Decision making in slow and rapid reaching: Sacrificing success to minimize effort

Hesse, Constanze; Kangur, Karina; Hunt, Amelia R.

doi:10.31234/osf.io/9w6jp

Cited by 5 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the current study, the execution of the detection aspect of the task responded to the motivation manipulation and showed clear improvements, but the decision about where to look in preparation for the task was unchanged. A similar dissociation is reported in [30], where participants performed rapid reaches towards pairs of lights, one of which was only revealed to be the target after the movement had begun. In these experiments, participants were able to weigh up possible movement plans and select those which minimised mechanical effort.…”

Section: Discussionsupporting

confidence: 81%

Inefficient Eye Movements: Gamification Improves Task Execution, But Not Fixation Strategy

James

Reuther

Angus

et al. 2019

Vision

Self Cite

View full text Add to dashboard Cite

Decisions about where to fixate are highly variable and often inefficient. In the current study, we investigated whether such decisions would improve with increased motivation. Participants had to detect a discrimination target, which would appear in one of two boxes, but only after they chose a location to fixate. The distance between boxes determines which location to fixate to maximise the probability of being able to see the target: participants should fixate between the two boxes when they are close together, and on one of the two boxes when they are far apart. We “gamified” this task, giving participants easy-to-track rewards that were contingent on discrimination accuracy. Their decisions and performance were compared to previous results that were gathered in the absence of this additional motivation. We used a Bayesian beta regression model to estimate the size of the effect and associated variance. The results demonstrate that discrimination accuracy does indeed improve in the presence of performance-related rewards. However, there was no difference in eye movement strategy between the two groups, suggesting this improvement in accuracy was not due to the participants making more optimal eye movement decisions. Instead, the motivation encouraged participants to expend more effort on other aspects of the task, such as paying more attention to the boxes and making fewer response errors.

show abstract

Section: Discussionsupporting

confidence: 81%

Inefficient Eye Movements: Gamification Improves Task Execution, But Not Fixation Strategy

James

Reuther

Angus

et al. 2019

Vision

Self Cite

View full text Add to dashboard Cite

show abstract

“…3A). This resulted in more initial and final decision errors during interception, supporting recent evidence that decision-making is impaired when the movement required is more demanding to perform (Hesse et al, 2020;Reynaud et al, 2020). The dorsal and ventral streams are driven predominantly by magnocellular and parvocellular inputs, respectively, and axons of parvocellular cells have slower conduction velocity than magnocellular cells (Maunsell et al, 1999).…”

Section: Visuomotor Decision Errors Suggest Impaired Ventral-dorsal Stream Processing In Older Adultssupporting

confidence: 76%

Age-related deficits in rapid visuomotor decision-making

Gómez-Granados

Barany

Schrayer

et al. 2021

Journal of Neurophysiology

View full text Add to dashboard Cite

Many goal-directed actions that require rapid visuomotor planning and perceptual decision-making are affected in older adults, causing difficulties in execution of many functional activities of daily living. Visuomotor planning and perceptual decision-making are mediated by the dorsal and ventral visual streams, respectively, but it is unclear how age-induced changes in sensory processing in these streams contribute to declines in goal-directed actions. Previously, we have shown that in healthy adults, task demands influence movement strategies during visuomotor decision-making, reflecting differential integration of sensory information between the two streams. Here, we asked the question if older adults would exhibit larger declines in interactions between the two streams during demanding motor tasks. Older adults (n=15) and young controls (n=26) performed reaching or interception movements towards virtual objects. In some blocks of trials, participants also had to select an appropriate movement goal based on the shape of the object. Our results showed that older adults corrected fewer initial decision errors during both reaching and interception movements. During the interception decision task, older adults made more decision- and execution-related errors than young adults, which were related to early initiation of their movements. Together, these results suggest that older adults have a reduced ability to integrate new perceptual information to guide online action, which may reflect impaired ventral-dorsal stream interactions.

show abstract

“…In order to transfer user preferences from one assembly task to another, we want to model the preferences based on features that are task-agnostic. Previous studies [14], [16], [17] have shown that users prefer to minimize movements during task execution. We expect the same for users in an assembly task, i.e., users would prefer to minimize movements required to perform the assembly.…”

Section: B Factors Affecting Human Preferences In Physical Tasksmentioning

confidence: 99%

“…Inspired from prior work in economy of human movement [14], [16], [17], we presume that users would try to minimize their movement throughout the task. Because the set of actions is fixed in our assembly tasks, users can minimize their movement when they switch from one action to the next.…”

Section: B Modeling User Preferences Over Abstract Featuresmentioning

confidence: 99%

“…Inspired from prior work in economy of human movement [14]- [17] and task ordering [18], we presume that users will have preferences over minimizing their movement and effort spent in the task. Thus, our key insight is that user preference across different assembly tasks can be represented with a set of abstract, task-agnostic features, such as the physical and mental effort required by the user to perform the actions in the assembly tasks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks

Nemlekar¹,

Guan²,

Luo³

et al. 2021

Preprint

View full text Add to dashboard Cite

To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and timeconsuming. Our thesis is that we can learn user preferences in assembly tasks from demonstrations in a representative canonical task. Inspired by previous work in economy of human movement, we propose to represent user preferences as a linear function of abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn their preference from demonstrations in a canonical task and use the learned preference to anticipate their actions in the actual assembly task without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.

show abstract

Decision making in slow and rapid reaching: Sacrificing success to minimize effort

Cited by 5 publications

References 22 publications

Inefficient Eye Movements: Gamification Improves Task Execution, But Not Fixation Strategy

Inefficient Eye Movements: Gamification Improves Task Execution, But Not Fixation Strategy

Age-related deficits in rapid visuomotor decision-making

Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks

Contact Info

Product

Resources

About