Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm

Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches

Prediction of Severity of Aviation Landing Accidents Using Support Vector Machine Models

The purpose of this study was to apply support vector machine (SVM) models to predict the severity of aircraft damage and the severity of personal injury during an aircraft approach and landing accident and to evaluate and rank the importance of 14 accident factors to the severity. Three new factors were introduced using the theory of inattentional blindness: The presence of visual area surface penetrations for a runway, the Federal Aviation Administration’s (FAA) visual area surface penetration policy timeframe, and the type of runway approach lighting. The study comprised 1,297 aircraft approach and landing accidents at airports within the United States with at least one instrument approach procedure. The dataset was gathered from a combination of the National Transportation Safety Board (NTSB) accident database, the NTSB accident reports, and the FAA’s Instrument Flight Procedure Gateway website. Four SVM models were developed in using the linear, polynomial, radial basis function (RBF), and sigmoid kernels for the severity of aircraft damage and another four SVM models were developed for the severity of personal injury. Five-fold cross-validation was used for testing the model accuracy and measures including evaluation of confusion matrices, misclassification rates, accuracy, precision, sensitivity/recall, and F1-scores for model comparison. The best kernel models were selected and its model hyperparameters were optimized for the best model performance. The SVM models using the RBF kernel produced the best machine learning models, with a 96% accuracy for predicting the severity of aircraft damage (0.94 precision, 0.95 recall, and 0.95 F1-score) and a 98% accuracy for predicting the severity of personal injury (0.99 precision, 0.98 recall, and 0.99 F1-score). The top predictors across both models were the pilot’s total flight hours, time of the accident, pilot’s age, crosswind component, landing runway number, single-engine land certificate, and any obstacle penetration. Specifically, the visual area surface obstacle penetration status ranked ninth across both SVM models. However, as a sub-category, an obstacle penetration on final approach was the seventh overall predictor and the second highest of the categorical predictors. The FAA visual area surface policy was ranked eighth as the overall factor, and the FAA policy from 2018 to 2019 was the third highest categorical predictor. Finally, the type of runway lighting was the sixth ranked prediction factor. This study demonstrates the benefit of SVM modeling using the RBF kernel for accident prediction and for datasets with categorical factors. It is recommended for the NTSB to add the collection of all three new factors into the NTSB database for future aviation accident research. Lastly, flight training should include information on a pilot’s susceptibility to inattentional blindness and the risks of potential obstacles in their flight path

Mapping female bodily features of attractiveness

Beauty is bought by judgment of the eye (Shakespeare, Loves Labours Lost), but the bodily features governing this critical biological choice are still debated. Eye movement studies have demonstrated that males sample coarse body regions expanding from the face, the breasts and the midriff, while making female attractiveness judgements with natural vision. However, the visual system ubiquitously extracts diagnostic extra-foveal information in natural conditions, thus the visual information actually used by men is still unknown. We thus used a parametric gaze-contingent design while males rated attractiveness of female front- and back-view bodies. Males used extra-foveal information when available. Critically, when bodily features were only visible through restricted apertures, fixations strongly shifted to the hips, to potentially extract hip-width and curvature, then the breast and face. Our hierarchical mapping suggests that the visual system primary uses hip information to compute the waist-to-hip ratio and the body mass index, the crucial factors in determining sexual attractiveness and mate selection

Semiotics and design: Towards an aesthetics of the artificial

Semiotics is the theory par excellence of the artificial and therefore should have a substantial role in understanding designed phenomena. By tracing the relation between design and semiotics at the level of the distinction between the analytic and the synthetic (or artificial), this paper argues that semiotics struggles to explain the environmental element of design so central to post-artefactual accounts of design. The analytic method of semiology is suitable for understanding existent semiotic structures but less so at modeling alternate signifying systems—or systems that alter, transform and self-interpret, that is, environments. The paper argues that to understand such milieus a turn to the aesthetic is necessary. By aesthetics it is meant the simultaneous mapping of the environment, the articulation of the environment and the counterfactual element of any design process. More particularly the paper will focus on recent developments within social semiotics to argue that such a framework must move beyond the constraints of analytical spatial and visual grammar to take into account not only multimodal texts but planning, systems and services. It will conclude by arguing that ultimately design and aesthetics are the same phenomenon, not in the sense that design is the study and application of aesthetic principles to useful objects or experiences, but in the sense that it is the organization of the counterfactual elements of artificial—designed—environments

Richard P. Bunge memorial lecture. Nerve injury and repair--a challenge to the plastic brain.

