Using forced choice to test belief bias in syllogistic reasoning

In deductive reasoning, believable conclusions are more likely to be accepted regardless of their validity. Although many theories argue that this belief bias reflects a change in the quality of reasoning, distinguishing qualitative changes from simple response biases can be difficult (Dube, Rotello, & Heit, 2010). We introduced a novel procedure that controls for response bias. In Experiments 1 and 2, the task required judging which of two simultaneously presented syllogisms was valid. Surprisingly, there was no evidence for belief bias with this forced choice procedure. In Experiment 3, the procedure was modified so that only one set of premises was viewable at a time. An effect of beliefs emerged: unbelievable conclusions were judged more accurately, supporting the claim that beliefs affect the quality of reasoning. Experiments 4 and 5 replicated and extended this finding, showing that the effect was mediated by individual differences in cognitive ability and analytic cognitive style. Although the positive findings of Experiments 3–5 are most relevant to the debate about the mechanisms underlying belief bias, the null findings of Experiments 1 and 2 offer insight into how the presentation of an argument influences the manner in which people reason.     

To see dynamic change: continuous focused attention facilitates change detection,but the effect persists briefly

What is the role of continuously focused attention on an object in change detection? To ensure focused attention on one object, we conducted a single object change detection task, manipulating an object’s location between pre-change and post-change displays (same or different location), and also manipulating a blank duration (the FOD task) and a pre-change object presentation duration (the FBD task). If attention is continuously focused at the spatial location of the pre-change object, a location shift of the post-change object should interrupt change detection due to a cognitive cost of attentional shift. Results suggest attention is focused continuously for a brief blank duration, and attention can facilitate the detection of change occurring at the location of attentional focus. Additionally, although attention is focused continuously for a long time if a target is visible, the effect of attention declines with time. The results clarify the new temporal characteristics of focused attention.     

Novel object detection and recognition system based on points of interest selection and SVM classification

Due to the progression in computer vision technology, object recognition systems have gained considerable research interest. Though there are numerous object recognition systems in the literature, there is always a constant demand for better object recognition systems. Taking this as a challenge, this work proposes a novel object recognition system based on points of interest and feature extraction. Initially, the points of interest of the image are selected by means of Derivative Kadir-Brady (DKB) detector and the neighbourhood pixels of a particular window size are selected for further processing. The gabor and curvelet features are extracted from the area of interest, followed by the Support Vector Machine (SVM) classification. The performance of the proposed object recognition system is evaluated against three analogous techniques in terms of accuracy, precision, recall and F-measure. On experimental analysis, it is proven that the proposed approach outperforms the existing approaches and the performance of the proposed work is satisfactory.     

Wild facial expression recognition based on incremental active learning

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.     

An illumination robust road detection method based on color names and geometric information

Road detection is a basic task in automated driving field. In existing methods, detecting lane marks is a frequently used approach. However, road marks could fade with time, or even do not exist on some roads. Considering these factors, road region segmentation from background is much more reliable. One of the difficulties in this area is dealing with variant illumination conditions. Existing methods make some progress on reducing the influence of shadows, but they are still not satisfactory. Taking this into account, we put forward an improved shadow-free road detecting method based on color names, whose performance exceeds the existing methods. In addition, to further improving our performance on application, we adopt vanishing point detection and fuse two confidence maps to reduce the interference of sidewalk regions. Experiments on KITTI dataset depict that the proposed method is efficient. Moreover, the improved road detection method has low-complexity, which meets the requirement of practical usage.     

Drawing what lies ahead: False intentions are more abstractly depicted than true intentions

The aim of this study was to examine how people mentally represent and depict true and false statements about claimed future actions—so‐called true and false intentions. On the basis of construal level theory, which proposes that subjectively unlikely events are more abstractly represented than likely ones, we hypothesized that false intentions should be represented at a more abstract level than true intentions. Fifty‐six hand drawings, produced by participants to describe mental images accompanying either true or false intentions, were rated on level of abstractness by a second set of participants (N = 117) blind to the veracity of the intentions. As predicted, drawings of false intentions were rated as more abstract than drawings of true intentions. This result advances the use of drawing‐based deception detection techniques to the field of true and false intentions and highlights the potential for abstractness as a novel cue to deceit.     

Facilitating memory‐based lie detection in immediate and delayed interviewing: The role of mnemonics

We experimentally investigated how different mnemonic techniques employed in an interview conducted immediately after an event affected truth tellers' and liars' responses when they were interviewed again after a 2‐week delay. We also compared how verbal accounts changed over time within truth tellers and liars, and how consistent both groups were. Participants (n = 143) were shown a mock intelligence operation video and instructed either to tell the truth or lie about its contents in two interviews, one of which was immediately after watching the video and the other after a 2‐week delay. In the immediate interview, they were asked to provide a free recall and then asked to provide further information via one of three mnemonics: context reinstatement, sketch, or event‐line. In the delayed interview, they were asked to provide only a free recall. Truth tellers reported more visual, spatial, temporal, and action details than did liars both immediately and after a delay. Truth tellers experienced more of a decline in reporting details after a delay than did liars, and this decline was affected by the mnemonic used. Truth tellers thus showed, more than liars, patterns of reporting indicative of genuine memory decay. Liars produced patterns of a “stability bias” instead. Truth tellers and liars were equally consistent between their immediate and delayed statements.     

目标决策还是动作诱发?动作反应对注意促进效应的影响

注意促进效应(ABE)指的是在双任务条件下, 伴随探测任务的目标刺激呈现的背景信息记忆成绩要优于伴随分心刺激呈现的背景信息记忆成绩的现象。以往研究主张, ABE的产生主要源于目标决策时所诱发的注意增强。但由于目标探测往往伴随着动作反应, 而已有研究发现, 动作反应就能直接诱发背景信息的记忆增强效应, 因此ABE也可能源于动作诱发的记忆增强效应。为此, 研究设置了NoGo目标探测条件与Go目标探测条件, 通过4个实验系统地探讨了动作反应与目标决策在ABE产生中的作用及关系。结果表明, Go目标探测条件下的ABE是稳定的, 但NoGo目标探测条件下的ABE会受到动作反应频率对分心词的影响。此外, NoGo目标探测跨条件的ABE也是非常稳定的。这些结果表明, 目标决策产生的注意促进作用是相对稳定的, 但ABE的产生更多是目标决策的促进作用与动作诱发的记忆增强效应动态权衡后的结果。     

自由回忆中元记忆监控的作用及其影响因素

采用“类信号检测论方法”,通过两个实验,研究了在记忆信息提取过程中元记忆的预见性监测判断（Feeling-of-knowing）和元控制的作用及其影响因素。实验一探讨了识记材料的难易、识记遍数对元监测判断以及输出控制中的反应标准是否有影响;实验二探讨了严格和宽松的奖惩条件对输出控制中反应标准的影响。用计算机随机呈现识记用的词表材料,识记之后让被试进行FOK等级判断和自由回忆,再用“类信号检测论”方法统计实验数据,分析各种条件下回忆成绩、FOK判断等级,计算出相应的符合率和反应标准。实验一结果表明,不同材料难度和识记遍数下的回忆成绩和FOK判断等级差异显著,但输出控制中的反应标准值没受这些因素的影响;实验二结果是严格和宽松奖惩条件下的反应标准值差异显著,严格条件下的反应标准显著高于宽松条件下的反应标准,表明奖惩条件对信息提取中的反应标准有显著影响     

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.