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"Behavioral sciences"
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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
Behavioural genetics for education
\"Educational environments interact with children's unique genetic profiles, leading to wide individual differences in learning ability, motivation, and achievement in different academic subjects - even when children study with the same teacher, attend the same school and follow the same curriculum. This book considers how education can benefit from the recent progress in genetically informative research. The book provides new insights into the origins of individual differences in education traits such as cognitive abilities and disabilities; motivation and personality; behavioural and emotional problems; social functioning; well-being, and academic achievement. Written and edited by international interdisciplinary experts, this book will be of interest to teachers, parents, educational and developmental psychologists, policy makers and researchers in different fields working on educationally-relevant issues. \"-- Provided by publisher.
Book
Nudging and Boosting
In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions—as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions.
Journal Article
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
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
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
Demand Effects in Survey Experiments: An Empirical Assessment
Survey experiments are ubiquitous in social science. A frequent critique is that positive results in these studies stem from experimenter demand effects (EDEs)—bias that occurs when participants infer the purpose of an experiment and respond so as to help confirm a researcher’s hypothesis. We argue that online survey experiments have several features that make them robust to EDEs, and test for their presence in studies that involve over 12,000 participants and replicate five experimental designs touching on all empirical political science subfields. We randomly assign participants information about experimenter intent and show that providing this information does not alter the treatment effects in these experiments. Even financial incentives to respond in line with researcher expectations fail to consistently induce demand effects. Research participants exhibit a limited ability to adjust their behavior to align with researcher expectations, a finding with important implications for the design and interpretation of survey experiments.
Journal Article
Update on Memory Systems and Processes
Ideas about how the brain organizes learning and memory have been evolving in recent years, with potentially important ramifications. We review traditional thinking about learning and memory and consider more closely emerging trends from both human and animal research that could lead to profound shifts in how we understand the neural basis of memory.
Journal Article
The power of negative and positive episodic memories
The power of episodic memories is that they bring a past moment into the present, providing opportunities for us to recall details of the experiences, reframe or update the memory, and use the retrieved information to guide our decisions. In these regards, negative and positive memories can be especially powerful: Life’s highs and lows are disproportionately represented in memory, and when they are retrieved, they often impact our current mood and thoughts and influence various forms of behavior. Research rooted in neuroscience and cognitive psychology has historically focused on memory for negative emotional content. Yet the study of autobiographical memories has highlighted the importance of positive emotional memories, and more recently, cognitive neuroscience methods have begun to clarify why positive memories may show powerful relations to mental wellbeing. Here, we review the models that have been proposed to explain why emotional memories are long-lasting (durable) and likely to be retrieved (accessible), describing how in overlapping—but distinctly separable—ways, positive and negative memories can be easier to retrieve, and more likely to influence behavior. We end by identifying potential implications of this literature for broader topics related to mental wellbeing, education, and workplace environments.
Journal Article