Optimal trade-off between speed and acuity when searching for a small object

A Searcher seeks to find a stationary Hider located at some point H (not necessarily a node) on a given network Q. The Searcher can move along the network from a given starting point at unit speed, but to actually find the Hider she must pass it while moving at a fixed slower speed (which may depend on the arc). In this “bimodal search game,” the payoff is the first time the Searcher passes the Hider while moving at her slow speed. This game models the search for a small or well hidden object (e.g., a contact lens, improvised explosive device, predator search for camouflaged prey). We define a bimodal Chinese postman tour as a tour of minimum time δ which traverses every point of every arc at least once in the slow mode. For trees and weakly Eulerian networks (networks containing a number of disjoint Eulerian cycles connected in a tree-like fashion) the value of the bimodal search game is δ/2. For trees, the optimal Hider strategy has full support on the network. This differs from traditional search games, where it is optimal for him to hide only at leaf nodes. We then consider the notion of a lucky Searcher who can also detect the Hider with a positive probability q even when passing him at her fast speed. This paper has particular importance for demining problems

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

Visual search and decision making in bees: time, speed and accuracy

An insect searching a meadow for flowers may detect several flowers from different species per second, so the task of choosing the right flowers rapidly is not trivial. Here we apply concepts from the field of visual search in human experimental psychology to the task a bee faces in searching a meadow for familiar flowers, and avoiding ‘‘distraction’’ by unknown or unrewarding flowers. Our approach highlights the importance of visual information processing for understanding the behavioral ecology of foraging. Intensity of illuminating light, target contrast with background (both chromatic and achromatic), and number of distractors are all shown to have a direct influence on decision times in behavioral choice experiments. To a considerable extent, the observed search behavior can be explained by the temporal and spatial properties of neuronal circuits underlying visual object detection. Our results also emphasize the importance of the time dimension in decision making. During visual search in humans, improved accuracy in solving discrimination tasks comes at a cost in response time, but the vast majority of studies on decision making in animals have focused on choice accuracy, not speed. We show that in behavioral choice experiments in bees, there is a tight link between the two. We demonstrate both between-individual and within- individual speed-accuracy tradeoffs, whereby bees exhibit considerable behavioral flexibility in solving visual search tasks. Motivation is an important factor in selection of behavioral strategies for a search task, and sensory discrimination capabilities may be underestimated by studies that quantify accuracy of behavioral choice but neglect the temporal dimension

