Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

textAlthough Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.

/pdf/running-experiments-on-amazon-mechanical-turk-2e3wkd1q12.pdf

Running experiments on Amazon Mechanical Turk

Although Mechanical Turk has recently become popular among social scientists as a source of experimental data, doubts may linger about the quality of data provided by subjects recruited from online labor markets. We address these potential concerns by presenting new demographic data about the Mechanical Turk subject population, reviewing the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and comparing the magnitude of effects obtained using Mechanical Turk and traditional subject pools. We further discuss some additional benefits such as the possibility of longitudinal, cross cultural and prescreening designs, and offer some advice on how to best manage a common subject pool.

/pdf/running-experiments-on-amazon-mechanical-turk-4ig2eb5kwq.pdf

A morphable model for the synthesis of 3D faces

Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd.

/pdf/data-collection-in-a-flat-world-the-strengths-and-weaknesses-18eu8fjf8f.pdf

Data collection in a flat world: the strengths and weaknesses of mechanical turk samples

Participants are not always as diligent in reading and following instructions as experimenters would like them to be. When participants fail to follow instructions, this increases noise and decreases the validity of their data. This paper presents and validates a new tool for detecting participants who are not following instructions – the Instructional manipulation check (IMC). We demonstrate how the inclusion of an IMC can increase statistical power and reliability of a dataset.

Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power

Instructional manipulation checks: Detecting satisficing to increase statistical power

http://www.math.pitt.edu/~troy/glass/spiral_biophysics.pdf

Spiral Waves in Two-Dimensional Models of Ventricular Muscle: Formation of a Stationary Core

We present a new methodology for constructing face stimuli for psychological experiments based on silhouetted face profiles. Face silhouettes carry a number of theoretical and methodological advantages compared to more complex face stimuli and lend themselves to a simple yet powerful parameterization. In five behavioral studies, we show that face silhouettes are processed like regular face stimuli: They provide enough information for accurate gender judgments (Study 1), age estimations (Study 2), and reliable and cross-valid attractiveness ratings (Study 3). Furthermore, face silhouettes elicit an inversion effect (Study 4) and allow for remarkably accurate cross-identification with front-view photographs (Study 5). We then describe a shape-based parameterization that relies on a small set of landmark points and show that face silhouettes can be effectively represented in a 20-dimensional ‘‘silhouette face space’’ (Study 6). We show that in this physical space, distance from the center of the space corresponds to perceived distinctiveness (Study 7), confirming a key axiom in the formulation of the face space model. Finally, we discuss straightforward applications of the face silhouette methodology and address some limitations.

http://vpnl.stanford.edu/papers/Davidenko-2007-jov-7-4-6.pdf

Nicolas Davidenko

Papers

Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power

Instructional manipulation checks: Detecting satisficing to increase statistical power

Spiral Waves in Two-Dimensional Models of Ventricular Muscle: Formation of a Stationary Core

Silhouetted face profiles: a new methodology for face perception research.

Gender aftereffects in the perception of silhouetted face profiles