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How do people interact with computers? This fundamental question was asked by Card, Moran, and Newell in 1983 with a proposition to frame it as a question about human cognition -- in other words, as a matter of how information is processed in the mind. Recently, the question has been reframed as one of adaptation: how do people adapt their interaction to the limits imposed by cognition, device design, and environment? This paper synthesizes advances toward an answer within the theoretical framework of computational rationality. The core assumption is that users act in accordance with what is best for them, given the limits imposed by their cognitive architecture and their experience of the task environment. This theory can be expressed in computational models that explain and predict interaction. The paper reviews the theoretical commitments and emerging applications in HCI, and it concludes by outlining a research agenda for future work.
Modern games make creative use of First- and Third-person perspectives (FPP and TPP) to allow the player to explore virtual worlds. Traditionally, FPP and TPP perspectives are seen as distinct concepts. Yet, Virtual Reality (VR) allows for flexibility in choosing perspectives. We introduce the notion of a perspective continuum in VR, which is technically related to the camera position and conceptually to how users perceive their environment in VR. A perspective continuum enables adapting and manipulating the sense of agency and involvement in the virtual world. This flexibility of perspectives broadens the design space of VR experiences through deliberately manipulating perception.
In a study, we explore users' attitudes, experiences and perceptions while controlling a virtual character from the two known perspectives. Statistical analysis of the empirical results shows the existence of a perspective continuum in VR. Our findings can be used to design experiences based on shifts of perception.
Online medical crowdfunding campaigns (OMCCs) help patients seek financial support. First impressions (FIs) of an OMCC, including perceived empathy, credibility, justice, impact, and attractiveness, could affect viewers' donation decisions. Images play a crucial role in manifesting FIs, and it is beneficial for fundraisers to understand how viewers may judge their selected images for OMCCs beforehand. This work proposes a data-driven approach to assessing whether an OMCC image conveys appropriate FIs. We first crowdsource viewers' perception of OMCC images. Statistical analysis confirms that agreement on all five dimensions of FIs exists, and these FIs positively correlate with donation intention. We compute image content, color, texture, and composition features, then analyze the correlation between these visual features and FIs. We further predict FIs based on these features, and the best model achieves an overall F1-score of 0.727. Finally, we discuss how our insights could benefit fundraisers and possible ethical concerns.
Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human-computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., "rotating" a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.
Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component Analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence Analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.