Controlling target selection for saccadic eye movements by informational value

Humans make up to three saccadic eye movements per second, with each saccade changing which part of a visual scene is projected on the relatively small retinal fovea, where visual information is processed with the highest visual acuity, and which parts of scenes are projected onto the relatively large peripheral part of the retina, where visual acuity is comparatively low. Due to the small size of the fovea, each saccade is necessarily led by a decision about which part of a visual scene to choose as the next eye movement target and followed by an evaluation of the eye movement’s spatial accuracy (i.e., was the saccade spatially accurate enough to allow foveation of relevant visual information?). Since visual scenes, however, are often complex and contain an abundance of visual information, two of the major challenges that the oculomotor system faces are to decide which environmental factors to take into account when choosing a saccade target, and what information in the postsaccadic visual input to consider for evaluation of eye movement accuracy. In three studies, the current dissertation investigated how the magnitude of task relevant information that can be gained at a saccade target (i.e., a saccade’s informational value) influences target selection for saccadic eye movements as well as target selection for saccade adaptation, i.e., the mechanism that maintains eye movement accuracy. Study I tested how the informational value and the costs of saccades are balanced for saccade target selection in a combined visual search and perceptual discrimination task. We instructed participants to find a target amidst distractors, and to discriminate a feature on the target. Participants were rewarded for correct discriminations and were given a limited amount of time to complete as many trials as possible. Informational value was manipulated by showing, in each trial, two targets with different discrimination difficulty. Participants were instructed that they could freely choose which target they wanted to search and discriminate. Costs of saccades were manipulated by, in each trial, varying the relative number of distractors that shared a feature with one or the other target. Since the available time to complete trials was limited, participants had to trade-off search costs (i.e., how much time to invest to find a target) against informational value (i.e., searching for an easy- or difficult-to-discriminate target) to optimize target selection, and their individual monetary gain per unit of time. We found that participants generally considered both informational value and search costs, when choosing which target to search and discriminate. However, participants accumulated less reward than was predicted by an ideal observer model that assumed an optimal trade-off between informational value and search costs for target selection. A generative probabilistic model revealed that this was due to noise at two levels: noise at the decision level corrupted which target participants chose to search for, resulting in occasional choices for lower gain targets, while noise at the fixation level corrupted target selection during search, resulting in occasional fixations on elements that were not instrumental to find the eventually chosen target quickly. Thus, although participants generally traded-off informational value and search costs for target selection, their final performance was constrained by noise in decisions about what to search for and how to search for it. Study II tested if informational value can facilitate the critical time-window in which the oculomotor system evaluates eye movement accuracy (Bahcall + Kowler, 2000; Fujita et al., 2002; Shafer et al., 2000). Participants were asked to make a saccade towards a neutral eye movement target. Upon saccade onset, two differently oriented stimuli were presented at opposite locations near the eye movement target, and participants were instructed that they have to report the orientation of one of the stimuli at the end of the trial. Critically, participants did not know upon saccade offset which stimulus was task-relevant (i.e., had higher informational value), but this was only revealed several seconds after saccade termination. Thus, eye movement accuracy (i.e., did the saccade accurately guide the fovea to a location close to the task-relevant visual information?) could not be evaluated upon saccade offset, but only based on visual working memory representation of the two stimuli, several seconds after saccade termination. We observed that informational value allowed for a delayed evaluation of eye movement accuracy, resulting in systematic trial-to-trial changes in the saccade amplitude that compensated for the experienced movement error relative to the task-relevant stimulus. This effect could not be explained by a lingering attentional bias, and disappeared when the informational value of stimuli was set to zero, i.e., when participants were not required to report their orientation. Thus, saccade adaptation considered the relative informational value of stimuli to select a target for saccade adaptation on a trial-to-trial basis, and informational value allowed for an evaluation of eye movement accuracy even long after saccade offset. Study III tested how eye movement accuracy is maintained in dynamic environments, where two stimuli with varying informational value, whose onset was separated in time, generated two conflicting error signals about movement accuracy. Participants were instructed to make a saccade towards a neutral eye movement target (a small square). Upon saccade onset, two candidate stimuli were shown at opposite locations near the eye movement target. Critically, the candidate stimuli were not shown at the same time, but their onset was separated by a temporal delay. Depending on the condition, participants were instructed either to discriminate a feature on the first candidate stimulus, whose error signal was available upon saccade offset, or on the second candidate stimulus, whose error signal became available only sometime after saccade termination. We found that saccade adaptation obligatorily evaluated movement accuracy based on the first available error signal after a saccade, irrespective of the informational value of the corresponding stimulus. This obligatory effect of the first available error signal disappeared when we, in a separate experiment, used a large circle instead of a small square as saccade target. However, although a large eye movement target allowed for greater flexibility when evaluating eye movement accuracy, it did not always allow for lasting saccade adaptation towards the location of task-relevant visual information, and it promoted a stronger contribution of an explicit targeting process, deployed voluntarily to bring gaze close to the location of task-relevant visual information. Thus, properties of the visual environment not only modulate whether saccade adaptation does or does not consider the relative informational value of two competing error signals when selecting a target for saccade adaptation, but environmental properties also influence how experienced movement errors are corrected. In sum, the present dissertation demonstrates that the oculomotor system considers the magnitude of task-relevant visual information at the eye movement target for target selection during visual search (Study I), and for selection of a target against which eye movement accuracy is evaluated (Studies II and III). This influence of informational value was observed on a trial-to-trial basis (Studies I and II), and on a longer time scale (Study III). However, the influence of informational value was not obligatory. Instead, the present dissertation shows that the oculomotor system aims to balance to benefits of saccades, in regard to the information that can be gained at the eye movement target, against the temporal costs of saccades (Study I), and it, furthermore, demonstrates that properties of visual environments modulate whether the relative informational value of stimuli is considered for evaluation of eye movement accuracy (Study III)

