dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the CARRI group's gradual gains in word reading fluency indicate that children at risk for reading disability can reach the level of mainstream students. However, they require much more time to reach that level. These Finnish results were replicated in a language with a non-transparent orthography (English,  Kyle et al. , 2013). However, the interpretation of the results of that study is limited by the fact that no individual data were provided and no fluency evaluations were performed. In another training study  (Brem et al. , 2010) , functional magnetic resonance imaging (fMRI) data were collected from 16 German-speaking kindergarteners from Switzerland trained with both GraphoGame and a non-linguistic number-knowledge control game (duration of each of the two training programs: less than 4 h per week over 8 weeks).
These results suggest that dyslexic children derive some benefits from this training, and that these benefits are also seen at the level of subcortical structures. In the same vein, other studies have attempted to develop procedures to improve the auditory-perceptual abilities of children with learning disabilities. Merzenich et al. (1996)  and  Tallal et al. (1996) , hypothesized that dyslexic children have a temporal processing disorder that could be remediated through auditory training, developed a computerized training program known as Fast ForWord ® (Scientific Learning Corporation, Oakland, CA, USA). The program consisted in a succession of tasks such as the comparison or identification of sounds, phonemes, syllables, and words with variations in acoustic parameters such as duration and frequency, and was recommended during a period of 6 to 8 weeks (100 min a day, 5 days a week).
(f ) Motor behaviour analysis The effect of motor behaviour on perceived surface displacement was analysed from the results of experiment 1. To this end, the finger pressing force was recorded at an 80 Hz sampling rate. The trial durations were measured from the instant the finger pressing force first crossed the 0. 5 N lower force limit to the instant when it dropped back to 0. 5 N. The numbers of trials were different for the two conditions, so the median values of the trial durations among trials corresponding to the same condition were computed. The non-parametric Friedman test was applied to the resulting data ('friedman. test' function of R statistical software).
(g) Testing methods and data analysis In experiment 1, immediately after each trial, the observers reported verbally in millimetres any perceived vertical displacement of their fingertip. The observers' verbal reports were recorded by the experimenter. Prior to the experiment, the rspb. royalsocietypublishing. org Proc. R. Soc. B 282: 20151661 observers inspected visually and haptically a vernier calliper which was opened at 1. 0, 5. 0 and 10. 0 mm in order to help them calibrate their subjective estimates. The Durbin test (non-parametric, balanced incomplete block design) was used to estimate the significance of the effect of the coefficient of modulation and of the upper force limit on the perceived displacement. For the control condition, the test was only used to assess the effect of the variation of the upper force limit.
For the main condition, the test was used to assess the effect of the upper force limit variation for a fixed coefficient of modulation. To assess the effect of the variation of the coefficient of modulation, the test was applied to the combined data for all upper force limits. Calculations were performed using the function 'durbin. test' of R statistical software (package 'agricolae'). In experiment 2, observers simply reported whether or not they felt their finger move. In experiment 3, the responses of each subject in the experiment block were fitted with probit models, one for in-phase and one for anti-phase condition for every subject. The PSEs were estimated to be the values of test stimuli for which the model gave 0. 5 probability for the test and reference conditions. The perceptual biases were computed by subtracting the reference displacement from the PSEs.
Acknowledgments DC is supported by the  Gordon and Betty Moore Foundation  through Grant  GBMF3834  and by the  Alfred P. Sloan Foundation  though Grant  2013-10-27. FS is financed by  ERC Advanced  Grant  323674  "FEEL". SM is supported by  NIMH  grant  R01 MH078160-07. DC and SM are supported by  R21 MH104651-01. We thank  Jeff Haller  for providing recordings of the ATM lessons discussed in Basic Coordination: Inhibiting Muscular Contraction and Coordinating attention: Motoric mind wandering as a context for practice in Figure 3 and supplementary material. Thanks to  Ed Olmstead  for his work on study design and video editing in the fireman project ( Figure 3C  and Supplementary Video 4). Thanks to  Roger Russell  for permission to reprint illustrations of ATM positions in Figures 1, 2 from Russell (2014).
Method Participants Participants were thirty-four full-term, monolingual Japanese 14-month-olds (M = 14 months; 16 days, range 13;27-15;09, 22 males). Infants were randomly assigned to either the match or mismatch condition (match: n = 17, M = 14:17, 11 males; mismatch: n = 17, M = 14:19, 11 males). An additional 11 infants were excluded from data analyses due to experimental error (n = 1), or fussiness (i. e. , inability to complete the experiment due to crying or movement out of range of the camera [see Procedure], n = 10). All participants were recruited from the participant pool of Tamagawa University (Tokyo, Japan) via telephone calls or email. All adults and parents of infants gave written informed consent before participating in the study. This study was approved by the Ethics Committee of Tamagawa University.
