Cutting through the clutter: Searching for targets in evolving complex scenes

We evaluated the use of visual clutter as a surrogate measure of set size effects in visual search by comparing the effects of subjective clutter (determined by independent raters) and objective clutter (as quantified by edge count and feature congestion) using evolving scenes, ones that varied incrementally in clutter while maintaining their semantic continuity. Observers searched for a target building in rural, suburban, and urban city scenes created using the game SimCity. Stimuli were 30 screenshots obtained for each scene type as the city evolved over time. Reaction times and search guidance (measured by scan path ratio) were fastest/strongest for sparsely cluttered rural scenes, slower/weaker for more cluttered suburban scenes, and slowest/weakest for highly cluttered urban scenes. Subjective within-city clutter estimates also increased as each city matured and correlated highly with RT and search guidance. However, multiple regression modeling revealed that adding objective estimates failed to better predict search performance over the subjective estimates alone. This suggests that within-city clutter may not be explained exclusively by low-level feature congestion; conceptual congestion (e.g., the number of different types of buildings in a scene), part of the subjective clutter measure, may also be important in determining the effects of clutter on search

Learning Interpretable Temporal Properties from Positive Examples Only

We consider the problem of explaining the temporal behavior of black-boxsystems using human-interpretable models. To this end, based on recent researchtrends, we rely on the fundamental yet interpretable models of deterministicfinite automata (DFAs) and linear temporal logic (LTL) formulas. In contrast tomost existing works for learning DFAs and LTL formulas, we rely on onlypositive examples. Our motivation is that negative examples are generallydifficult to observe, in particular, from black-box systems. To learnmeaningful models from positive examples only, we design algorithms that relyon conciseness and language minimality of models as regularizers. To this end,our algorithms adopt two approaches: a symbolic and a counterexample-guidedone. While the symbolic approach exploits an efficient encoding of languageminimality as a constraint satisfaction problem, the counterexample-guided onerelies on generating suitable negative examples to prune the search. Both theapproaches provide us with effective algorithms with theoretical guarantees onthe learned models. To assess the effectiveness of our algorithms, we evaluateall of them on synthetic data.<br

The psychological interaction of spam email features

This study explored distinct perceptual and decisional contributions to spam email mental construal. Participants classified spam emails according to pairings of three stimulus features – presence or absence of awkward prose, abnormal message structure, and implausible premise. We examined dimensional interactions within general recognition theory (GRT; a multidimensional extension of signal detection theory). Classification accuracy was highest for categories containing either two non-normal dimension levels (e.g. awkward prose and implausible premise) or two normal dimension levels (e.g. normal prose and plausible premise). Modelling indicated both perceptual and decisional contributions to classification responding. In most cases, perceptual discriminability was higher along one dimension when stimuli contained a non-normal level of the paired dimension (e.g. prose discriminability was higher with abnormal structure). Similarly, decision criteria along one dimension were biased in favour of the non-normal response when stimuli contained a non-normal level of the paired dimension. Potential applications for training are discussed

Visual search in ecological and non-ecological displays: Evidence for a non-monotonic effect of complexity on performance

Copyright @ 2013 PLoSThis article has been made available through the Brunel Open Access Publishing Fund.Considerable research has been carried out on visual search, with single or multiple targets. However, most studies have used artificial stimuli with low ecological validity. In addition, little is known about the effects of target complexity and expertise in visual search. Here, we investigate visual search in three conditions of complexity (detecting a king, detecting a check, and detecting a checkmate) with chess players of two levels of expertise (novices and club players). Results show that the influence of target complexity depends on level of structure of the visual display. Different functional relationships were found between artificial (random chess positions) and ecologically valid (game positions) stimuli: With artificial, but not with ecologically valid stimuli, a “pop out” effect was present when a target was visually more complex than distractors but could be captured by a memory chunk. This suggests that caution should be exercised when generalising from experiments using artificial stimuli with low ecological validity to real-life stimuli.This study is funded by Brunel University and the article is made available through the Brunel Open Access Publishing Fund

Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis

We propose "semantic labelling" as a novel ingredient for solving games in the context of LTL synthesis. It exploits recent advances in the automata-based approach, yielding more information for each state of the generated parity game than the game graph can capture. We utilize this extra information to improve standard approaches as follows. (i) Compared to strategy improvement (SI) with random initial strategy, a more informed initialization often yields a winning strategy directly without any computation. (ii) This initialization makes SI also yield smaller solutions. (iii) While Q-learning on the game graph turns out not too efficient, Q-learning with the semantic information becomes competitive to SI. Since already the simplest heuristics achieve significant improvements the experimental results demonstrate the utility of semantic labelling. This extra information opens the door to more advanced learning approaches both for initialization and improvement of strategies

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Virtual Reality for Nondestructive Evaluation Applications

Gas transmission pipelines are often inspected and monitored using the magnetic flux leakage method [1]. An inspection vehicle known as a “pig” is launched into the pipeline and conveyed along the pipe by the pressure of natural gas. The pig contains a magnetizer, an array of sensors and a microprocessor-based data acquisition system for logging data. The data is subsequently retrieved and analyzed offline. The pipeline inspection results in the generation of a vast amount of data — in excess of 4 GB, even in compressed form. It is important that these data are presented in a suitable manner for evaluation by trained operator. Virtual reality (VR) display techniques represent an attractive mechanism for presenting this huge amount of data effectively. The application of VR techniques enables the operator to explore the virtual environment generated by the computer. This technique can serve as an important bridge between human operator and the computer. In this paper, we present some preliminary efforts in achieving this interface

Striatal Volume Predicts Level of Video Game Skill Acquisition

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.United States. Office of Naval Research (grant number N00014-07-1-0903