduce the possibility of information leakage, enhance the sharing and use of IoT data, and prevent data monopoly and misuse. The industry will have a bright future as soon as better technology is developed and when a healthy infrastructure is created to support the blockchain market.
Book
A tutorial on open-source large language models for behavioral science
Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucial advantage for use in behavioral science. To help researchers harness the promise of LLMs, this tutorial offers a primer on the open-source Hugging Face ecosystem and demonstrates several applications that advance conceptual and empirical work in behavioral science, including feature extraction, fine-tuning of models for prediction, and generation of behavioral responses. Executable code is made available at
github.com/Zak-Hussain/LLM4BeSci.git
. Finally, the tutorial discusses challenges faced by research with (open-source) LLMs related to interpretability and safety and offers a perspective on future research at the intersection of language modeling and behavioral science.
Journal Article
Scaling up behavioral science interventions in online education
Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and completion rates in a handful of courses, but evidence of their effectiveness across diverse educational contexts is limited. In this study, we test a set of established interventions over 2.5 y, with one-quarter million students, from nearly every country, across 247 online courses offered by Harvard, the Massachusetts Institute of Technology, and Stanford. We hypothesized that the interventions would produce medium-to-large effects as in prior studies, but this is not supported by our results. Instead, using an iterative scientific process of cyclically preregistering new hypotheses in between waves of data collection, we identified individual, contextual, and temporal conditions under which the interventions benefit students. Self-regulation interventions raised student engagement in the first few weeks but not final completion rates. Value-relevance interventions raised completion rates in developing countries to close the global achievement gap, but only in courses with a global gap. We found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order-of-magnitude. However, iterative scientific investigations can uncover what works where for whom.
Journal Article
Megastudies improve the impact of applied behavioural science
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes
1
. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals
2
. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy—a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research
3
–
6
. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.
A massive field study whereby many different treatments are tested synchronously in one large sample using a common objectively measured outcome, termed a megastudy, was performed to examine the ability of interventions to increase gym attendance by American adults.
Journal Article
How Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project
The Big Five personality traits have been linked to dozens of life outcomes. However, metascientific research has raised questions about the replicability of behavioral science. The Life Outcomes of Personality Replication (LOOPR) Project was therefore conducted to estimate the replicability of the personality-outcome literature. Specifically, I conducted preregistered, high-powered (median N = 1,504) replications of 78 previously published trait–outcome associations. Overall, 87% of the replication attempts were statistically significant in the expected direction. The replication effects were typically 77% as strong as the corresponding original effects, which represents a significant decline in effect size. The replicability of individual effects was predicted by the effect size and design of the original study, as well as the sample size and statistical power of the replication. These results indicate that the personality-outcome literature provides a reasonably accurate map of trait–outcome associations but also that it stands to benefit from efforts to improve replicability.
Journal Article
B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors
Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.
The study of naturalistic behaviour using video tracking is challenging. Here the authors develop a system, B-SOiD which allows automated behavioural tracking and segmentation of video of movements tested in mice, flies and humans.
Journal Article
Why we need to abandon fixed cutoffs for goodness-of-fit indices: An extensive simulation and possible solutions
To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values (e.g., CFI > .950) derived from simulation studies. Methodologists have cautioned that cutoffs for GOFs are only valid for settings similar to the simulation scenarios from which cutoffs originated. Despite these warnings, fixed cutoffs for popular GOFs (i.e., χ
2
, χ
2
/
df
, CFI, RMSEA, SRMR) continue to be widely used in applied research. We (1) argue that the practice of using fixed cutoffs needs to be abandoned and (2) review time-honored and emerging alternatives to fixed cutoffs. We first present the most in-depth simulation study to date on the sensitivity of GOFs to model misspecification (i.e., misspecified factor dimensionality and unmodeled cross-loadings) and their susceptibility to further data and analysis characteristics (i.e., estimator, number of indicators, number and distribution of response options, loading magnitude, sample size, and factor correlation). We included all characteristics identified as influential in previous studies. Our simulation enabled us to replicate well-known influences on GOFs and establish hitherto unknown or underappreciated ones. In particular, the magnitude of the factor correlation turned out to moderate the effects of several characteristics on GOFs. Second, to address these problems, we discuss several strategies for assessing model fit that take the dependency of GOFs on the modeling context into account. We highlight tailored (or “dynamic”) cutoffs as a way forward. We provide convenient tables with scenario-specific cutoffs as well as regression formulae to predict cutoffs tailored to the empirical setting of interest.
Journal Article
Use caution when applying behavioural science to policy
Social and behavioural scientists have attempted to speak to the COVID-19 crisis. But is behavioural research on COVID-19 suitable for making policy decisions? We offer a taxonomy that lets our science advance in ‘evidence readiness levels’ to be suitable for policy. We caution practitioners to take extreme care translating our findings to applications.
Journal Article
The weirdest people in the world?
Behavioral scientists routinely publish broad claims about human psychology and behavior in the world's top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers – often implicitly – assume that either there is little variation across human populations, or that these “standard subjects” are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species – frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior – hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges.
Journal Article
How games can make behavioural science better
Wordle, Minecraft and Scrabble are played online by millions. Gamifying experiments can make behavioural research more inclusive, rigorous and reproducible — if it’s done right.
Wordle, Minecraft and Scrabble are played online by millions. Gamifying experiments can make behavioural research more inclusive, rigorous and reproducible — if it’s done right.
Journal Article