Vocabulary analysis The J-MCDI data were obtained from 28 out of 34 parents of infants by mail within one month after the infants' participation of the experiment. Comprehension on the J-MCDI ranged from 9 to 287 words (Mdn = 51, M = 72. 3, SD = 68. 1) and reported production ranged from 0 to 73 words (Mdn = 3, M = 9. 3, SD = 16. 1). Comprehension of the filler words was at 50. 9%, meaning that, on average, infants knew only 2 out of four filler words. Thus, although the norms on the J-MCDI suggested that a majority of 14-month-old Japanese infants should know the words used for the filler trials, these words were not so familiar to the infants in our sample. Moreover, infants in match and mismatch conditions did not differ on their vocabulary size in either comprehension or expression, and did not differ on the reported number of known filler words (the Mann-Whitney U test was used, since MCDI scores were non-normally distributed: comprehension, U = 79.
The patterns observed in Fig. 3  indicate otherwise, but the time window analysis used in previous work assessing preferential looking in infants cannot statistically separate these two interpretations. To circumvent the problems described above, we conducted a Bayesian analysis similar to the one proposed by Yurovsky et al. [45]. Their analysis has at least three advantages over traditional linear models for looking-time data. First, looking behavior is analyzed as a continuous time series. This means that, instead of averaging the looking time over a pre-determined timewindow, all the frame-by-frame data points are used for the analysis. The analysis specifies an optimal "time window" for a given experimental setting through parameter estimation of the dataset. Second, instead of assuming a linear increase or decrease of looking time, this analysis considers the probability of looking as a polynomial function of time with multiple factors as its coefficients.
In analyzing our data, hypothetical relationships among a set of experimental factors (i. e. , effects due to experimental manipulations: training, sound symbolism at test, and their interaction), subsidiary factors (i. e. , a bias to look toward an specific object or a specific location in the monitor), and potential patterns of behaviors (i. e. , looking time) are expressed as a statistical model with a set of parameters, which is estimated through model fitting to a dataset. The model consists of a set of parameters, which also include preference parameters for factors that could potentially affect the looking of a particular area of interest (AOI) at a given moment: individual infants' preference to look at a particular location (i. e. , left or right or neither), a preference to look at a particular object, a preference to look at the "correct" object with which the label was associated, or a preference to look at the object that was sound symbolically matching.
Results of the Bayesian analysis We modeled the probability of looking at an object (after the onset of the target word) as a function of its various properties: the three experimental factors (training, sound-symbolic match at test, and the training-sound-symbolism interaction), whose influence was assumed to change as a function of time within a trial, two subsidiary preference factors (object shape and object location), assumed to be constant over time. We evaluated whether the model should include all three experimental factors and what type of time-dependency of the three experimental factors (a linear, quadratic, or cubic function of time) was optimal. To compare these possible models, we ran a series of (Bayesian) models that examined experiment-related factors (see Table  1 ). In Bayesian hypothesis testing, each model specifies a probability for the hypothesis to reproduce the current dataset.
Through the process of model fitting, we estimated a set of parameters for all of the five factors described above (i. e. , three experimental factors and two factors reflecting individual looking biases), but we report only the experiment-relevant factors here (i. e. , training, sound-symbolic match at test, and training-sound-symbolism interaction factors). The effect of each factor is evaluated by a Bayes factor  ([46]  See also  [47]  for a review in psychological studies). According to  Jeffreys (1961) , "[t]he Bayes factor (BF) X of Model A to B given a dataset indicates that the odds ratio for Model A to reproduce the dataset is X times higher than that for Model B to do so under an even prior probability for each model. " Here we use a derivative of the BF, the log-BFβ, which indicates that the probability of reproducing the data is exp(β) times higher as compared to the base model.
Data Extraction and Analysis Of the 216 trials obtained from the participating infants, 18 had to be excluded due to the infant failing to perform reaching movements, inattentiveness (looking away from the object), technical difficulties (object falling off the attractor magnet), or fussiness. Of the remaining 198 trials, 12 reaches led to no attempt to grasp and 186 led to unimanual grasping or attempts to grasp. For all infants, one hand could always be identified touching the object before the other and, thus, reaching and grasping were consistently considered as unimanual movements. For each reaching trial, reaching onset, hand use, object start, object touch, and, if applicable, object grasp, and hand position at grasp, were identified by frame-by-frame analyses (25 frames/s). Object start was determined as the first frame when the object moved toward the infant.
