Going Through the Motions: Evaluating the Impact of Task, Device and Platform on Mouse-Tracking Derived Measures of Decision Difficulty

• Author / Creator

  Ouellette Zuk, Alexandra A.

• As decisions require actions to have an effect on the world (Cisek & Kalaska, 2010), measures derived from movements can be used to provide a powerful index of decision-making processes (e.g., Gallivan & Chapman, 2014). Measures of trajectory curvature (interpreted as a competitive pull from the non-chosen choice; Spivey, Grosjean, & Knoblich, 2005), reaction time, and movement time obtained during mouse-tracked, reach-decision tasks thus provide a metric of the relative difficulty of decisions (McKinstry, Dale, & Spivey, 2008). While these measures of decision difficulty have been demonstrated across a variety of decision domains, they are reported in different studies with different groups of participants and are often captured using experimental systems both impractical and inaccessible outside of laboratory exploration. The current study therefore aimed to assess whether within-participant metrics of decision difficulty remain consistent across decision domains varying in choice stimuli, objectivity and processing requirement, data collection devices varying in size and user-interaction requirements (e.g., mouse-based interactions to touchscreen use) and implementation platforms requiring individualized data processing and cleaning strategies. Specifically, three primary questions were addressed: 1) How do measures of decision difficulty change across testing device: computers, tablets and smartphones? 2) How do measures of decision difficulty relate to each other and how does this change across decision domain and device? and 3) How does implementation platform effect measures of decision difficulty? Deploying a classic mouse-tracking, reach-decision paradigm, participants (N = 279) were asked to complete a numeric-size congruency (SC) task requiring objective perceptual judgements of which of two digits with different physical sizes was numerically larger (Faulkenberry et al., 2016), a sentence verification (SV) task requiring semi-subjective conceptual judgements about the truth value of statements varying in truth value and negation (Maldonado et al., 2019), and a photo preference (PP) task requiring a subjective judgement of preference between two images varying in pleasantness (Koop & Johnson, 2013). An identical experiment was developed for implementation using both Labvanced and Horizon testing platforms, with participation using the prior platform distributed between personal computer (N = 83), tablet (N = 78) and smartphone (N = 78) testing devices and the latter limited to personal computer use (N = 40). Participation occurred remotely, online, and without device specification requirements.
  Broadly, task-specific results replicated previous work: SC: We found an increase in decision difficulty when digit choice options were incongruent in physical and numeric size; SV: Measures of decision difficulty increased when participants were asked to affirm negated sentences compared to non-negated sentences, with greater negation-driven difficulty effects for true statements than false statements; PP: Images matched in pleasantness showed increased decision difficulty compared to image options that differed in pleasantness. Importantly, task-dependent decision difficulty effects were replicated independent of testing device or platform, demonstrating the robustness of trajectory-tracked measures of decision difficulty and offering seminal validation for the study of decision processes using small, portable devices outside of controlled laboratory spaces. Independent from these replication results, nuanced differences observed in pre-movement (i.e., reaction time) and post-movement (i.e., movement time and trajectory curvature) measures revealed device-dependent differences in which tablet- and smartphone-acquired measures showed right-hand reach direction biases resembling those seen in real-world movements while computer-acquired results did not. Tablet- and smartphone-use also showed greater sensitivity to decision difficulty expressed in movement times and trajectory curvature while computer-acquired results displayed greater sensitivity to decision difficulty expressed in reaction times. Finally, while task-replication results revealed an increase in all measures (reaction time, movement time and trajectory curvature) in response to increased decision difficulty, a correlation analysis between measures of decision difficulty revealed consistent between-measure relationships within each task and across each device and platform, wherein faster decisions (i.e., decisions with decreased reaction time) had more decision difficulty reflected in the movement (increased movement time and trajectory curvature). Together, these results provide support for models of decision making in which decision processes continue to unfold after movements to enact a choice have been initiated (Wispinski, Gallivan & Chapman, 2020), and further suggest that these processes are flexibly adjusted along the time course of a decision even when decision domain and difficulty remain consistent.

• Subjects / Keywords

  • Decision-making
  • Mouse-tracking
  • Smartphone
  • Tablet
  • Decision processes
  • Decision difficulty
  • Reach-decision

• Graduation date

  Fall 2021

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-8brz-4397

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.