BackgroundMindfulness meditation has demonstrated modest benefits for mental health and well-being, although the relationship between practice dose and outcomes is unclear. Meta-analyses and randomized controlled trials have shown mixed results so far, although such results may stem from methodological issues rather than reflecting the absence of an underlying effect. Research outside structured programs suggests that long-term practice time is linked to positive outcomes, but bias due to self-selection over time may explain these results. ObjectiveThe proposed trial aims to test dose-response effects for an online mindfulness meditation course, examining outcomes and participant engagement across different practice doses. In this pragmatic randomized controlled trial, we hypothesize that larger doses of mindfulness training will yield significantly larger effects and different doses will be significantly associated with variation in participant engagement, with lower engagement evident for higher doses. MethodsAt least 688 healthy adults aged between 18 and 65 years will be randomized to join one of three 4-week online mindfulness courses with daily practices of varying lengths (ie, 10, 20, or 30 min) against a minimally active control condition (4 min). Psychological well-being will be measured using the Warwick-Edinburgh Mental Wellbeing Scale at the baseline, midintervention, and postintervention time points and at 1-month follow-up. Secondary outcomes are psychological distress, anxiety, depression, social anxiety, nonattachment, trait mindfulness, decentering, equanimity, repetitive negative thoughts, emotion regulation, attention control, and emotional reactivity. Other outcomes will be collected weekly and daily during the intervention period. The primary analysis will be undertaken following the intention-to-treat approach. We will also conduct per-protocol secondary analyses on all outcomes (ie, primary and secondary). In addition, we will systematically monitor for possible adverse experiences. ResultsThis study began screening and recruitment in May 2024. Recruitment was paused approximately 6 weeks later after a substantial number of participants were identified as being fraudulent and not meeting the eligibility criteria. Recruitment reopened in October 2024, and by the end of 2024, a total of 70 eligible participants were enrolled. Recruitment recommenced in early 2025 and will continue until the end of March 2025 or until the target sample is reached. We estimate that the results will be published by March 2026. ConclusionsThis study will contribute to the evidence base for mindfulness meditation and the question of how much practice people need to engage in to improve well-being and other psychological outcomes. International Registered Report Identifier (IRRID)DERR1-10.2196/72786

Introduction: Mindfulness meditation has demonstrated modest benefits for mental health and wellbeing, although the relationship between practice dose and outcomes is unclear. Meta-analyses and randomized controlled trials have shown mixed results so far, although such results may stem from methodological issues rather than reflecting the absence of an underlying effect. Research outside structured programs suggests that long-term practice time is linked to positive outcomes, but bias due to self-selection over time may explain these results. The proposed trial aims to test dose-response effects for an online mindfulness meditation course, examining outcomes and participant engagement across different practice doses. In this pragmatic randomized controlled trial, we hypothesize that larger doses of mindfulness training will yield significantly larger effects, and different doses will be significantly associated with variation in participant engagement, with lower engagement evident for higher doses. Methods and analysis: At least 688 healthy adults aged between 18-65 years will be randomised to join one of three 4-week online mindfulness courses with daily practices of varying lengths (i.e. 10-mins, 20-mins, or 30-mins) against a minimally active control condition (3-4 mins). Psychological wellbeing will be measured using the Warwick-Edinburgh Mental Well-being Scale at baseline, mid-intervention, postintervention, and 1-month follow-up. Secondary outcomes are psychological distress, anxiety, depression, social anxiety, nonattachment, trait mindfulness, decentering, equanimity, repetitive negative thoughts, emotion regulation, attention control, and emotional reactivity. Other outcomes will be collected weekly and daily during the intervention period. The primary analysis will be undertaken following the intention-to-treat approach. We will also conduct per-protocol secondary analyses on all outcomes (i.e. primary and secondary). We will also systematically monitor for possible adverse experiences. Discussion: The study will contribute to the evidence-based for mindfulness meditation, and the question of how much practice people need to engage in to improve wellbeing and other psychological outcomes. Trial registration number: The study has been prospectively registered at ClinicalTrials.gov with the identifier NCT06378450.

