Abstract
In fast-paced, dynamic tasks, the ability to anticipate the future outcome of a sequence of events is crucial to quickly selecting an appropriate course of action among multiple alternative options. There are two classes of theories that describe how anticipation occurs. Serial theories assume options are generated and evaluated one at a time, in order of quality, whereas parallel theories assume simultaneous generation and evaluation. The present research examined the option evaluation process during a task designed to be analogous to prior anticipation tasks, but within the domain of narrative text comprehension. Prior research has relied on indirect, off-line measurement of the option evaluation process during anticipation tasks. Because the movement of the hand can provide a window into underlying cognitive processes, online metrics such as continuous mouse tracking provide more fine-grained measurements of cognitive processing as it occurs in real time. In this study, participants listened to three-sentence stories and predicted the protagonists’ final action by moving a mouse toward one of three possible options. Each story was presented with either one (control condition) or two (distractor condition) plausible ending options. Results seem most consistent with a parallel option evaluation process because initial mouse trajectories deviated further from the best option in the distractor condition compared to the control condition. It is difficult to completely rule out all possible serial processing accounts, although the results do place constraints on the time frame in which a serial processing explanation must operate.
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Notes
The deviation of initial trajectories was analyzed with location of the best option (top of the screen, bottom left, or bottom right) included as a factor, and the results revealed no main effect of option location, F(2,61) = 0.78, p = 0.459, η 2p = 0.01, nor an interaction with plausibility condition, F(2,61) = 1.34, p = 0.266, η 2p = 0.02. Within distractor trials, the direction of the competitor relative to the perceived best option (clockwise or counterclockwise) did not affect the deviation from perceived best option, t(62) = 0.72, p = 0.475, d = 0.03.
Interestingly, there appeared to be some individual differences in the use of this reaction strategy with 11 participants making a move toward the middle on more than one-third of the trials in the distractor condition, 17 participants never moving toward the middle, and the remaining 35 participants using this strategy at least once but on less than on third of the trials. Furthermore, there was a significant correlation between proportion of use of this strategy on the distractor cohesive stories and the distractor non-cohesive stories, r = 0.76, p < 0.001.
If all deviations of trajectories were coded as positive values, then any effect seen would be due to a push away from the best option, but would not necessarily show the effect of the plausible distractor on the best option.
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Acknowledgements
Special thanks to Maranda Duncan and Adrian Lewis for helping to create stimuli, Chris Austin for helping program the experiment to capture mouse movements, and Evan Cranford for helping design the task background and scoring algorithm.
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This research involved human participants, and informed consent was obtained. All procedures performed in studies involving human participants were in accordance with the ethical standards of the Mississippi State University Institutional Review Board for the Protection of Human Subjects in Research and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Handling editor: Martin V. Butz (University of Tübingen).
Reviewers: Robert Wirth (University of Würzburg), Thomas Faulkenberry (Tarleton State University), Joost Rommers (Radboud University).
Appendices
Appendix 1: Stimulus materials
Table 2 lists the stories used in the experiment, the possible options presented to the participants, and the duration (in seconds) of the recordings.
Appendix 2: Norming study
Participants in the norming study were 72 undergraduates from Mississippi State University who completed the experiment for course credit. The experiment was presented as an online survey. Participants read 59 cohesive stories and 30 non-cohesive stories that were created for the experiment. For each story, participants read the three sentences and were then presented with 11 possible endings (three good endings, four possible endings, and four unrelated endings). Participants were asked to (1) select one of the endings that would best complete the story and (2) select three more endings from the same list that could possibly complete the story. The two endings that were selected least often as possible endings, and never chosen as a best ending, were used as unrelated options in the present experiment. The ending that was selected most often as the best ending was used as the best option. Finally, the ending that was selected the most as a possible, but non-best ending was used as the plausible distractor.
Of the 59 cohesive stories, 35 were selected for the experiment. Stories were included if they met the following criteria: (1) If one option was selected as the best option by the majority of the participants (mean proportion who chose best option as the best = 69.8%), (2) if one option was selected as a possible alternative option a majority of the time (mean proportion who chose plausible option as a possible alternative option = 64.0%) and rarely selected as the best option (mean proportion who chose plausible option as a best option = 4.6%), and (3) if the best and possible endings both shared context with each sentence of the story. Therefore, if participants naturally generated options when reading the stories, then the two plausible options provided would likely be included in the set. Five stories from this final set of 35 were randomly selected for practice trials. All of the non-cohesive stories were used. Each of the non-cohesive stories had a unique best ending, a plausible distractor, and two unrelated endings.
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Cranford, E.A., Moss, J. Mouse-tracking evidence for parallel anticipatory option evaluation. Cogn Process 19, 327–350 (2018). https://doi.org/10.1007/s10339-017-0851-4
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DOI: https://doi.org/10.1007/s10339-017-0851-4