The effects of categorical similarity and feature-space proximity on visual working memory processing

Two patterns of data predict that similarity has a positive effect and a negative effect on visual working memory (VWM) processing. We assume that these two empirical outcomes do not distinguish categorical similarity from feature-space proximity, resulting in this divergence. To investigate how categorical similarity and feature-space proximity modulate VWM, we tested memory for an array of pictures drawn from either mixed categories or a single category in which feature-space proximity varied along a morph continuum in a change-detection task. We found that memory under the mixed-category condition was better than that under the single-category condition, whereas memory under high feature-space proximity was superior to that under low feature-space proximity. These patterns were unaffected by manipulations of stimulus type (faces or scenes), encoding duration (limited or self-paced), and presentation format (simultaneous or sequential). These results are consistent with our hypotheses that categorical similarity inhibits VWM, whereas feature-space proximity facilitates VWM. We also found that memory for items with low feature-space proximity benefited more from mixed-category encoding than that for items with high feature-space proximity. Memory for faces benefited more from mixed-category encoding than scenes, whereas memory for scenes benefited more from feature-space proximity than faces. These results suggest that centre-surround inhibition organization might underlie similarity effects in VWM. Centre-surround inhibition organization for complex real-world objects could have both categorical level and feature-space level. The feature-space level might differ by category.     

Où en est t’on du Big Data en médecine ?

Opening of medical datas and files is nowadays an important challenge. French Government increments new rules to allow access to these data files.     

Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea

One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models were used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to “driving words.” The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.     

TDQ-60 – a color picture-naming test for adults and elderly people: validation and normalization data

Word-finding difficulties are usually assessed with picture-naming tests. In this article, we present the TDQ-60, a new test designed to assess acquired lexical access deficits, taking into account semantics and psycholinguistic variables. The article includes three studies. Study 1 describes the development phase of the TDQ-60. In study 2, healthy control participants and individuals with a diagnosis of the semantic variant of primary progressive aphasia were assessed to establish the convergent and discriminant validity of the TDQ-60. Finally, in Study 3, a group of 305 young and elderly French-speaking adults from Quebec were assessed in order to provide normative data. The results demonstrate that the TDQ-60 has good convergent validity and good discriminant validity. This study also provides normative data in which were considered the effect of age and education.The TDQ-60 is a new valid picture-naming test, controlled for psycholinguistic variables and designed to identify the influence of semantics on lexical access in spoken production.     

The Quantified Relationship

The growth of self-tracking and personal surveillance has given rise to the Quantified Self movement. Members of this movement seek to enhance their personal well-being, productivity, and self-actualization through the tracking and gamification of personal data. The technologies that make this possible can also track and gamify aspects of our interpersonal, romantic relationships. Several authors have begun to challenge the ethical and normative implications of this development. In this article, we build upon this work to provide a detailed ethical analysis of the Quantified Relationship (QR). We identify eight core objections to the QR and subject them to critical scrutiny. We argue that although critics raise legitimate concerns, there are ways in which tracking technologies can be used to support and facilitate good relationships. We thus adopt a stance of cautious openness toward this technology and advocate the development of a research agenda for the positive use of QR technologies.     

A Large‐Scale Analysis of Variance in Written Language

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, 2007 ; Jones & Mewhort, 2007 ; Landauer & Dumais, 1997 ; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013 ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing.     

Functional analysis screening for multiple topographies of problem behavior

The current study evaluated a screening procedure for multiple topographies of problem behavior in the context of an ongoing functional analysis. Experimenters analyzed the function of a topography of primary concern while collecting data on topographies of secondary concern. We used visual analysis to predict the function of secondary topographies and a subsequent functional analysis to test those predictions. Results showed that a general function was accurately predicted for five of six (83%) secondary topographies. A specific function was predicted and supported for a subset of these topographies. The experimenters discuss the implication of these results for clinicians who have limited time for functional assessment.     

Compositional insights on the association between physical activity and sedentary behavior on momentary mood in daily life

ObjectiveGiven the increasing number of mental disorders, a growing body of studies now focuses on the relationship between physical activity (PA), sedentary behavior (SB), and mental health. However, momentary mechanisms and interrelatedness between PA, SB, and mood in daily life are highly understudied.MethodsTo investigate whether the composition of light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and SB influences mood, we conducted an Ambulatory Assessment study in the everyday life of 103 university students over five days. We measured PA and SB via accelerometers and assessed mood up to six times each day on smartphone diaries. We combined compositional data analyses (CoDA) with multilevel modeling to analyze within-person effects of the behavioral composition on mood.ResultsHigher ratio of the SB to LPA and MVPA composition within the 60 min prior to a diary rating negatively influenced valence (p = 0.006) and energetic arousal (p < 0.001). Furthermore, a higher ratio of LPA to SB and MVPA positively influenced energetic arousal (p < 0.001) and a higher ratio of MVPA to SB and LPA positively influenced valence (p = 0.004) and energetic arousal (p = 0.022). Simulation analyses revealed that replacing 20 min of SB with PA can influence mood rating up to 3.39 units [range from 0–100].ConclusionsFindings suggest that minutely replacements of SB with PA may lead to mood enhancements. Given the high prevalence of mental disorders, more studies are warranted to deepen the understanding of momentary compositional mechanisms between SB, PA, and mood. Applying CoDA to intensive longitudinal data can serve as a starting point to identify the optimal composition of SB, LPA, and MVPA for mood enhancements in everyday life.     

国内中介效应的方法学研究

中介效应可以分析自变量对因变量的影响过程和作用机制, 已成为分析多个变量之间关系的一种重要统计方法。最近20年, 中介效应成了研究方法的一个热点。从中介效应的检验方法、效应量、类别变量的中介效应检验、纵向数据的中介效应检验和模型拓展(包括多重中介、多层中介、有调节的中介和有中介的调节模型)五个方面系统总结了国内中介效应的方法学研究的发展历程。最后对中介效应的国外方法学研究进展和中介效应的未来研究方向做了讨论和拓展。