Human Variability and the Explore-Exploit Trade-Off in Recommendation.

The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These algorithms attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences stemming from the need to select items about which it is uncertain to obtain information about users versus the need to select items about which it is certain to secure high ratings. This tension is an instance of the exploration-exploitation trade-off in the context of recommender systems. Because humans are in this interaction loop, the long-term trade-off behavior depends on human variability. Our goal is to characterize the trade-off behavior as a function of human variability fundamental to such human-algorithm interaction. To tackle the characterization, we first introduce a unifying model that smoothly transitions between active learning and recommending relevant information. The unifying model gives us access to a continuum of algorithms along the exploration-exploitation trade-off. We then present two experiments to measure the trade-off behavior under two very different levels of human variability. The experimental results inform a thorough simulation study in which we modeled and varied human variability systematically over a wide rage. The main result is that exploration-exploitation trade-off grows in severity as human variability increases, but there exists a regime of low variability where algorithms balanced in exploration and exploitation can largely overcome the trade-off.

Spatiotemporal integration of isolated binocular three-dimensional motion cues.

Two primary binocular cues-based on velocities seen by the two eyes or on temporal changes in binocular disparity-support the perception of three-dimensional (3D) motion. Although these cues support 3D motion perception in different perceptual tasks or regimes, stimulus cross-cue contamination and/or substantial differences in spatiotemporal structure have complicated interpretations. We introduce novel psychophysical stimuli which cleanly isolate the cues, based on a design introduced in oculomotor work (Sheliga, Quaia, FitzGibbon, & Cumming, 2016). We then use these stimuli to characterize and compare the temporal and spatial integration properties of velocity- and disparity-based mechanisms. On average, temporal integration of velocity-based cues progressed more than twice as quickly as disparity-based cues; performance in each pure-cue condition saturated at approximately 200 ms and approximately 500 ms, respectively. This temporal distinction suggests that disparity-based 3D direction judgments may include a post-sensory stage involving additional integration time in some observers, whereas velocity-based judgments are rapid and seem to be more purely sensory in nature. Thus, these two binocular mechanisms appear to support 3D motion perception with distinct temporal properties, reflecting differential mixtures of sensory and decision contributions. Spatial integration profiles for the two mechanisms were similar, and on the scale of receptive fields in area MT. Consistent with prior work, there were substantial individual differences, which we interpret as both sensory and cognitive variations across subjects, further clarifying the case for distinct sets of both cue-specific sensory and cognitive mechanisms. The pure-cue stimuli presented here lay the groundwork for further investigations of velocity- and disparity-based contributions to 3D motion perception.

Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset.

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated actions. The quality of the data leads to promising results in both tasks. Moreover, using a learned human gaze model to inform imitation learning leads to an 115% increase in game performance. We interpret these results as highlighting the importance of incorporating human visual attention in models of decision making and demonstrating the value of the current dataset to the research community. We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.

Binocular viewing geometry shapes the neural representation of the dynamic three-dimensional environment.

Sensory signals give rise to patterns of neural activity, which the brain uses to infer properties of the environment. For the visual system, considerable work has focused on the representation of frontoparallel stimulus features and binocular disparities. However, inferring the properties of the physical environment from retinal stimulation is a distinct and more challenging computational problem-this is what the brain must actually accomplish to support perception and action. Here we develop a computational model that incorporates projective geometry, mapping the three-dimensional (3D) environment onto the two retinae. We demonstrate that this mapping fundamentally shapes the tuning of cortical neurons and corresponding aspects of perception. For 3D motion, the model explains the strikingly non-canonical tuning present in existing electrophysiological data and distinctive patterns of perceptual errors evident in human behavior. Decoding the world from cortical activity is strongly affected by the geometry that links the environment to the sensory epithelium.