In this paper we investigate the criterion validity of forced-choice comparisons of the quality of written arguments with normative solutions. Across two studies, novices and experts assessing quality of reasoning through a forced-choice design were both able to choose arguments supporting more accurate solutions-62.2% (SE = 1%) of the time for novices and 74.4% (SE = 1%) for experts-and arguments produced by larger teams-up to 82% of the time for novices and 85% for experts-with high inter-rater reliability, namely 70.58% (95% CI = 1.18) agreement for novices and 80.98% (95% CI = 2.26) for experts. We also explored two methods for increasing efficiency. We found that the number of comparative judgments needed could be substantially reduced with little accuracy loss by leveraging transitivity and producing quality-of-reasoning assessments using an AVL tree method. Moreover, a regression model trained to predict scores based on automatically derived linguistic features of participants' judgments achieved a high correlation with the objective accuracy scores of the arguments in our dataset. Despite the inherent subjectivity involved in evaluating differing quality of reasoning, the forced-choice paradigm allows even novice raters to perform beyond chance and can provide a valid, reliable, and efficient method for producing quality-of-reasoning assessments at scale.

Strategies for accessing temporal information of autobiographical memories may differ based on the qualities of the target memory. Distance-based models propose dating occurs through accessing a decaying strength of a memory, whereas location-based theories suggest it’s achieved through reconstruction of contextual details. Source element availability and high temporal fidelity requirements may bias participants toward location-based strategies. The present study utilises participants' social media posts and emails as cues in an experience sampling study to encourage distance-based strategy utilisation. Thirty-four participants indicated the week, day-of-week, and time-of-day of their Gmails and Twitter, Facebook, and Instagram posts from the 4 weeks preceding their memory test. Results showed that error scores decreased with larger time scales and errors in week judgements increased with proximity to week boundaries and the correct response. All findings suggest that people can rely on the strength of memories to judge the age of emails and social media posts.

A primary challenge for alibi-generation research is establishing the ground truth of real-world events of interest. In the current study, we used a smartphone app to record data on adult participants (N = 51) for a month prior to a memory test. The app captured accelerometry data, GPS locations, and audio environments every 10 min. After a week-long retention interval, we asked participants to identify where they were at a given time from among four alternatives. Participants were incorrect 36% of the time. Furthermore, our forced-choice procedure allowed us to conduct a conditional logit analysis to assess the different aspects of the events that the participants experienced and their relative importance to the decision process. We found strong evidence that participants confuse days across weeks. In addition, people often confused weeks in general and also hours across days. Similarity of location induced more errors than similarity of audio environments or movement types.

We present a system for automatic annotation of daily experience from multisensory streams on smartphones. Using smartphones as platform facilitates collection of naturalistic daily activity, which is difficult to collect with multiple on-body sensors or array of sensors affixed to indoor locations. However, recognizing daily activities in unconstrained settings is more challenging than in controlled environments: 1) multiples heterogeneous sensors equipped in smartphones are noisier, asynchronous, vary in sampling rates and can have missing data; 2) unconstrained daily activities are continuous, can occur concurrently, and have fuzzy onset and offset boundaries; 3) ground-truth labels obtained from the user’s self-report can be erroneous and accurate only in a coarse time scale. To handle these problems, we present in this paper a flexible framework for incorporating heterogeneous sensory modalities combined with state-of-the-art classifiers for sequence labeling. We evaluate the system with real-life data containing 11721 minutes of multisensory recordings, and demonstrate the accuracy and efficiency of the proposed system for practical lifelogging applications.

: HanDles is a document visualization tool developed by Ohio State University for DRDC Toronto. One aspect of documents that might be of interest to analysts is the extent to which they express positive or negative opinion or sentiment toward some issue or group. In this report, we describe how HanDles was extended to include the ability to classify documents as containing predominantly positive or negative sentiment. The capability was added to the tool so that it could be used in Influence Operations contexts. As a test case, we trained HanDles to distinguish good and poor film reviews, and then tested it three times to see how well it classified documents. The first test was conducted on reviews of the Amazon Kindle. The second test was run on text segments of the original training set of movie reviews, and finally, it was tested on a set of movie reviews that it had not seen before. In general, HanDles did a poor job detecting the sentiment associated with the reviews of the Amazon Kindle. We attribute the poor performance to the fact that movie and product reviews discuss different issues, and as such, there is limited similarity in the two classes of document. Not surprisingly, HanDles did a good job classifying text segments of the original training set. Also, the finding demonstrated that, unlike many other sentiment analysis tools that only classify text at the whole-document level, HanDles can be used effectively to extract the issues being discussed within documents, and assign sentiment to those. For example, a review of a film might be classified as negative overall, but HanDles can determine that the acting was good, but the directing was poor. Finally, when we tested HanDles on a new set of movie reviews it had not seen before, it performed with 93.3% accuracy. The results of our trial suggest that there must be some similarity between the documents used during training and those used in the operational context for HanDles to work properly.

