Barsalou Abstract Concepts talk online

As part of a Workshop on Fresh / New Perspectives on Abstract Concepts presented by Sapienza University of Rome and the TRAINCREASE Project in March 2021, Lawrence Barsalou presented a talk, “Moving beyond the distinction between concrete and abstract concepts.” A video recording of the talk can be found on YouTube here.

Applications open for SGSSS PhD Position

Applications have now opened for the following PhD position:

Lawrence Barsalou and Esther Papies (University of Glasgow) and Aleksandar Matic (Koa Health) have been awarded a PhD position from the Scottish Graduate School of Social Science beginning in the fall of 2021.  This studentship is associated with SGSSS program that fosters collaborative projects between academia and industry. 

In this project, our team will build and evaluate a health app to help individuals learn about and regulate life stress (utilizing the Situated Assessment Method developed at Glasgow, together with other tools developed at Koa Health).  Of particular interest is building an effective digital tool that promotes learning about distress and eustress to decrease distress and increase eustress (shifting the affect associated with stress from negative to positive). 

The PhD student will become part of a large lab group at the University of Glasgow that focuses on health cognition and behaviour.  The two Glasgow supervisors (Barsalou and Papies) will provide training in health cognition, behaviour change, and research methods.  The industry partner (Matic) will serve as an equal third supervisor, providing training in app development, implementation, and assessment.  The PhD project will play a foundational role for developing future collaborative projects that aim to develop increasingly powerful apps for decreasing distress and increasing eustress.  Students without a masters degree are eligible and will be funded for an additional year of masters training.

If you are interested in applying for this position, please do so by clicking on the image below:

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Lawrence Barsalou receives a position in the ARC Accelerator Programme to develop and commercialise the Situated Assessment Method

January 2021

The ARC Accelerator Programme is a first-of-its-kind opportunity designed specifically to help social science researchers to develop ideas based on their research into businesses or ventures that help people, society and the economy.  In the 2021 programme, 15 UK-wide training positions were available across participating universities.

The ARC Accelerator is a six-month programme in which trainees benefit from:  (1) support for exploring and developing their idea, (2) a three-week entrepreneurial bootcamp led by industry experts, (3) specialised sector-specific training with access to experts, investors, and key networks, (4) mentorship to validate ideas, develop business models, and support pitches for funding and investment.

While participating in the ARC programmer, I aim to develop, implement, and distribute the Situated Assessment Method (SAM2) for measuring, predicting, and changing health behaviours, including stress, social connectiveness, eating habits, trichotillomania, etc.  This novel and powerful approach has significant potential for being implemented in digital health apps for use by clinicians, health practitioners, and private individuals.

New PhD position available: Improving engagement with mobile health apps by understanding (mis)alignment between design elements and personal characteristics

January 2021

Aleksandar Matic (Koa Health) and Lawrence Barsalou (University of Glasgow) are supervising this PhD project in the University of Glasgow Center for Doctoral Training in Socially Intelligent Artificial Agents.  Applications are open for the position that begins in Fall 2021, beginning with a year of funded masters work, followed by three years of funded PhD work.

Project summary:  A growing consensus has concluded that improving engagement with health apps requires personalisation at an individual level.  In this project, we will pursue two novel approaches for improving engagement with health apps.

First, we will conduct a retrospective exploration of previous app use as documented in the literature. Specifically, we will assess( a) personal characteristics of individuals who have previously used mobile health apps, (b) design elements (including intervention mechanisms) of these apps, and (c) outcomes related to app engagement (e.g., drop-out rates, frequency of use). Of focal interest will be how personal characteristics and app design interact to produce different levels of app engagement.  We aim to publish a major review of the literature based on this work.

Second, in a well-established stress app that we continue to develop, we will allow users to configure its design features in various ways.  We will also collect data about users’ personal characteristics.  From these data, we hope to develop design principles for tailoring future apps and intervention mechanisms to specific individuals.  A series of studies will be performed in this line of work, together with related publications.

This project is likely to focus on stress as the primary-use case.  In a related project, we are developing and evaluating stress apps that measure and predict stress in specific situations, linking psychological assessment to physiological data harvested implicitly from wearables.  In a third project, we are implementing behaviour change interventions in digital health apps to reduce distress and increase eustress.  Work from all three projects will be integrated to develop maximally effective stress apps, tailored to individuals, that effectively measure, predict, and alter stress experience.

Application sites: https://socialcdt.org/https://socialcdt.org/how-to-apply/

New PhD position available: Developing and assessing a digital health app that implements the Situated Assessment Method to decrease distress and increase eustress

November 2021

Lawrence Barsalou and Esther Papies (University of Glasgow) and Aleksandar Matic (Koa Health) have been awarded a PhD position from the Scottish Graduate School of Social Science beginning in the fall of 2021.  This studentship is associated with SGSSS program that fosters collaborative projects between academia and industry. 

In this project, our team will build and evaluate a health app to help individuals learn about and regulate life stress (utilizing the Situated Assessment Method developed at Glasgow, together with other tools developed at Koa Health).  Of particular interest is building an effective digital tool that promotes learning about distress and eustress to decrease distress and increase eustress (shifting the affect associated with stress from negative to positive). 

