Line-up Member Similarity Influences the Effectiveness of a Salient Rejection Option for Eyewitnesses

Visually salient line-up rejection options have not been systematically studied with adult eyewitnesses. We explored the impact of using a non-verbal, salient rejection option on adults' identification accuracy for line-ups containing low- or high-similarity fillers. The non-verbal, salient rejection option had minimal impact on accuracy in low-similarity line-ups, but in high-similarity line-ups its inclusion increased correct rejections for target-absent line-ups as well as incorrect rejections in target-present line-ups, relative to a verbal rejection condition. The improved performance in target-absent line-ups suggests that adults, like children, may experience pressure to choose and guess during difficult tasks. This pressure is reduced when a prominent non-verbal rejection option is displayed in the line-up. However, the salient rejection option also appears to increase the attractiveness of avoiding a difficult choice between the target and highly similar fillers. Implications of these findings for the experimental literature and justice system are discussed.     

On evaluation of consistency in multi-regional clinical trials

In recent years, multi-regional clinical trials (MRCT) that conduct clinical trials simultaneously in Asian Pacific region, Europe, and the United States have become very popular for global pharmaceutical development. The main purpose of multi-regional clinical trials is to shorten the time for pharmaceutical development and regulatory submission, and approval around the world. In practice, however, clinical results observed from some regions (sub-population) may not be consistent with the results from other regions and/or all regions combined (entire population). The inconsistency observed may be due to ethnic differences in different regions, differences in medical practice, time points of assessment, or by random chance due to small sample size for the region. Some regional regulatory agencies require consistency evaluation between local country results and overall results. However, the challenge is there is no detailed guidance on the definition of ‘consistency’ and methodology to evaluate it. Therefore, the questions are: how to evaluate consistency and what statistical methods are appropriate to be used for consistency evaluation? In this article, several statistical tests for consistency (similarity) between clinical results observed from a specific sub-population and the entire population are proposed. These methods are compared through extensive simulation. As most published articles discussed consistency evaluation for superiority situations, we have discussed consistency evaluation for non-inferiority situation in this article through a simulated example concerning consistency in some countries. Recommendations of the statistical methods to be used for consistency evaluation are given. Other aspects that should be considered for consistency evaluation are also provided.     

Decoding the information structure underlying the neural representation of concepts

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

The capacity for conceptual knowledge is arguably one of the most defining properties of human cognition, and yet it is still unclear how concepts are represented in the brain. Recent developments in functional neuroimaging and computational linguistics have sparked renewed interest in elucidating the information structures and neural circuits underlying concept representation (1–5). Attempts to characterize the representational code for concepts typically involve information structures based on three qualitatively distinct types of information, namely, taxonomic, experiential, and distributional information. As the term implies, a taxonomic information system relies on category membership and intercategory relations. Our tendency to organize objects, events, and experiences into discrete categories has led most authors—dating back at least to Plato (6)—to take taxonomic structure as the central property of conceptual knowledge (7). The taxonomy for concepts is traditionally seen as a hierarchically structured network, with basic-level categories (e.g., “apple,” “orange”) grouped into superordinate categories (e.g., “fruit,” “food”) and subdivided into subordinate categories (e.g., “Gala apple,” “tangerine”) (8). A prominent account in cognitive science maintains that such categories are represented in the mind/brain as purely symbolic entities, whose semantic content and usefulness derive primarily from how they relate to each other (9, 10). Such representations are seen as qualitatively distinct from the sensory-motor processes through which we interact with the world, much like the distinction between software and hardware in digital computers.An experiential representational system, on the other hand, encodes information about the experiences that led to the formation of particular concepts. It is motivated by a view, often referred to as embodied, grounded, or situated semantics, in which concepts arise primarily from generalization over particular experiences, as information originating from the various modality-specific systems (e.g., visual, auditory, tactile, motor, affective) is combined and re-encoded into progressively more schematic representations that are stored in memory. Since, in this view, there is a degree of continuity between conceptual and modality-specific systems, concept representations are thought to reflect the structure of the perceptual, affective, and motor processes involved in those experiences (11–14).Finally, distributional information pertains to statistical patterns of co-occurrence between lexical concepts (i.e., concepts that are widely shared within a population and denoted by a single word) in natural language usage. As is now widely appreciated, these co-occurrence patterns encode a substantial amount of information about word meaning (15–17). Although word co-occurrence patterns primarily encode contextual associations, such as those connecting the words “cow,” “barn,” and “farmer,” semantic similarity information is indirectly encoded since words with similar meanings tend to appear in similar contexts (e.g., “cow” and “horse,” “pencil” and “pen”). This has led some authors to propose that concepts may be represented in the brain, at least in part, in terms of distributional information (15, 18).Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessments of patients with semantic deficits, with results typically interpreted in terms of taxonomic (19–24), experiential (13, 25–34), or distributional (2, 3, 5, 35, 36) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial (23, 37, 38), in part, because their representations of common lexical concepts are strongly intercorrelated. Patterns of word co-occurrence in natural language are driven in part by taxonomic and experiential similarities between the concepts to which they refer, and the taxonomy of natural categories is systematically related to the experiential attributes of the exemplars (39–41). Consequently, the empirical evidence currently available is unable to discriminate between these representational systems.Several computational models of concept representation have been proposed based on these structures. While earlier models relied heavily on hierarchical taxonomic structure (42, 43), more recent proposals have emphasized the role of experiential and/or distributional information (34, 44–46). The model by Chen and colleagues (45), for example, showed that graded taxonomic structure can emerge from the statistical coherent covariation found across experiences and exemplars without explicitly coding such taxonomic information per se. Other models propose that concepts may be formed through the combination of experiential and distributional information (44, 46), suggesting a dual representational code akin to Paivio’s dual coding theory (47).We investigated the relative contribution of each representational system by deriving quantitative predictions from each system for the similarity structure of a large set of concepts and then using representational similarity analysis (RSA) with high-resolution functional MRI (fMRI) to evaluate those predictions. Unlike the more typical cognitive subtraction technique, RSA focuses on the information structure of the pattern of neural responses to a set of stimuli (48). For a given stimulus set (e.g., words), RSA assesses how well the representational similarity structure predicted by a model matches the neural similarity structure observed from fMRI activation patterns (Fig. 1). This allowed us to directly compare, in quantitative terms, predictions derived from the three representational systems.Open in a separate windowFig. 1.Representational similarity analysis. (A) An fMRI activation map was generated for each concept presented in the study, and the activation across voxels was reshaped as a vector. (B) The neural RDM for the stimulus set was generated by computing the dissimilarity between these vectors (1 − correlation) for every pair of concepts. (C) A model-based RDM was computed from each model, and the similarity between each model’s RDM and the neural RDM was evaluated via Spearman correlation. (D) Anatomically defined ROIs. The dashed line indicates the boundary where temporal lobe ROIs were split into anterior and posterior portions (see main text for acronyms). (E) Cortical areas included in the functionally defined semantic network ROI (49).     