: The Automated Grading System (AGS) was developed jointly by Defence Research and Development Canada (DRDC) Toronto and the Canadian Forces School of Military Intelligence (CFSMI) to provide students at the school a tool to help in the composition of accurate and effective Intelligence Summaries (INTSUMs). The AGS is a web-browser based system that provides feedback to students about how well their summary matches that of a gold standard summary written by an instructor. The AGS allows students to iteratively correct and re-submit their summaries as they attempt to maximize the match between their summary and the gold standard. In this report, we provide both the instructor and student user's manual for the AGS. Importantly, we also provide the results of a small validation study wherein we asked participants to summarize news stories about sea piracy near Somalia. Participants used feedback from the AGS to improve their summaries until they were satisfied that they had done the best job they could do. The grades given to the first and final summaries by the AGS were then compared to the grades awarded by the lead instructor at CFSMI. The tool and the instructor's assessments of the summaries were in close agreement. The results confirm that the AGS can be used as an effective teaching tool to help students improve their summary-writing skills.

Ten models are compared in their ability to predict eye-tracking data that was collected from 49 participants' goal-oriented search tasks on a total of 1809 Web pages. Forming the basis of six of these models, three semantic models and two corpus types are compared as components for the Semantic Fields model (Stone and Dennis, 2007) that estimates the semantic salience of different areas displayed on Web pages. Latent Semantic Analysis, Sparse Nonnegative Matrix Factorization, and Vectorspace were used to generate similarity comparisons of goal and Web page text in the semantic component of the Semantic Fields model. Overall, Vectorspace was the best performing semantic model in this study. Two types of corpora or knowledge-bases were used to inform the semantic models, the well known TASA corpus and other corpora that were constructed from the Wikipedia encyclopedia. In all cases the Wikipedia corpora outperformed the TASA corpora. A non-corpus-based Semantic Fields model that incorporated word overlap performed more poorly at these tasks. Three baseline models were also included as a point of comparison to evaluate the effectiveness of the Semantic Fields models. In all cases the corpus-based Semantic Fields models outperformed the baseline models when predicting the participants' eye-tracking data. Both final destination pages and pupil data (dilation) indicated that participants' were actively performing goal-oriented search tasks.

Comparative Analysis of Semantic Models and Corpora Choice when using Semantic Fields to Predict Eye Movement on Webpages Benjamin P. Stone (bpstone@psychology.adelaide.edu.au) School of Psychology, Level 4, Hughes Building, The University of Adelaide Adelaide, SA 5005 Australia Simon J. Dennis (dennis.210@osu.edu) Department of Psychology, Ohio State University, 1827 Neil Ave Columbus, OH 43210 USA Abstract Nine models are compared in their ability to predict eye- tracking data that was collected from 49 participants’ goal- oriented search tasks on a total of 1809 webpages. Forming the basis of six of these models, three semantic models and two corpus types are compared as components for the Seman- tic Fields model (Stone and Dennis, 2007) that estimates the semantic saliency of different areas displayed on webpages. Latent Semantic Analysis, Sparse Non-Negative Matrix Fac- torization, and Vectorspace were used to generate similarity comparisons of goal and webpage text in the semantic compo- nent of the Semantic Fields model. Surprisingly, Vectorspace was consistently the best performing model in this study. Two types of corpora or knowledge-bases were used to inform the semantic models, the well known TASA corpus and other cor- pora that were constructed from the Wikipedia encyclopedia. In all cases the Wikipedia corpora out performed the TASA corpora. Three other baseline models: Flat, Non-Flat, and No-Model were included as a point of comparison to evaluate the effectiveness of the Semantic Fields models. In all cases the Semantic Fields models outperformed the baseline models when predicting the participants’ eye-tracking data. Keywords: LSA; Vectorspace; SpNMF; Semantic Fields; web pages; eye-tracking, goal-directed visual search Introduction The exponential increase in Internet usage over the last decade has motivated psychological researchers to examine web users’ behavior in this virtual environment. Research fo- cussing on user behavior in web page environments can gen- erally be delineated into two main streams: display-based and semantics-based research. While both methods to some de- gree attempt to predict the area on a webpage that a user will focus their attention on, they approach this task in different ways. Display-based research has focused on perceptual as- pects of the webpage, components such as element and menu position, color usage, and font style. Alternatively, when attempting to predict user’s webpage navigation, semantic- based research matches web user’s information needs to the concepts displayed within the textual content of webpages. Display-based and semantics-based research into web user’s visual search of web page hyperlinks has indicated that the user’s search processes are influenced by: text semantics, el- ement position, aesthetic qualities of elements, and environ- mental learning (Faraday, 2000, 2001; Ling & Van Schaik, 2002, 2004; Chi et al., 2003; Cox & Young, 2004; McCarthy, Sasse, & Rigelsberger, 2003; Pearson & Van Schaik, 2003; Pirolli & Fu, 2003; Rigutti & Gerbino, 2004; Blackmon, Ki- tajima, & Polson, 2005; Kaur & Hornof, 2005; Pirolli, 2005; Stone & Dennis, 2007). Several researchers have highlighted the importance of combining display-based and semantic information when modelling user’s navigation through web sites (Blackmon, Polson, Kitajima, & Lewis, 2002; Chi et al., 2003; Pirolli & Fu, 2003; Kaur & Hornof, 2005; Stone & Dennis, 2007). Research that has combined display and semantic informa- tion when predicting web users’ behavior include the Cogni- tive Walkthrough for the Web (CWW, Blackmon, Kitajima & Polson, 2005), the Bloodhound Project (Chi et al., 2003), and the Latent Semantic Analysis - Semantic Fields model (LSA- SF, Stone & Dennis, 2007). For a detailed description of the CWW and Bloodhound Project the reader is directed to Stone and Dennis (2007). One major unanswered question when integrating display- based and semantic information is the nature of the seman- tic model itself, and the data upon which it should rely. As discussed previously, the computational semantics literature provides multiple options for semantic models, and similarly there are several approaches one might take in specifiying a corpus relevant to the task (Stone, Dennis, & Kwantes, sub- mitted). In this paper, we aim to shed light on these issues, by comparing different models and corpora on their ability to predict human eye movements during web browsing. Semantic Fields (SF) In a previous article, we presented the LSA-SF model (Stone & Dennis, 2007), which was used to predict the eye move- ments of 49 participants recorded during goal-oriented search tasks on three websites. The LSA-SF model used LSA to cal- culate the similarity between a textual representation of the users’ goal and each of the textual elements displayed on a webpage. Using a decay function, these LSA estimates of similarity were then distributed and summed over each pixel position for all of the textual elements contained on a web- page (see Equation 1). Combining the semantic information (L) with distance (d i(x,y) ) from its display position using a de- cay function, enabled the production of maps of information density for each web page in our study (see Figure 1). SF ( x, y) = ∑ L i e −λd i(x,y) i Initially, the TASA corpus was used as a knowledge based

