OVERVIEW

In our day-to-day lives, we rarely perform actions in isolation. Individual actions are usually embedded in a continuous stream of actions requiring whole-body coordination (i.e. a combination of manual actions performed with our hands, and locomotor activities, involving our feet.) 

As adults, we seamlessly transition between actions, often without realizing that we strategically plan and adjust our current movements to facilitate the smooth execution of subsequent actions. For example, when picking up an overturned glass to pour water, we naturally employ a thumb-down grasp, whereas when reaching for a glass to drink from, we instinctively use a thumb-up grasp. This planning is essential for flexible and fluent navigation in our environment, enabling us to carry out daily activities with ease.

This study explores multi-step action planning as it unfolds in real-time (i.e. the systems that coordinate to perform the series of activities successfully) and as it develops (i.e., how planning changes from toddlers to adults). 

LITERATURE REVIEW

• To understand the lay of the land and existing findings in the area we first did an extensive literature review on single-step and multi-step planning, involving all age groups. Our literature review revealed that although there were several studies investigating the development of manual action planning in both adults and children, the locomotor landscape was relatively less explored. Moreover, there was only one published paper investigating multi-step planning in a combination of manual and locomotor tasks—a situation that is closer to how actions occur in real life—and it only included adults.

• The literature review revealed an opportunity area—to explore the development of multi-step action planning in children, as well as take advantage of the advanced technology in the lab to investigate how multi-step planning unfolded in real-time.

STUDY DESIGN

• We chose to test 2, 4, 6, 8-year-olds and adults.

• We had several meetings to discuss and finalize the study design and timeline for the study. We wanted to design a task that combined a locomotor task with a manual action task but both tasks needed to be simple enough that they could be performed effortlessly in isolation by all the age groups in the study. 

• We adopted the task from the controlled, adult-only multi-step planning study and adapted it into an age-appropriate, fun game for children--while still getting at the same core phenomenon. 

• Participants started on a pair of footprints and walked up to a table (locomotor task) where they picked up a ball and put it in a bucket, also on the table (manual task). The bucket location was varied across trials--alternating between near and far, left or right of the ball. To keep the youngest kids motivated we used snacks and simple games, while social interaction and more advanced construction games served as motivators for the older children. Adults did not need any additional motivation and performed the multiple trials regardless.  The entire session was recorded on video to capture detailed behavioral data.

• To see how the sequence of actions unfold over time, all participants approached the table over a pressure-sensitive gait carpet that allowed us to quantify footfall measures like step length and step count and wore head-mounted eye trackers that allowed us to see the visual information gathered by the participants during the task

OPERATIONALIZE BEHAVIOR

• With such a rich data set of behavior, the next step was to characterize and quantify the observed behavior to turn it into meaningful and analyzable data. We brainstormed how to operationalize the captured behavior and design codes to identify and quantify the key behaviors of interest.

• A previous study in adults noted that adults showed a landing foot preference at the table for the far trials, demonstrating their ability to plan. We coded their landing foot at the table to capture this phenomenon, but one outcome measure did not fully capture the observed phenomena, so we went back to expand and add additional behavioral codes that allowed us to dig into our data and find the full story.

DATA ANALYSIS

Data Collection and coding are still ongoing for this study! 

STORYBOARDING

As the data reveals new insights, we are simultaneously working to storyboard expected results and decide on visualization methods that best depict the data.