scholarly journals The Unbearable Lightness of Finger Movements: Commentary to Doliński

2018 ◽  
Vol 13 (2) ◽  
pp. e26110 ◽  
Author(s):  
Karl Halvor Teigen
Keyword(s):  
Social Psychology ◽  
Rating Scales ◽  
Empirical Studies ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Ethical Review ◽  
Questionnaire Data ◽  
Target Article ◽  
Self Reports ◽  
Cognitive Revolution

In the target article, Doliński (2018, this issue) showed that empirical studies of “real” behaviour are an almost extinct species of research, judged from articles published in the most recent volume of JPSP (Journal of Personality and Social Psychology). This finding continues a trend identified by Baumeister and colleagues ten years ago. The reliance on self-reports and rating scales can hardly be explained as an aftermath of the cognitive revolution in psychology, or a preoccupation with measurements and advanced statistical analyses, as Doliński suggests, but is more compatible with the ease of collecting questionnaire data, combined with the pressure to publish large multi-study papers and to obtain approval from ethical review boards. This development is further strengthened by the accessibility of online participant pools. An informal count showed that students participating for course credit were in 2006 involved more than 90% of empirical JPSP studies, as against 22.5% in 2017. In contrast, Amazon Mechanical Turk workers, non-existent in 2006, participated in 55.3% of the empirical studies published in the most recent volume. Parallel to this development the number of participants per study and the number of studies per article have vastly increased.

Advancing empirical resilience research

2015 ◽  
Vol 38 ◽  
Author(s):  
Raffael Kalisch ◽  
Marianne B. Müller ◽  
Oliver Tüscher
Keyword(s):  
Social Psychology ◽  
Quantitative Study ◽  
Empirical Studies ◽  
Research Strategy ◽  
Research Community ◽  
Collaborative Effort ◽  
Major Point ◽  
Target Article ◽  
The Many ◽  
Definition Of

AbstractWe are delighted by the broad, intense, and fruitful discussion in reaction to our target article. A major point we take from the many comments is a prevailing feeling in the research community that we need significantly and urgently to advance resilience research, both by sharpening concepts and theories and by conducting empirical studies at a much larger scale and with a much more extended and sophisticated methodological arsenal than is the case currently. This advancement can be achieved only in a concerted international collaborative effort. In our response, we try to argue that an explicitly atheoretical, purely observational definition of resilience and a transdiagnostic, quantitative study framework can provide a suitable basis for empirically testing different competing resilience theories (sects. R1, R2, R6, R7). We are confident that it should be possible to unite resilience researchers from different schools, including from sociology and social psychology, behind such a pragmatic and theoretically neutral research strategy. In sections R3 to R5, we further specify and explain the positive appraisal style theory of resilience (PASTOR). We defend PASTOR as a comparatively parsimonious and translational theory that makes sufficiently concrete predictions to be evaluated empirically.

scholarly journals Evaluating extinction, renewal, and resurgence of operant behavior in humans with Amazon Mechanical Turk

2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik
Keyword(s):  
Operant Behavior ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk

scholarly journals Amazon Mechanical Turk: Gold Mine or Coal Mine?

2011 ◽  
Vol 37 (2) ◽  
pp. 413-420 ◽  
Author(s):  
Karën Fort ◽  
Gilles Adda ◽  
K. Bretonnel Cohen
Keyword(s):  
Coal Mine ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Gold Mine

scholarly journals Developing and validating an isotrigon texture discrimination task using Amazon Mechanical Turk

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
John WG Seamons ◽  
Marconi S Barbosa ◽  
Jonathan D Victor ◽  
Dominique Coy ◽  
Ted Maddess
Keyword(s):  
Discrimination Task ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Texture Discrimination

The Longitudinal Israeli Study of Twins (LIST)—An Integrative View of Social Development

2012 ◽  
Vol 16 (1) ◽  
pp. 197-201 ◽  
Author(s):  
Reut Avinun ◽  
Ariel Knafo
Keyword(s):  
Social Behavior ◽  
Parenting Styles ◽  
Molecular Genetic ◽  
Developmental Study ◽  
Questionnaire Data ◽  
Parental Questionnaire ◽  
Integrative View ◽  
Self Reports ◽  
Pro Social Behavior ◽  
Advance Knowledge

The Longitudinal Israeli Study of Twins (LIST) is a social developmental study, which implements social-developmental, molecular genetic, epigenetic, and behavioral genetic methods to advance knowledge on the development of individual differences in social behavior. Twins are followed from the age of three and both observational and parental-questionnaire data are collected on their empathy, temperament, and pro-social behavior. The parenting styles of parents are also evaluated using self-reports and observations and DNA samples are collected from parents and twins. In the current paper, we provide a review of our recent work and discuss the future aims of the LIST.

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

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