Privacy versus open science

Towards an ecologically valid naturalistic cognitive neuroscience of memory and event cognition

The landscape of human memory and event cognition research has witnessed a transformative journey toward the use of naturalistic contexts and tasks. In this review, we track this progression from abrupt, artificial stimuli used in extensively controlled laboratory experiments to more naturalistic tasks and stimuli that present a more faithful representation of the real world. We argue that in order to improve ecological validity, naturalistic study designs must consider the complexity of the cognitive phenomenon being studied. Then, we review the current state of "naturalistic" event segmentation studies and critically assess frequently employed movie stimuli. We evaluate recently developed tools like lifelogging and other extended reality technologies to help address the challenges we identified with existing naturalistic approaches. We conclude by offering some guidelines that can be used to design ecologically valid cognitive neuroscience studies of memory and event cognition.

Assessing computational reproducibility in Behavior Research Methods

Psychological science has thrived thanks to new methods and innovative practices. Journals, including Behavior Research Methods (BRM), continue to support the dissemination and evaluation of research assets including data, software/hardware, statistical code, and databases of stimuli. However, such research assets rarely allow for computational reproducibility, meaning they are difficult to reuse. Therefore, in this preregistered report, we explore how BRM's authors and BRM structures shape the landscape of functional research assets. Our broad research questions concern: (1) How quickly methods and analytical techniques reported in BRM can be used and developed further by other scientists; (2) Whether functionality has improved following changes to BRM journal policy in support of computational reproducibility; (3) Whether we can disentangle such policy changes from changes in reproducibility over time. We randomly sampled equal numbers of papers (N = 204) published in BRM before and after the implementation of policy changes. Pairs of researchers recorded how long it took to ensure assets (data, software/hardware, statistical code, and materials) were fully operational. They also coded the completeness and reusability of the assets. While improvements were observed in all measures, only changes to completeness were altered significantly following the policy changes (d = .37). The effects varied between different types of research assets, with data sets from surveys/experiments showing the largest improvements in completeness and reusability. Perhaps more importantly, changes to policy do appear to have improved the life span of research products by reducing natural decline. We conclude with a discussion of how, in the future, research and policy might better support computational reproducibility within and beyond psychological science.

Examining dependencies among different time scales in episodic memory - an experience sampling study

We re-examined whether different time scales such as week, day of week, and hour of day are independently used during memory retrieval as has been previously argued (i.e., independence of scales). To overcome the limitations of previous studies, we used experience sampling technology to obtain test stimuli that have higher ecological validity. We also used pointwise mutual information to directly calculate the degree of dependency between time scales in a formal way. Participants were provided with a smartphone and were asked to wear it around their neck for two weeks, which was equipped with an app that automatically collected time, images, GPS, audio and accelerometry. After a one-week retention interval, participants were presented with an image that was captured during their data collection phase, and were tested on their memory of when the event happened (i.e., week, day of week, and hour). We find that, in contrast to previous arguments, memories of different time scales were not retrieved independently. Moreover, through rendering recurrence plots of the images that the participants collected, we provide evidence the dependency may have originated from the repetitive events that the participants encountered in their daily life.

Open access and its potential impact on public health - A South African perspective

Traditionally, access to research information has been restricted through journal subscriptions. This means that research entities and individuals who were unable to afford subscription costs did not have access to journal articles. There has however been a progressive shift toward electronic access to journal publications and subsequently growth in the number of journals available globally. In the context of electronic journals, both open access and restricted access options exist. While the latter option is comparable to traditional, subscription-based paper journals, open access journal publications follow an "open science" publishing model allowing scholarly communications and outputs to be publicly available online at no cost to the reader. However, for readers to enjoy open access, publication costs are shifted elsewhere, typically onto academic institutions and authors. SARS-CoV-2, and the resulting COVID-19 pandemic have highlighted the benefits of open science through accelerated research and unprecedented levels of collaboration and data sharing. South Africa is one of the leading open access countries on the African continent. This paper focuses on open access in the South African higher education research context with an emphasis on our Institution and our own experiences. It also addresses the financial implications of open access and provides possible solutions for reducing the cost of publication for researchers and their institutions. Privacy in open access and the role of the Protection of Personal Information Act (POPIA) in medical research and secondary use of data in South Africa will also be discussed.

