A Framework for Computational Design and Adaptation of Extended Reality User Interfaces

To facilitate high quality interaction during the regular use of computing systems, it is essential that the user interface (UI) deliver content and components in an appropriate manner. Although extended reality (XR) is emerging as a new computing platform, we still have a limited understanding of how best to design and present interactive content to users in such immersive environments. Adaptive UIs offer a promising approach for optimal presentation in XR as the user's environment, tasks, capabilities, and preferences vary under changing context. In this position paper, we present a design framework for adapting various characteristics of content presented in XR. We frame these as five considerations that need to be taken into account for adaptive XR UIs: What?, How Much?, Where?, How?, and When?. With this framework, we review literature on UI design and adaptation to reflect on approaches that have been adopted or developed in the past towards identifying current gaps and challenges, and opportunities for applying such approaches in XR. Using our framework, future work could identify and develop novel computational approaches for achieving successful adaptive user interfaces in such immersive environments.Comment: 5 pages, CHI 2023 Workshop on The Future of Computational Approaches for Understanding and Adapting User Interface

Inter-item associations and memory for order

Remembering the order in which a sequence of events occurred can be an invaluable function of memory. Although important, the quality of memory for order information has been shown to be significantly affected by the way in which the information was encoded. That is, some encoding conditions lead to better memory for order than others. This dissertation presents a systematic examination of the features of encoding tasks that disrupt memory for order. Chapter I examines the effects of semantic versus non-semantic processing, item-specific versus item-generic processing, and item-specific versus relational processing on memory for order, revealing that any type of response-required task disrupts memory for order, unless that task is relational in nature. Chapter II examines the role of processing time in the disruption of memory for order and demonstrates that preserving response-free study time benefits memory for order, suggesting that response tasks disrupt memory for order because they take time away from the encoding of relational information. Chapter III introduces a novel procedure for examining memory for order—an order recognition task—which provides a new method for testing relational memory, allowing for conceptual replications and enhancing the generalizability of findings surrounding encoding tasks and memory for order. Taken together, the experiments in this dissertation demonstrate that poor memory for order is the direct result of reduced time for processing inter-item relations

Retrieval-induced forgetting: Testing the competition assumption of inhibition theory

Practicing the retrieval of some information can lead to poorer retrieval of other related information; this phenomenon is called retrieval-induced forgetting. This pattern has been explained as the result of inhibition of the related information during retrieval practice (Anderson, 2003). A core assumption of this inhibition account is that, to be suppressed, the related information must compete with the target information at the time of practice. Four experiments are reported that test this competition assumption. Two experiments showed that retrieval-induced forgetting did not occur without specific retrieval practice of the target items, replicating and extending prior findings. Two further experiments then showed that retrieval-induced forgetting did occur, however, when competition between target information and related information during retrieval practice was eliminated, undermining the competition assumption and hence the inhibition account. A new explanation of retrieval-induced forgetting is introduced that emphasizes context change between study, retrieval practice, and test

Neural reactivation in parietal cortex enhances memory for episodically linked information.

Remembering is a complex process that involves recalling specific details, such as who you were with when you celebrated your last birthday, as well as contextual information, such as the place where you celebrated. It is well established that the act of remembering enhances long-term retention of the retrieved information, but the neural and cognitive mechanisms that drive memory enhancement are not yet understood. One possibility is that the process of remembering results in reactivation of the broader episodic context. Consistent with this idea, in two experiments, we found that multiple retrieval attempts enhanced long-term retention of both the retrieved object and the nontarget object that shared scene context, compared with a restudy control. Using representational similarity analysis of fMRI data in experiment 2, we found that retrieval resulted in greater neural reactivation of both the target objects and contextually linked objects compared with restudy. Furthermore, this reactivation occurred in a network of medial and lateral parietal lobe regions that have been linked to episodic recollection. The results demonstrate that retrieving a memory can enhance retention of information that is linked in the broader event context and the hippocampus and a posterior medial network of parietal cortical areas (also known as the Default Network) play complementary roles in supporting the reactivation of episodically linked information during retrieval

XAIR: A Framework of Explainable AI in Augmented Reality

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing System

Enhancing SART Validity by Statistically Controlling Speed-Accuracy Trade-Offs

Numerous studies focused on elucidating the correlates, causes, and consequences of inattention/attention lapses employ the Sustained Attention to Response Task (SART), a GO-NOGO task with infrequent withholds. Although the SART has become popular among inattention researchers, recent work has demonstrated its susceptibility to speed-accuracy trade-offs (SATOs), rendering its assessment of inattention problematic. Here, we propose and illustrate methods to statistically control for the occurrence of SATOs during SART performance. The statistical solutions presented here can be used to correct standard SART error scores, including those of already-published data, thereby allowing researchers to re-examine existing data, and to more sensitively evaluate the validity of earlier conclusions