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.     

An Insight into the Molecular Characteristics and Associated Pathology of Chicken Astroviruses

The chicken astrovirus (CAstV) is a ubiquitous enteric RNA virus that has been associated mainly with conditions, such as the runting-stunting syndrome, severe kidney disease, visceral gout, and white chick syndrome, in broiler-type chickens worldwide. Sequence analysis of the capsid genes’ amino acids of the strains involved in these conditions reveals a genetic relationship and diversity between and within the CAstV genogroups and subgroups based on phylogenetic analysis, genetic distance (p-dist), and pathogenicity. While the two genogroups (A and B) are demarcated phylogenetically, their pairwise amino acid sequence identity is 39% to 42% at a p-dist of 0.59 to 0.62. Group-A consists of three subgroups (Ai, Aii, and Aiii) with an inter- and intra-subgroup amino acid identity of 78% to 82% and 92% to 100%, respectively, and a p-dist of 0.18 to 0.22. On the other hand, the six subgroups (Bi, Bii, Biii, Biv, Bv, and Bvi) in Group-B, with a p-dist of 0.07 to 0.18, have an inter- and intra-subgroup amino acid identity of 82% to 93% and 93% to 100%, respectively. However, these groupings have little to no effect on determining the type of CAstV-associated pathology in chickens.     

Identification of 4‐aryl‐1H‐pyrrole[2,3‐b]pyridine derivatives for the development of new B‐Raf inhibitors

During the last years, a significant interest in the identification of new classes of B‐Raf inhibitors has emerged. In this study, which was conceived within an effort that culminated in the recent report of the first dual inhibitors of B‐Raf and Hsp90, we describe the identification of four compounds based on 4‐aryl‐1H‐pyrrole[2,3‐b]pyridine scaffold as interesting starting points for the development of new B‐Raf inhibitors. Structure–activity relationships and predicted binding modes are discussed. Moreover, the novelty of the newly identified structures with respect to currently known B‐Raf inhibitors was assessed through a ligand‐based dissimilarity assessment. Finally, structural modifications with the potential ability to improve the activity toward B‐Raf are put forward.     

白桦总三萜类成分高效液相色谱指纹图谱研究

目的建立白桦总三萜类成分的HPLC指纹图谱分析方法,研究不同采收期白桦药材的质量。方法以Kromasil C18柱（4.6mm×250mm,5μm）为色谱柱,流动相乙腈-水,采用梯度洗脱,流速为1.0ml/min,柱温25℃,检测波长280nm;进样量20μl;检测时间60min,对10批白桦总三萜类成分的HPLC指纹图谱进行研究,并运用相似度评价法对数据进行分析。结果实验标定9个共有峰,不同采收期的白桦指纹图谱相似度较好。结论该方法准确可靠,重复性好,用于白桦的质量评价切实可行;不同采收期白桦总三萜类化学组成相似,其相对比例较稳定。     

低阻抗意念导入疗法与催眠疗法的异同点分析

低阻抗意念导入疗法要求患者处于低阻抗状态时进行治疗,而低阻抗状态是指从清醒到睡眠这个过程的某种中间状态,这与催眠状态存在相似之处.为了更好地区分低阻抗意念导入疗法与催眠疗法,将对两者的异同点进行分析,以便读者更好地对低阻抗意念导入疗法进行把握.     

基于EEG模糊相似性的癫痫发作预测

本研究提出基于EEG序列模糊相似性指数方法预测癫痫发作.首先,结合复自相关法和Cao法对EEG序列进行了相空间重构;然后,计算相关积分时用Gaussian函数代替Heavyside函数,克服了Heavyside函数的刚性边界问题,使得计算相似性指数更加准确和可靠;最后,分析大鼠癫痫EEG信号,检测癫痫发作前期状态.分析结果表明模糊相似性指数方法能够比动态相似性指数方法获得更长的预测时间和更低的错误预测率.