Visual Adaptations and Behavioural Strategies to Detect and Catch Small Targets

Predatory behaviours are ideal for studying the limits of performance and control within animals. Predation naturally creates a competition between the sensors and physiology of predator and prey. Aerial predation demonstrates the greatest feats of physical performance, demanding the highest speeds and accelerations whilst both predator and prey are free to pitch, yaw, and roll. These high speeds and degrees of rotational freedom make control a complex problem. However, from the perspective of the researcher attempting to decipher the control laws that underpin predator guidance, the question is made more soluble by the predator’s fixation on its target. The goal of the pursuer is clear, to contact the target, and thus their systems are focused on the optimization of that action. This is as opposed to more mundane activities, where conflicting interests compete for the attention and behavioural response of the animal. In order to study the necessary trade-offs that underpin aerial predation, this thesis will focus on the hunting behaviour of two fly species. The first is a robber fly, Holcocephala fusca, on which the majority of the first two chapters focus. Secondarily, work with the killer fly Coenosia attenuata will be included in the latter two chapters as a direct contrast to results from Holcocephala. Both are miniature dipteran predators, but not closely related. The structure of this thesis is broken into six chapters, summarised in the following list: 1. Thecompoundeyeofinsectsgenerallyhasmuchpoorerresolutionthanthatofcameratype eyes. Poor resolution is exacerbated in smaller insects that cannot commit the resources required for eyes with large lenses that facilitate high spatial resolution. Holcocephala has developed a small number of facets into a forward-facing acute zone where the spatial acuity is reduced to ~0.28°, rivalling the very best resolution of any compound eye. The only compound eyes with a comparable spatial resolution belong to dragonflies, in excess of an order of magnitude larger than Holcocephala. 2. Numerous potential targets may be airborne within the visual range of a predator. Not all of these may be suitable. Chasing unsuitable targets may waste energy or result in direct harm should they turn out to be larger than the predator can overcome. It is thus a strong imperative for a predator to filter the targets it takes after. Targets silhouetted against the sky display a paucity of cues that a predator could use to determine their size. Holcocephala displays acute size selectivity towards smaller targets. This selectivity goes beyond heuristic rules and size/speed ratios. Instead, Holcocephala appears able to determine absolute size and distance of targets. 3. Both Holcocephala and Coenosia intercept targets, heading for where the target is going to be in the future rather than its current location. Both species plot trajectories in keeping with the guidance law of proportional navigation, an algorithm derived for modern guided missiles. There are key differences evident in the internal physiological constants applied to the control system between the species. These differences are likely linked to the specific environmental conditions and visual physiologies of the flies, especially the range at which targets are attacked. 4. Stemming from the use of the proportional navigational framework, this chapter dives into the intricacies of gain and the weighting of the navigational constant, and the geometric factors that underpin the control effort and eventual success of the control system. 5. “Falcon-diving” can be found in killer flies dropping from their enclosure ceiling, in which they miss targets after diving towards them. Through proportional navigation, it can be demonstrated that the navigational system combined with excessive speed results in acceleration demands the body cannot match. 6. Holcocephala is capable of evading static obstacle whilst intercepting targets. Application of proportional navigation and a secondary obstacle-evasive controller can demonstrate where the fly is combining multiple inputs to guide its heading.This work was funded by the United States Airforce Office of Scientific Research

Active Sensing as Bayes-Optimal Sequential Decision Making

Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that optimize abstract statistical objectives such as information maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy [Najemnik & Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and effort. We simulate these algorithms on a simple visual search task to illustrate scenarios in which context-sensitivity is particularly beneficial and optimization with respect to generic statistical objectives particularly inadequate. Motivated by the geometric properties of the C-DAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate the more complex problem involving peripheral vision, and we notice that the difference between C-DAC and statistical policies becomes even more evident in this scenario.Comment: Scheduled to appear in UAI 201

Approximate optimal control model for visual search tasks

Visual search is a cognitive process that makes use of eye movements to bring the relatively high acuity fovea to bear on areas of interest to aid in navigation or interaction within the environment. This thesis explores a novel hypothesis that human visual search behaviour emerges as an adaptation to the underlying human information processing constraint, task utility and ecology. A new computational model (Computationally Rational Visual Search (CRVS) model) for visual search is also presented that provides a mathematical formulation for the hypothesis. Through the model, we ask the question, what mechanism and strategy a rational agent would use to move gaze and when should it stop searching? The CRVS model formulates the novel hypothesis for visual search as a Partially Observable Markov Decision Process (POMDP). The POMDP provides a mathematical framework to model visual search as a optimal adaptation to both top-down and bottom-up mechanisms. Specifically, the agent is only able to partially observe the environment due to the bounds imposed by the human visual system. The agent learns to make a decision based on the partial information it obtained and a feedback signal. The POMDP formulation is very general and it can be applied to a range of problems. However, finding an optimal solution to a POMDP is computationally expensive. In this thesis, we use machine learning to find an approximately optimal solution to the POMDP. Specifically, we use a deep reinforcement learning (Asynchronous Advantage Actor-Critic) algorithm to solve the POMDP. The thesis answers the where to fixate next and when to stop search questions using three different visual search tasks. In Chapter 4 we investigate the computationally rational strategies for when to stop search using a real-world search task of images on a web page. In Chapter 5, we investigate computationally rational strategies for where to look next when guided by low-level feature cues like colour, shape, size. Finally, in Chapter 6, we combine the approximately optimal strategies learned from the previous chapters for a conjunctive visual search task (Distractor-Ratio task) where the model needs to answer both when to stop and where to search question. The results show that visual search strategies can be explained as an approximately optimal adaptation to the theory of information processing constraints, utility and ecology of the task