A cognitive framework for object recognition with application to autonomous vehicles

Autonomous vehicles or self-driving cars are capable of sensing the surrounding environment so they can navigate roads without human input. Decisions are constantly made on sensing, mapping and driving policy using machine learning techniques. Deep Learning – massive neural networks that utilize the power of parallel processing – has become a popular choice for addressing the complexities of real time decision making. This method of machine learning has been shown to outperform alternative solutions in multiple domains, and has an architecture that can be adapted to new problems with relative ease. To harness the power of Deep Learning, it is necessary to have large amounts of training data that are representative of all possible situations the system will face. To successfully implement situational awareness in driverless vehicles, it is not possible to exhaust all possible training examples. An alternative method is to apply cognitive approaches to perception, for situations the autonomous vehicles will face. Cognitive approaches to perception work by mimicking the process of human intelligence – thereby permitting a machine to react to situations it has not previously experienced. This paper proposes a novel cognitive approach for object recognition. The proposed cognitive object recognition algorithm, referred to as Recognition by Components, is inspired by the psychological studies pertaining to early childhood development. The algorithm works by breaking down images into a series of primitive forms such as square, triangle, circle or rectangle and memory based aggregation to identify objects. Experimental results suggest that Recognition by Component algorithm performs significantly better than algorithms that require large amounts of training data

Technical and conceptual considerations for using animated stimuli in studies of animal behavior

Rapid technical advances in the field of computer animation (CA) and virtual reality (VR) have opened new avenues in animal behavior research. Animated stimuli are powerful tools as they offer standardization, repeatability, and complete control over the stimulus presented, thereby "reducing" and "replacing" the animals used, and "refining" the experimental design in line with the 3Rs. However, appropriate use of these technologies raises conceptual and technical questions. In this review, we offer guidelines for common technical and conceptual considerations related to the use of animated stimuli in animal behavior research. Following the steps required to create an animated stimulus, we discuss (I) the creation, (II) the presentation, and (III) the validation of CAs and VRs. Although our review is geared toward computer-graphically designed stimuli, considerations on presentation and validation also apply to video playbacks. CA and VR allow both new behavioral questions to be addressed and existing questions to be addressed in new ways, thus we expect a rich future for these methods in both ultimate and proximate studies of animal behavior

Is Navigation in Virtual Reality with fMRI Really Navigation?

Identifying the neural mechanisms underlying spatial orientation and navigation has long posed a challenge for researchers. Multiple approaches incorporating a variety of techniques and animal models have been used to address this issue. More recently, virtual navigation has become a popular tool for understanding navigational processes. Although combining this technique with functional imaging can provide important information on many aspects of spatial navigation, it is important to recognize some of the limitations these techniques have for gaining a complete understanding of the neural mechanisms of navigation. Foremost, among these is that, when participants perform a virtual navigation task in a scanner, they are lying motionless in a supine position while viewing a video monitor. Here, we provide evidence that spatial orientation and navigation rely to a large extent on locomotion and its accompanying activation of motor, vestibular, and proprioceptive systems. Researchers should therefore consider the impact on the absence of these motion-based systems when interpreting virtual navigation/functional imaging experiments to achieve a more accurate understanding of the mechanisms underlying navigation

Antecedents and consequences of employees’ adjustment to overseas assignment: a meta‐analytic review

Research on the antecedents and consequences of expatriate adjustment was reviewed using meta-analytic methods. The antecedents and outcomes of three facets of adjustment were examined. Self-efficacy, frequency of interaction with host nationals, and family support consistently predicted all three types of adjustment. In addition, better interpersonal skills were associated with greater adjustment to general environment. Greater cultural novelty was associated with less interactional adjustment. Role conflict, ambiguity, and discretion were also strong predictors of work adjustment. A structural equations model that illustrated causal relationships involving expatriate adjustment and outcomes of job strain, job satisfaction, organisational citizenship, intent to turnover, and job performance generated a good fit with the data