Repair and reconstruction of major nerve trunks in the upper extremity is a very challenging surgical problem. Today, there is no surgical repair technique that can assure recovery of tactile discrimination in the hand of an adult patient following nerve repair. In contrast, young individuals usually attain a complete recovery of functional sensibility. The outcome from nerve repair depends mainly on central nervous system factors including functional cortical reorganizational processes caused by misdirection in axonal outgrowth. Deafferentation due to local anesthetic block, amputation or nerve transection in the upper extremity leads to very rapid cortical synaptic remodeling, resulting in a distorted cortical hand representation as well as in enlarged and overlapping cortical receptive fields. Sensory relearning programs are aimed at refinement of these receptive fields to normalize the distorted hand map and improve processing at a high-order cortical level in the context of the 'new language spoken by the hand'. As peripheral nerve repair techniques cannot be further refined, there is a need for new and improved strategies for sensory relearning following nerve repair. We propose the utilization of multimodal capacity of the brain, using another sense (hearing) to substitute for lost hand sensation and to provide an alternate sensory input from the hand early after transection. The purpose was to modulate cortical reorganizations due to deafferentation to preserve cortical hand representation. Preliminary results from a prospective clinical randomized study indicate that the use of a Sensor Glove System, which stereophonically transposes the friction sound elicited by active touch, results in improved recovery of tactile discrimination in the nerve-injured hand. Future strategies for treatment of nerve injuries should promote cellular methods to minimize post-traumatic nerve cell death and to improve axonal outgrowth rate and orientation, but high on the agenda are new strategies for refined sensory relearning following nerve repair

A Saliency-based Clustering Framework for Identifying Aberrant Predictions

In machine learning, classification tasks serve as the cornerstone of a wide range of real-world applications. Reliable, trustworthy classification is particularly intricate in biomedical settings, where the ground truth is often inherently uncertain and relies on high degrees of human expertise for labeling. Traditional metrics such as precision and recall, while valuable, are insufficient for capturing the nuances of these ambiguous scenarios. Here we introduce the concept of aberrant predictions, emphasizing that the nature of classification errors is as critical as their frequency. We propose a novel, efficient training methodology aimed at both reducing the misclassification rate and discerning aberrant predictions. Our framework demonstrates a substantial improvement in model performance, achieving a 20\% increase in precision. We apply this methodology to the less-explored domain of veterinary radiology, where the stakes are high but have not been as extensively studied compared to human medicine. By focusing on the identification and mitigation of aberrant predictions, we enhance the utility and trustworthiness of machine learning classifiers in high-stakes, real-world scenarios, including new applications in the veterinary world

A framework for the design and evaluation of magic tricks that utilises computational systems configured with psychological constraints

PhDA human magician blends science, psychology and performance to create a magical e ect. This thesis explores what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical e ects are all in some form based on hidden mathematical, scienti c or psychological principles; the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical e ect required. The complexity is often caused by interacting and con icting physical and psychological constraints that need to be optimally balanced. Normally this tuning is done by trial and error, combined with human intuitions. This thesis focuses on applying Arti- cial Intelligence methods to the creation, and optimisation, of magic tricks exploiting mathematical principles. Experimentally derived, crowd sourced, data about particular perceptual and cognitive features is used, combined with a model of the underlying mathematical process, to provide a psychologically valid metric to allow optimisation of magical impact. The thesis describes an optimisation framework that can be exibly applied to a range of di erent types of mathematics based tricks. Three case studies are presented as exemplars of the methodology at work, the outputs of which are: language and image based prediction and mind reading tricks, a magical jigsaw, and a mind reading card trick e ect. Each trick created is evaluated through testing at public engagement events, and in a laboratory environment. Further, a demonstration of the real world e cacy of the approach for professional performers is presented in the form of sales of the tricks in a reputable magic shop in London.Engineering and Physical Sciences Research Council (EPSRC), grant number EP/J50029X/1.