Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon's Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences.

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Prosthetic technology is a prime candidate for the integration of haptic feedback.Conventional myoelectric prostheses do not have a mechanism to convey any sensory information, making it difficult for users to feel connected to their hand and to engage in active grasping and exploration tasks. Vibrotactile stimulation is a simple and safe choice for a noninvasive haptic display that can be easily integrated into current hardware. A force-matching grasping task is used to quantify performance improvements at three different force levels with a pulsing vibrotactile feedback channel to convey grasping force. Results show that the haptic feedback led to improved performance in an experienced subgroup of subjects while naive subjects showed no improvement. These preliminary findings suggest that users experienced in EMG control may be able to improve their control of grasping capabilities with a vibrotactile representation of grip force.

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people's mental representations of these two categories.