Study Aims Running Head: VICTIMISATION AND OBESITY: LONGITUDINAL ASSOCIATIONS 9 There is cross-sectional and prospective evidence of associations between higher BMI and victimization in children aged more than seven years, pre-adolescence and adolescence, but there are no examinations of these relations in children below that age. Further, there are no prospective examinations of lower BMI and victimization, and no studies utilising longitudinal designs that examine bidirectional effects between BMI and victimization over more than 3 years. In the current study, we used a longitudinal design over seven years with annual data collection for children aged three-to-ten years. We first used curvilinear analyses to investigate whether higher and lower BMI puts a child at risk of victimization by their peers.
8% girls) took part in at Time 1 by providing informed consent. The current study used data from the fourth annual wave of data collection (T4) through to the tenth wave of data collection (T10) 1 from the QLSCD. By T4, when the children were on average 3. 4 years of age, 1950 participants provided data; by T10, when the children were on average 10. 1 years of age, 1334 participants provided data. In order to minimize the bias associated with missing data, we used the expectation maximization (EM) algorithm to impute missing data  (Schafer & Graham, 2002). Only individuals who participated in five out of the seven measurement waves from the 4 th -10 th wave of data collection were included in the present study, and a total of 1344 children (girls = 706; boys = 638) met these criteria. Participants with and without complete data were compared using  Little's (1988)  Missing Completely At Random (MCAR) test.
Measures Body Mass Index (BMI) A trained interviewer measured children's height and weight at home at each data collection period (as per  Desrosiers, Dubois, & Bédard, 2005; Dubois & Girard, 2007). A standardized protocol was followed when height and weight were measured, with participants wearing their clothes, but not wearing their shoes. BMI was calculated as weight [kg]/height [m] 2 , and these BMI values were converted to BMI z-scores using national reference data to adjust for age and sex. The practice of standardizing BMI score based on age-and sexspecific growth charts is recommended by the Centers for Disease Control and Prevention 1 This tenth wave of data collection is noted as T11 in the QLSCD archive because it occurs two years after the previous time point (T9). For ease of understanding, we refer to this time point as T10 in the current paper.
Data Analysis Plan Cross-lagged panel analysis was used to examine both cross-sectional and longitudinal influences on BMI and victimization. The control variables (family structure [lone parenting], mothers' level of education, and insufficient household income) were also included in the model. We used multi-group panel analyses to investigate (1) whether higher BMI puts a child at risk of short-and long-term victimization, (2) the degree to which victimization has long-and short-term effects on BMI independent of any reciprocal effects of BMI on victimization, and (3) whether the same effects are evident for males and females. Crosslagged panel analysis is a practical analytic solution for answering these questions because it allows modelling of short-and long-term effects of BMI on victimization, and vice versa.
To examine whether victimization was characteristic of girls with low and high BMI, we examined the curvilinear relations between these variables. Curvlinear within-time analyses yielded no significant quadratic relations for girls or boys (B <. 13, p ≥. 63) between BMI and victimization. Curvilinear analyses were also used to determine the exact associations between BMI at one time point and victimization at the following data collection wave. These analyses revealed no quadratic relations for girls or boys (B <. 099, p ≥. 71). Thus, our analyses showed only linear relations between BMI and victimization within and over time, suggesting that between the ages of 3-10, victimization is related only to higher BMI. Correlational analyses also revealed associations between BMI and victimization and the covariates, which were measured at each time point, but for boys only.
, 1998). Even so, our design could be improved by using both parent-and child-self report data for children over the age of six. Third, we have investigated only victimization by peers. Given evidence that obese/overweight children are victimised by teachers and family members  (Eisenberg et al. , 2003; Goldfield et al. , 2010; Puhl & King, 2013) , future studies may usefully explore how differences in attitudes towards obese girls and boys from parents, educators, and healthcare providers predict changes in BMI over time. A final consideration is the need to control for variables other than those related to family adversity that may qualify the association between victimization and high BMI. For example, longitudinal research links internalizing problems with peer victimization  (Zwierzynska, Wolke, & Lereya, 2013)  and obesity  (Bradley, Houts, Nader, O'Brien, Belsky, & Crosnoe, 2008)  and it is possible that internalizing problems partially explain why some children are more likely to be victimized and overweight than others.
Acknowledgements: This research was based on the  Québec Longitudinal Study of Child Development (QLSCD) , and was supported by the  Institut de la statistique du Québec , the  Québec Ministry of Health and Social Services , the  Québec Ministry of Families and Seniors , the  Lucie et André Chagnon Foundation , the  Fonds Québécois de la Recherche sur la Société et la Culture (FQRSC) , the  Social Science and Humanities Research Council of Canada (SSHRC) , the  Canadian Institutes for Health Research (CIHR) , the  Research Unit on Children's Psychosocial Maladjustment (GRIP) , and the  Ste. Justine's Hospital Research Center. Michel Boivin is supported by the  Canada Research Chair Program. We are grateful to the participating parents, children, teachers, and schools of the QLSCD. We thank the  Institut de la statistique du Québec ,  Mireille Jetté ,  Bertrand Perron , and the  GRIP  staff for data collection and management.
