Context learning for threat detection

It is hypothesised that threatening stimuli are detected better due to their salience or physical properties. However, these stimuli are typically embedded in a rich context, motivating the question whether threat detection is facilitated via learning of contexts in which threat stimuli appear. To address this question, we presented threatening face targets in new or old spatial configurations consisting of schematic faces and found that detection of threatening targets was faster in old configurations. This indicates that individuals are able to learn regularities within visual contexts and use this contextual information to guide detection of threatening targets. Next, we presented threatening and non-threatening face targets embedded in new or old spatial configurations. Detection of threatening targets was facilitated in old configurations, and this effect was reversed for non-threatening targets. Present findings show that detection of threatening targets is driven not only by stimulus properties as theorised traditionally but also by learning of contexts in which threatening stimuli appear. Further, results show that context learning for threatening targets obstructs context learning for non-threatening targets. Overall, in addition to typically emphasised bottom-up factors, our findings highlight the importance of top-down factors such as context and learning in detection of salient, threatening stimuli.     

Sequential detection of learning in cognitive diagnosis

In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broadly defined skills, when the objective is to hasten learning. If these fine‐grained skills can be identified, a sharpened focus on learning and remediation can be achieved. The focus here is on how to detect when learning has taken place for a particular attribute and efficiently guide a student through a sequence of items to ultimately attain mastery of all attributes while administering as few items as possible. This can be seen as a problem in sequential change‐point detection for which there is a long history and a well‐developed literature. Though some ad hoc rules for determining learning may be used, such as stopping after M consecutive items have been successfully answered, more efficient methods that are optimal under various conditions are available. The CUSUM, Shiryaev–Roberts and Shiryaev procedures can dramatically reduce the time required to detect learning while maintaining rigorous Type I error control, and they are studied in this context through simulation. Future directions for modelling and detection of learning are discussed.     

Implicit learning for probable changes in a visual change detection task

Previous research demonstrates that implicitly learned probability information can guide visual attention. We examined whether the probability of an object changing can be implicitly learned and then used to improve change detection performance. In a series of six experiments, participants completed 120–130 training change detection trials. In four of the experiments the object that changed color was the same shape (trained shape) on every trial. Participants were not explicitly aware of this change probability manipulation and change detection performance was not improved for the trained shape versus untrained shapes. In two of the experiments, the object that changed color was always in the same general location (trained location). Although participants were not explicitly aware of the change probability, implicit knowledge of it did improve change detection performance in the trained location. These results indicate that improved change detection performance through implicitly learned change probability occurs for location but not shape.     

Application of deep transfer learning for automated brain abnormality classification using MR images

Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.     

Memory for dangers past: threat contexts produce more consistent learning than do non-threatening contexts

In earlier work we showed that individuals learn the spatial regularities within contexts and use this knowledge to guide detection of threatening targets embedded in these contexts. While it is highly adaptive for humans to use contextual learning to detect threats, it is equally adaptive for individuals to flexibly readjust behaviour when contexts once associated with threatening stimuli begin to be associated with benign stimuli, and vice versa. Here, we presented face targets varying in salience (threatening or non-threatening) in new or old spatial configurations (contexts) and changed the target salience (threatening to non-threatening and vice versa) halfway through the experiment to examine if contextual learning changes with the change in target salience. Detection of threatening targets was faster in old than new configurations and this learning persisted even after the target changed to non-threatening. However, the same pattern was not seen when the targets changed from non-threatening to threatening. Overall, our findings show that threat detection is driven not only by stimulus properties as theorised traditionally but also by the learning of contexts in which threatening stimuli appear, highlighting the importance of top-down factors in threat detection. Further, learning of contexts associated with threatening targets is robust and speeds detection of non-threatening targets subsequently presented in the same context.     

Maternal responsiveness and search for hidden objects and contingency learning by infants

The relationship between maternal responsiveness and infant cognition was examined during two activities: the search for hidden objects and the learning of a contingency rule. Thirty-four mother–infant dyads were observed in a laboratory setting when the infants were 11 months old. The experimental session included three phases: a search for hidden objects (Piagetian tasks), the learning of a contingency rule on a touch screen, and a mother–infant play session using a standardised toy. The results indicated a link between performances in the search and contingency tasks. Moreover, infants who succeeded in both tasks had mothers who displayed higher responsiveness score. The findings are discussed in terms of the infant's detection of relevant stimulus information. © 1998 John Wiley & Sons, Ltd.     

Electrifying diagrams for learning: principles for complex representational systems

Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, Watts). A task analysis is presented that demonstrates that problem solving using a conventional algebraic approach demands more effort than AVOW diagrams. In an experiment comparing two groups of learners using the alternative approaches, the group using AVOW diagrams learned more than the group using equations and were better able to solve complex transfer problems and questions involving multiple constraints. Analysis of verbal protocols and work scratchings showed that the AVOW diagram group, in contrast to the equations group, acquired a coherently organised network of concepts, learnt effective problem solving procedures, and experienced more positive learning events. The six principles of effective representations were proposed on the basis of these findings. AVOW diagrams are Law Encoding Diagrams, a general class of representations that have been shown to support learning in other scientific domains.     

Improving reading outcomes for children with learning disabilities: using brief experimental analysis to develop parent-tutoring interventions

The present study assessed the effects of summer parent tutoring on 3 children with learning disabilities using empirically derived reading interventions. Brief experimental analyses were used to identify customized reading fluency interventions. Parents were trained to use the intervention strategies with their children. Parents implemented the procedures during parent-tutoring sessions at home and results were measured continuously in high-word-overlap and low-word-overlap passages to determine whether generalization occurred. Parent and child satisfaction with the procedures was assessed. Results demonstrated generalized increases in reading fluency in both high-word-overlap and low-word-overlap passages as a function of parent tutoring. Also, acceptability ratings by children and their parents indicated that they viewed the interventions as acceptable and effective. Results are discussed in terms of structuring reading fluency interventions that promote generalization and maintenance of treatment effects.