Fostering openness in open science: An ethical discussion of risks and benefits

Transformation of science by embracing the concepts of open science presents a very attractive strategy to enhance the reliability of science. Open science policies embody the concepts of open data and open access that encompass sharing of resources, dissemination of ideas, and synergizing the collaborative forums of research. Despite the opportunities in openness, however, there are grave ethical concerns too, and they present a dual-use dilemma. Access to sensitive information is seen as a security risk, and it also possesses other concerns such as confidentiality, privacy, and affordability. There are arguments that open science can be harmful to marginalized groups. Through this study, we aim to discuss the opportunities of open science, as well as the ethical and security aspects, which require further deliberation before full-fledged acceptance in the science community.

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

BACKGROUND

Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature.

OBJECTIVE

This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods.

METHODS

A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology.

RESULTS

Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies.

CONCLUSIONS

The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

Differential privacy in health research: A scoping review

OBJECTIVE

Differential privacy is a relatively new method for data privacy that has seen growing use due its strong protections that rely on added noise. This study assesses the extent of its awareness, development, and usage in health research.

MATERIALS AND METHODS

A scoping review was conducted by searching for ["differential privacy" AND "health"] in major health science databases, with additional articles obtained via expert consultation. Relevant articles were classified according to subject area and focus.

RESULTS

A total of 54 articles met the inclusion criteria. Nine articles provided descriptive overviews, 31 focused on algorithm development, 9 presented novel data sharing systems, and 8 discussed appraisals of the privacy-utility tradeoff. The most common areas of health research where differential privacy has been discussed are genomics, neuroimaging studies, and health surveillance with personal devices. Algorithms were most commonly developed for the purposes of data release and predictive modeling. Studies on privacy-utility appraisals have considered economic cost-benefit analysis, low-utility situations, personal attitudes toward sharing health data, and mathematical interpretations of privacy risk.

DISCUSSION

Differential privacy remains at an early stage of development for applications in health research, and accounts of real-world implementations are scant. There are few algorithms for explanatory modeling and statistical inference, particularly with correlated data. Furthermore, diminished accuracy in small datasets is problematic. Some encouraging work has been done on decision making with regard to epsilon. The dissemination of future case studies can inform successful appraisals of privacy and utility.

CONCLUSIONS

More development, case studies, and evaluations are needed before differential privacy can see widespread use in health research.

Open-source smartphone app and tools for measuring, quantifying, and visualizing technology use

Psychological science has spent many years attempting to understand the impact of new technology on people and society. However, the frequent use of self-report methods to quantify patterns of usage struggle to capture subtle nuances of human-computer interaction. This has become particularly problematic for devices like smartphones that are used frequently and for a variety of purposes. While commercial apps can provide an element of objectivity, these are 'closed' and cannot be adapted to deliver a researcher-focused 'open' platform that allows for straightforward replication. Therefore, we have developed an Android app that provides accurate, highly detailed, and customizable accounts of smartphone usage without compromising participants' privacy. Further recommendations and code are provided to assist with data analysis. All source code, materials, and data are freely available (see links in supplementary materials section).

The Fallacy of an Airtight Alibi: Understanding Human Memory for "Where" Using Experience Sampling

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.

Democratizing Access to Community-Based Survey Findings Through Dynamic Data Visualizations

OurStats ( https://www.cbrc.net/ourstats ) is a data visualization dashboard developed by the Community-Based Research Centre (CBRC) to increase access to data from the Sex Now surveys-Canada's largest community-based surveillance study of gay and bisexual men. An evaluation of the OurStats dashboard was conducted using an online survey distributed through the CBRC and Advance Alliance-an alliance of Canada's leading HIV and queer men's health organizations. Since being launched in November 2019 (through December 2019), 350 unique visitors used the OurStats Dashboard (5.8 per day). Based on responses from 10 community partners, all respondents said they would probably/definitely use OurStats again and would probably/definitely recommend it to colleagues; nine felt it was much/somewhat better than traditional academic outputs (e.g., poster presentations, journal articles); and seven felt it was much/somewhat better than traditional knowledge translation outputs (e.g., fliers, posters, and social media posts). Respondents said they would use OurStats to identify needs of gay and bisexual men (n = 9), prepare grant/funding applications (n = 9), prepare presentations about Sex Now data (n = 7), and evaluate the impact of local programs (n = 4). Overall, half felt that OurStats was somewhat/extremely easy to use and half felt that it was somewhat difficult to use. The most commonly identified requested improvement was to provide help documentation that explained how each of the display settings changed the visualizations. From these findings, we conclude that dynamic visualizations for community-based survey data are highly feasible and acceptable, provided appropriate support is available to help community partners use these tools.

Opening Pandora's Box: Peeking inside Psychology's data sharing practices, and seven recommendations for change

Open data-sharing is a valuable practice that ought to enhance the impact, reach, and transparency of a research project. While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals. Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability—conclusions that closely mirror those reported outside of Psychology. Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.