We probed for situations in which their interaction with color was particularly effective, but also when it was extremely difficult or impossible. Data Collection: We recorded audio for each interview and took written notes. We also recorded video of participants' interactions with the objects they had created, and photographed each artifact and any related color creation or manipulation tools. Analysis: We created a storyboard to illustrate each story, with a photograph of the artifact, as well as drawings to describe each step in the color creation and manipulation process (see Fig. 4 ). We later showed these storyboards to the participants to verify the details. We used a grounded theory  [9]  approach to define ten different color manipulation categories and mapped each story to one or more categories.
Testing with non-color specialists To test the broader applicability of the Color Portraits design space, we interviewed eight scientists and engineers (6 men, 2 women, aged 23-45), from the following disciplines: biology, biochemistry, computer science, data visualization, game developing, virtual reality engineering, automatics and information theory. Each interview lasted approximately one hour, in the participant's office. As before, we asked participants to show us recent artifacts they had created and to describe the steps they followed to incorporate color. At the end of the interview, we asked for additional stories related to each design space category. We recorded audio for all interviews and recorded video of participants' interactions with the resulting artifacts.
INTERACTING WITH PROBES We selected participants from the two earlier studies to try the probes. We wanted to get an idea of how they interpreted each tool in general, and also in the context of their current work. Participants: We re-interviewed eight participants, half from each of the two previous studies (5 men, 3 women, aged  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. Professions included: designer, illustrator, painter, service designer, researcher in data visualization, information theorist, virtual reality engineer and programmer. Procedure: Each session lasted approximately one hour in the participant's studio or office. We presented each tool, in turn, and gave the participant five minutes to experiment and perform short tasks with each tool, as follows: Palette Explorer: Create a book's cover page.
Color Compositor: Design a textured color for the background of the book cover. Color Partner: Create your favorite red and favorite blue. Color Revealer: Write a summary of a recent project. After each task, we asked participants to think of recent color projects in which they manipulated color and show us how the tool might, or might not, be useful for those tasks. We counter-balanced the order of tools across participants. Data collection: We collected audio recordings of each session and screen captures of their interactions with each color tool. We also took notes based on participants' answers to a common set of interview questions.
Background: Relapse is high in lifestyle obesity interventions involving behavior and weight change. Identifying mediators of successful outcomes in these interventions is critical to improve effectiveness and to guide approaches to obesity treatment, including resource allocation. This article reviews the most consistent self-regulation mediators of medium-and long-term weight control, physical activity, and dietary intake in clinical and community behavior change interventions targeting overweight/obese adults. Methods: A comprehensive search of peer-reviewed articles, published since 2000, was conducted on electronic databases (for example, MEDLINE) and journal reference lists. Experimental studies were eligible if they reported intervention effects on hypothesized mediators (self-regulatory and psychological mechanisms) and the association between these and the outcomes of interest (weight change, physical activity, and dietary intake).
Search strategy and study selection A comprehensive search of peer-reviewed articles published between January 2000 and February 2014 (including online ahead of print publication) was conducted in six electronic databases (Pubmed, MEDLINE, PsycINFO, the Cochrane Library, Web of Knowledge, and SPORT-Discus). The decision to restrict the selection to studies published since 2000 is based on the fact that recent development in studies testing the effectiveness of lifestyle interventions for obesity makes older studies less externally valid. For instance, in 1995, Friedman and Brownell  [23]  alerted for the need of a "third generation" of obesity treatment studies analyzing causal mechanisms between psychosocial variables and weight change. Despite this, one decade later, it has been observed that very few studies had investigated such mechanisms, and even fewer looked into long-term changes  [2].
Data coding and extraction A data extraction form was developed, informed by the PRISMA statement for reporting systematic reviews  [21]  and the Cochrane Collaboration's tool for assessing risk of bias  [27]. Data extraction included information about study details (authors, year, country of publication, affiliations, and funding), participants (characteristics, recruitment, setting, attrition, compliance, and blinding), study design and setting, outcomes of interest, mediators/ predictors (in/out list), intervention length and characteristics, psychosocial instruments, and statistical analysis, including mediation techniques (a complete coding form can be obtained from the authors). Authors of included studies were contacted when necessary to retrieve missing data in published reports. Considering that the main focus of this review was the identification of mediators, data extraction was performed separately, starting with the studies formally testing mediation (see Additional file 2), followed by those that reported both the effect of the intervention on hypothesized mediators (path a) and the association of the putative mediator with the outcomes of interest (path b), but did not test mediation (see Additional file 3).