The value of narrative fiction as a vehicle for empathie growth is touted across diverse disciplines, but these ideas have rarely undergone empidcal scrutiny. The purpose of this study is to investigate whether enhancing imagery generation while reading fiction can potentiate empathy and prosocial behavior. Participants (N = 98) were randomly assigned to generate imagery across multiple sensory domains while reading (imagery-generation condition), focus on the semantic meaning of words in the story (verbal-semantic condition), or read the story as they would for leisure (leisure-reading condition). Participants who generated higher levels of imagery were significantly more transported into the story and felt significantly higher empathy for the story's characters. Individuals in the imagery-generation condition were over 3 times more likely to exhibit prosocial behavior than individuals in the leisurereading condition.

The literature suggests students gain important skills when directly involved with faculty in research. However, students at smaller institutions are often faced with limited research opportunities and faculty members are faced with limited participant-pools, funding, and space to perform research. Amazon's Mechanical Turk (MTurk) may provide a solution to many of these problems. MTurk provides an online human participant-pool, along with tools to build experiments, and it allows data to be collected quickly and inexpensively. In this study of narrative fiction and empathy, data was collected using the traditional, laboratory-based approach, and on MTurk using identical measures and protocols. Results indicated MTurk data exhibits comparable reliability, gender and ethnicity composition to data collected in the laboratory. Two important differences emerged: MTurk participants were 10 years older, on average, and they demonstrated higher scores on trait measures of empathy and state measures of involvement into the story presented in the study. A brief user's guide to MTurk is presented that caters to first-time users. Finally, common pitfalls and their solutions are presented with the hope that faculty and students can begin doing research on MTurk immediately.

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