A critical issue in the development of statistical models of lexical semantics is the nature of the background corpus that is used to derive term representations. Often good performance can be achieved if background corpora are hand selected, but performance can drop precipitously when this is not the case. In this study, we investigated querying a larger textbase (Wikipedia) to create subcorpora for analysis with lexical semantics models (Zelikovitz & Kogan, 2006). Similarities generated from six models of lexical semantics were compared against human ratings of document similarity on two document sets the Internet Movie Database set and the newswire set from Lee, Pincomb and Welsh (2005). These methods included the vector space model (Salton, Wong & Yang, 1975), Latent Semantic Analysis (LSA, Kintsch, McNamara, Dennis, & Landauer, 2006), sparse Independent Components Analysis (Bronstein, Bronstein, Zibulevsky, & Zeevi, 2005), the topics model (Griffiths & Steyvers, 2002, Blei, Ng, & Jordan, 2003), nonnegative matrix factorization (Xu, Liu, & Gong, 2003) and the constructed semantics model (Kwantes, 2005). We found that on these datasets the creation of subcorpora can be very effective even improving upon the performance of corpora that had been hand selected for the domain. Surprisingly, the overall best performance was observed for the vectorspace model.

Using LSA Semantic Fields to Predict Eye Movement on Web Pages Benjamin Stone (bpstone@psychology.adelaide.edu.au) School of Psychology, University of Adelaide Adelaide, SA 5005 AUSTRALIA Simon Dennis (simon.dennis@adelaide.edu.au) School of Psychology, University of Adelaide Adelaide, SA 5007 Australia et al., 2001), the CWW process takes some aspects of the web page’s display structure into consideration by grouping screen areas into regions. Also, like the Bloodhound Project, the semantic content of the each web page is evaluated statistically against the web user’s target goals. However, instead of using the WUFIS (Web User Flow by Information Scent) algorithm, the CWW uses Latent Semantic Analysis (LSA) to compare semantic content. Furthermore, similar to the Bloodhound Project’s close relative SNIF-ACT, which is a model based on the ACT-R cognitive architecture (Pirolli & Fu, 2003; Pirolli, 2005), the CWW does not limit the content of its statistical semantic analysis to the documents in the website. To this end, both CWW and SNIF-ACT also incorporate a corpus of documents that is considered to represent a user’s knowledge base. Once a web page has been segmented into regions or sections, the model generates a description of each section, and these descriptions are then compared, using LSA, with the users goals and knowledge base. The section with the highest similarity to these user components is then selected for further analysis. Link texts in the selected section are again evaluated against the web user’s goal and knowledge base using LSA. After this evaluation, the model then follows the hyperlink with the highest utility. Abstract This paper outlines a new method for estimating the visual saliency different areas displayed on a web page. Latent Semantic Analysis is used to calculate Semantic Fields values for any (x, y) coordinate point on a web page based on the structure of that web page. These Semantic Field values were then used to predict eye-tracking data that was collected from 49 participants’ goal-orient search tasks on a total of 1842 web pages. Semantic Field values were found to predict the participants’ eye-tracking data. Keywords: LSA; Semantic Fields; LSA-SF; web pages; eye- tracking; visual saliency. Introduction Combining approaches A review of both the Display-based and Semantics-based research into web user’s visual search of web page hyperlinks has indicated that the user’s search processes are influenced by: text semantics, element position, aesthetic qualities of elements, and environmental learning (Brumby & Howes, 2003, 2004; Chi et al. 2003; Faraday, 2000, 2001; Cox & Young, 2004; Kaur & Hornof, 2005; Ling & van Schaik, 2002, 2004; McCarthy, Sasse & Rigelsberger, 2003; Pearson & van Schaik, 2003; Pirolli & Fu, 2003; Rigutti & Gerbino, 2004; Blackmon, Kitajima & Polson, 2005; Grier, 2005; Pirolli, 2005). As is described more fully below, Semantics-based researchers have, to varying degrees, started to incorporate characteristics of the web-page display into their models. Moreover, several researchers have highlighted the importance of this combined approach to modelling users navigation through web sites (Blackmon et al, 2002; Pirolli and Fu, 2003; Chi et al. 2003; and, Kaur & Hornof, 2005). The Cognitive Walkthrough for the Web (CWW) is a theory-based tool designed to assess the usability of websites (Blackmon, Kitajima & Polson, 2005). To this end, CWW simulates web user’s navigation through a website using the CoLiDeS (Comprehension-based Linked model of Deliberate Search) model. Furthermore, CoLiDeS is based on Kintsch’s Construction-Integration theory of comprehension. CWW approaches the problem of modelling web user’s link following behaviour in a somewhat similar fashion to the Bloodhound Project (Chi et al., 2003). Like the Bloodhound Project’s use of page position to inform calculation of probable link choice (Chi Latent Semantic Analysis (LSA) LSA is a statistical method of textual evaluation that allows the researcher to derive meaning from a set of documents (Landuaer & Dumais, 1997; Landauer, MacNamara, Dennis & Kintsch 2007). Linear algebraic methods, such as Singular Value Decomposition, enable the researcher to determine the semantic similarity between words and sets of words contained within a corpus of documents. In a way, the corpus of documents acts as a knowledge base. For example, the Touchstone Applied Science Associates (TASA) document corpus represents literature that students may have been exposed between grade 3 and the first year of college. Moreover, the research described in this paper uses the TASA corpus as a best approximation to the knowledge-base of the first year university students who have participated in this study. LSA - Semantic Fields (LSA-SF) In this paper, an alternative method of modelling human behaviour in web page environments is reported. The LSA-

This experiment explores whether the size of human pupil dilation is a measure that can discriminate between easier and harder intellectual tasks undertaken by the participant when information is displayed on a computer monitor. While it has been previously found that a participant’s pupil dilation will be larger during harder intellectual tasks, these experiments have not generally been conducted under the environmental condition of light radiated from a computer monitor. The findings of this experiment indicate that computer monitor’s radiance did not interfere with the researcher’s ability to discriminate successfully between the participant’s taskrelated pupil dilation.

Spatial analysis and spatial information systems have great potential in many non‐geographic domains. This paper presents an example of the utility of spatial analysis in a non‐geographic domain. A technique of pupillometry using digital infrared video loosely coupled with a Spatial Information System and a spreadsheet is developed to accurately quantify pupil dilation magnitude and constriction onset latency for participants of different cognitive ability and under different cognitive loads. Spatio‐temporal pupil dynamics of participants are recorded using digital infrared video. The pupil to iris area ratio is calculated for over 470,000 temporally sequenced de‐interlaced video fields by automatic feature extraction using a combination of threshold analysis, spatial smoothing and areal filtering. Pupil dilation magnitudes and constriction onset latencies are calculated through post‐processing in a spreadsheet. The study identifies inadequacies in current spatial analytical techniques for automatic feature extraction not necessarily evident in geographic applications. Issues impeding the employment of spatial analysis in non‐geographic domains including the lack of a generic spatial referencing system are identified and discussed.