Modeling Task Control of Eye Movements

In natural behavior, visual information is actively sampled from the environment by a sequence of gaze changes. The timing and choice of gaze targets, and the accompanying attentional shifts, are intimately linked with ongoing behavior. Nonetheless, modeling of the deployment of these fixations has been very difficult because they depend on characterizing the underlying task structure. Recently, advances in eye tracking during natural vision, together with the development of probabilistic modeling techniques, have provided insight into how the cognitive agenda might be included in the specification of fixations. These techniques take advantage of the decomposition of complex behaviors into modular components. A particular subset of these models casts the role of fixation as that of providing task-relevant information that is rewarding to the agent, with fixation being selected on the basis of expected reward and uncertainty about environmental state. We review this work here and describe how specific examples can reveal general principles in gaze control

Attention, Reward, and Information Seeking

Decision making is thought to be guided by the values of alternative options and involve the accumulation of evidence to an internal bound. However, in natural behavior, evidence accumulation is an active process whereby subjects decide when and which sensory stimulus to sample. These sampling decisions are naturally served by attention and rapid eye movements (saccades), but little is known about how saccades are controlled to guide future actions. Here we review evidence that was discussed at a recent symposium, which suggests that information selection involves basal ganglia and cortical mechanisms and that, across different contexts, it is guided by two central factors: the gains in reward and gains in information (uncertainty reduction) associated with sensory cues

Differential impact of partial cortical blindness on gaze strategies when sitting and walking – An immersive virtual reality study

AbstractThe present experiments aimed to characterize the visual performance of subjects with long-standing, unilateral cortical blindness when walking in a naturalistic, virtual environment. Under static, seated testing conditions, cortically blind subjects are known to exhibit compensatory eye movement strategies. However, they still complain of significant impairment in visual detection during navigation. To assess whether this is due to a change in compensatory eye movement strategy between sitting and walking, we measured eye and head movements in subjects asked to detect peripherally-presented, moving basketballs. When seated, cortically blind subjects detected ∼80% of balls, while controls detected almost all balls. Seated blind subjects did not make larger head movements than controls, but they consistently biased their fixation distribution towards their blind hemifield. When walking, head movements were similar in the two groups, but the fixation bias decreased to the point that fixation distribution in cortically blind subjects became similar to that in controls – with one major exception: at the time of basketball appearance, walking controls looked primarily at the far ground, in upper quadrants of the virtual field of view; cortically blind subjects looked significantly more at the near ground, in lower quadrants of the virtual field. Cortically blind subjects detected only 58% of the balls when walking while controls detected ∼90%. Thus, the adaptive gaze strategies adopted by cortically blind individuals as a compensation for their visual loss are strongest and most effective when seated and stationary. Walking significantly alters these gaze strategies in a way that seems to favor walking performance, but impairs peripheral target detection. It is possible that this impairment underlies the experienced difficulty of those with cortical blindness when navigating in real life

Climate change scenarios for the California region

To investigate possible future climate changes in California, a set of climate change model simulations was selected and evaluated. From the IPCC Fourth Assessment, simulations of twenty-first century climates under a B1 (low emissions) and an A2 (a medium-high emissions) emissions scenarios were evaluated, along with occasional comparisons to the A1fi (high emissions) scenario. The climate models whose simulations were the focus of the present study were from the Parallel Climate Model (PCM1) from NCAR and DOE, and the NOAA Geophysical Fluid Dynamics Laboratory CM2.1 model (GFDL). These emission scenarios and attendant climate simulations are not “predictions,” but rather are a purposely diverse set of examples from among the many plausible climate sequences that might affect California in the next century. Temperatures over California warm significantly during the twenty-first century in each simulation, with end-of-century temperature increases from approximately +1.5°C under the lower emissions B1 scenario in the less responsive PCM1 to +4.5°C in the higher emissions A2 scenario within the more responsive GFDL model. Three of the simulations (all except the B1 scenario in PCM1) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter. Relatively small (less than ~10%) changes in overall precipitation are projected. The California landscape is complex and requires that model information be parsed out onto finer scales than GCMs presently offer. When downscaled to its mountainous terrain, warming has a profound influence on California snow accumulations, with snow losses that increase with warming. Consequently, snow losses are most severe in projections by the more responsive model in response to the highest emissions

Climate scenarios for California

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions

Development of a Virtual Laboratory for the Study of Complex Human Behavior

The study of human perception has evolved from examining simple tasks executed in reduced laboratory conditions to the examination of complex, real-world behaviors. Virtual environments represent the next evolutionary step by allowing full stimulus control and repeatability for human subjects, and a testbed for evaluating models of human behavior. Visual resolution varies dramatically across the visual field, dropping orders of magnitude from central to peripheral vision. Humans move their gaze about a scene several times every second, projecting taskcritical areas of the scene onto the central retina. These eye movements are made even when the immediate task does not require high spatial resolution. Such “attentionally-driven” eye movements are important because they provide an externally observable marker of the way subjects deploy their attention while performing complex, real-world tasks. Tracking subjects’ eye movements while they perform complex tasks in virtual environments provides a window into perception. In addition to the ability to track subjects’ eyes in virtual environments, concurrent EEG recording provides a further indicator of cognitive state. We have developed a virtual reality laboratory in which head-mounted displays (HMDs) are instrumented with infrared video-based eyetrackers to monitor subjects’ eye movements while they perform a range of complex tasks such as driving, and manual tasks requiring careful eye-hand coordination. A go-kart mounted on a 6DOF motion platform provides kinesthetic feedback to subjects as they drive through a virtual town; a dual-haptic interface consisting of two SensAble Phantom extended range devices allows free motion and realistic force-feedback within a 1^3 m volume (Refer to PDF file for exact formulas)