Exploring the Effect of Virtual Depth on Pupil Diameter

Iskander, Julie; Attia, Mohamed; Saleh, Khaled J.; Abobakr, Ahmed; Nahavandi, Darius; Hossny, Mohammed; Nahavandi, Saeid

doi:10.1109/smc.2019.8913975

Cited by 4 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This implies that the pupil near response does not result in a sustained change of pupil size. This contradicts the findings by Iskander, et al (2019), who found increased pupil size when targets appeared closer by. It was theorized that these previous findings could be explained by (subjective) differences in target luminance caused by variations in the target's size relative to the visual field.…”

Section: Discussioncontrasting

confidence: 87%

“…However, this time the target could appear at any of five locations, corresponding to the corners and centre of a square with a height and width of 16° of the visual field (Figure 2). Within a block, target distance was fixed and the target would appear on all five locations in random order, remaining at each location for 5 s. This was repeated for five blocks, each positioning the target at a different distance (1.5 m, 1.75 m, 2 m, 3 m and 4 m), as derived from Iskander, et al (2019). The order of the distances was randomized.…”

Section: Methodsmentioning

confidence: 99%

“…To our knowledge, only a single experiment has attempted to research if target distance influences pupil size when using HMDs (Iskander et al, 2019). In that experiment, participants were asked to sequentially focus several targets at a range of distances while their pupil size was measured.…”

Section: Introductionmentioning

confidence: 99%

“…The first experiment used a methodology which could detect pupil near responses when shifting gaze from a far to a near target (Campbell, 1957;Feil, et al, 2017;Fry, 1945), but it could not be used to replicate the findings by Iskander, et al (2019), who looked at average pupil size while fixating gaze on targets at various distances. Therefore, a second experiment was added, more in line with Iskander, et al (2019). In this experiment, average pupil size was compared between conditions where participants fixated a target placed at different distances.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The pupil near response is short lasting and intact in virtual reality head mounted displays

Pielage¹,

Zekveld²,

Ven³

et al. 2023

JEMR

View full text Add to dashboard Cite

The pupil of the eye constricts when moving focus from an object further away to an object closer by. This is called the pupil near response, which typically occurs together with accommodation and vergence responses. When immersed in virtual reality mediated through a head-mounted display, this triad is disrupted by the vergence-accommodation conflict. However, it is not yet clear if the disruption also affects the pupil near response. Two experiments were performed to assess this. The first experiment had participants follow a target that first appeared at a far position and then moved to either a near position (far-to-near; FN) or to another far position (far-to-far; FF). The second experiment had participants follow a target that jumped between five positions, which was repeated at several distances. Experiment 1 showed a greater pupil constriction amplitude for FN trials, compared to FF trials, suggesting that the pupil near response is intact in head-mounted display mediated virtual reality. Experiment 2 did not find that average pupil dilation differed when fixating targets at different distances, suggesting that the pupil near response is transient and does not result in sustained pupil size changes.

show abstract

Section: Discussioncontrasting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The pupil near response is short lasting and intact in virtual reality head mounted displays

Pielage¹,

Zekveld²,

Ven³

et al. 2023

JEMR

View full text Add to dashboard Cite

show abstract

“…The advantage of the proposed method in the bipedal walking problem and the wide variety of activation functions demonstrated (Figure 7) a promising potential for solving several biomechanics problems where different muscles have different characteristics and response functions, as highlighted in [22]. Applications such as fall detection and prevention [23], ocular motility and the associated cognitive load, and motion sickness [24][25][26][27][28][29][30][31], as well as intent prediction of pedestrians and cyclists [32,33]. Training stability, when using the parameterised tanh in an actor-critic architecture, can potentially be used for advancing Generative Adversarial Networks (GANs) research for image synthesis [34].…”

Section: Discussionmentioning

confidence: 99%

Refined Continuous Control of DDPG Actors via Parametrised Activation

Hossny¹,

Iskander²,

Attia³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Continuous action spaces impose a serious challenge for reinforcement learning agents. While several off-policy reinforcement learning algorithms provide a universal solution to continuous control problems, the real challenge lies in the fact that different actuators feature different response functions due to wear and tear (in mechanical systems) and fatigue (in biomechanical systems). In this paper, we propose enhancing the actor-critic reinforcement learning agents by parameterising the final layer in the actor network. This layer produces the actions to accommodate the behaviour discrepancy of different actuators under different load conditions during interaction with the environment. To achieve this, the actor is trained to learn the tuning parameter controlling the activation layer (e.g., Tanh and Sigmoid). The learned parameters are then used to create tailored activation functions for each actuator. We ran experiments on three OpenAI Gym environments, i.e., Pendulum-v0, LunarLanderContinuous-v2, and BipedalWalker-v2. Results showed an average of 23.15% and 33.80% increase in total episode reward of the LunarLanderContinuous-v2 and BipedalWalker-v2 environments, respectively. There was no apparent improvement in Pendulum-v0 environment but the proposed method produces a more stable actuation signal compared to the state-of-the-art method. The proposed method allows the reinforcement learning actor to produce more robust actions that accommodate the discrepancy in the actuators’ response functions. This is particularly useful for real life scenarios where actuators exhibit different response functions depending on the load and the interaction with the environment. This also simplifies the transfer learning problem by fine-tuning the parameterised activation layers instead of retraining the entire policy every time an actuator is replaced. Finally, the proposed method would allow better accommodation to biological actuators (e.g., muscles) in biomechanical systems.

show abstract

Measuring Cognitive Load in Virtual Reality Training via Pupillometry

Lee,

de Jong,

Donkers

et al. 2024

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

Pupillometry is known as a reliable technique to measure cognitive load in learning and performance. However, its applicability to virtual reality (VR) environments, an emerging technology for simulation-based training, has not been well-verified in educational contexts. Specifically, the VR display causes light reflexes that confound task-evoked pupillary responses (TEPRs), impairing cognitive load measures. Through this pilot study, we validated whether task difficulty can predict cognitive load as measured by TEPRs corrected for the light reflex and if these TEPRs correlate with cognitive load selfratings and performance. 14 students in health sciences performed observation tasks in two conditions: difficult versus easy tasks, whilst watching a VR scenario in home health care. Then, a cognitive load self-rating ensued. We used a VR system with a built-in eye-tracker and a photosensor installed to assess pupil diameter and light intensity during the scenario. Employing a method from the human-computer interaction field, we determined TEPRs by modeling the pupil light reflexes using a baseline. As predicted, the difficult task caused significantly larger TEPRs than the easy task. Only in the difficult task condition did TEPRs positively correlate with the performance measure. These results suggest that TEPRs are valid measures of cognitive load in VR training when corrected for the light reflex. It opens up possibilities to use real-time cognitive load for assessment and instructional design for VR training. Future studies should test our findings with a larger sample size, in various domains, involving complex VR functions such as haptic interaction.

show abstract

Exploring the Effect of Virtual Depth on Pupil Diameter

Cited by 4 publications

References 15 publications

The pupil near response is short lasting and intact in virtual reality head mounted displays

The pupil near response is short lasting and intact in virtual reality head mounted displays

Refined Continuous Control of DDPG Actors via Parametrised Activation

Measuring Cognitive Load in Virtual Reality Training via Pupillometry

Contact Info

Product

Resources

About