The PhD student will become part of a large lab group at the University of Glasgow that focuses on health cognition and behaviour.  The two Glasgow supervisors (Barsalou and Papies) will provide training in health cognition, behaviour change, and research methods.  The industry partner (Matic) will serve as an equal third supervisor, providing training in app development, implementation, and assessment.  The PhD project will play a foundational role for developing future collaborative projects that aim to develop increasingly powerful apps for decreasing distress and increasing eustress.  Students without a masters degree are eligible and will be funded for an additional year of masters training.

Applications open:  1 March 2021.

Lawrence Barsalou, Christoph Scheepers, and Aleksandar Matic win an award from the University of Glasgow Knowledge Exchange Fund for developing a stress app that implements the Situated Assessment Method

December 2020

This award University of Glasgow Knowledge Exchange Fund allows us to collect data that will inform the design of various stress apps currently under development.

Title:  Developing a digital health app that implements the Situated Assessment Method to decrease distress and increase eustress

Summary:  We are developing an academia-industry collaboration for building a digital health app that implements the SAM2 assessment procedure, together with other stress tools that Koa Health has developed.  The central aim of this collaborative work is to decrease distress and increase eustress by (1) inducing users to learn about psychological mechanisms associated with their individual stress, (2) providing users with precision feedback about these stress mechanisms, along with tools for working on them.

Academic contributions will focus on psychological and biological mechanisms of stress.  Industry contributions will focus on electronic hardware and machine learning algorithms for implementing state-of-the-art health apps.  Integrating these two contributions will produce a stress app that potentially offers users considerable leverage in decreasing distress and increasing eustress.  We further aim to develop forms of the app that preclude health intervention disparities across SES levels.

Initially, the app will collect situations in a user’s life where they experience distress and eustress.  On subsequent occasions, users will evaluate these situations with respect to the stress mechanisms that SAM2 typically assesses (e.g., expectation violation, threat, coping ability, negative affect, rumination, pessimism, judgmentalness).  After performing the SAM2 procedure on a given occasion, users will receive feedback about their levels of distress and eustress, together with information about the specific stress mechanisms most related to their stress levels.  Finally, the app will provide tailored instructions about how to change these predictive patterns so that stress experience shifts increasingly from negative distress to positive eustress. 

Courtney Taylor-Browne Luka wins grant from the TLC Foundation for using the Situated Assessment Method to understand and change compulsive hair pulling

December 2020

In collaboration with her supervisors (Lawrence Barsalou and Jude Stevenson), Courtney Taylor-Browne Luka won a competition for TLC Foundation funds to significantly develop and extend her PhD research.

Title:  Understanding, predicting, and changing hairpulling across specific situations

Synopsis:  Our research aims to understand, predict, and ultimately change hairpulling. Hairpulling behaviours are highly varied, differing in pulling sites, frequency, and duration, not only between individuals but also within. Research has further suggested that hairpulling is highly situation specific, tending to occur more often in some situations than others. To the extent that pulling reflects situational constraints, this suggests measuring and working with it in a situated manner.

Problematically, however, measures that assess hairpulling in individuals are unsituated, meaning that they evaluate hairpulling experiences using items that abstract over specific situations. Take, for example, an item from the Massachusetts General Hospital-Hair Pulling Scale, “On an average day, how often did you actually pull your hair?” To evaluate this item, individuals must abstract over many specific situations they experienced previously to provide an overall judgement across them. By not assessing specific situations, unsituated measures may instead measure intuitive theories about oneself more than experiences of actual pulling situations. Further, unsituated theories are unable to provide much detail about pulling behaviour, such as initiating conditions (e.g., environmental triggers), surrounding actions (e.g., rituals), and outcomes (e.g., changes in emotion). Not capturing situational features of pulling experiences limits our ability to understand an individual’s hairpulling, in turn limiting our ability to accurately predict and ultimately change it. Our project aims to better understand, predict, and change hairpulling behaviour in specific situations.

In previous research, we have developed a new situated approach to measuring hairpulling, whereby individuals no longer abstract over specific situations to produce overall judgements of their hairpulling experience. Instead, individuals rate their experiences of pulling in specific situations (e.g., while watching television, while cleaning the house). Because this approach is grounded in two dimensions of situatedness—situational experience and the situated action cycle—we call it the Situated Assessment Method (SAM²). On the first dimension of situatedness, SAM² assesses a behaviour, such as pulling in specific life situations.  On the second dimension of situatedness SAM² assess the basic phases of the target behaviour in the situation, including environmental cues that trigger it, appraisals and goals that arise from these triggers, emotion and motivation in the body, cognitive and motoric actions to achieve goals, and the outcomes of these actions on the environment and oneself.  Thus, to measure a behaviour like hairpulling, SAM² assesses it in the situations where it occurs across all these phases of the behaviour. Additionally, we measure these phases in ways that reflect three major theoretical perspectives traditionally used to explain trichotillomania: the Comprehensive Behavioural Model (ComB), the Cognitions and Beliefs Model, and the Emotion Regulation Model.

Using the SAM2 method, we construct detailed models of the situational features that best predict pulling for a single individual. Consistent with the ComB model, triggers positively predict pulling/urge across most individuals. Consistent with the Cognitions and Beliefs model, reduced control increases hairpulling for many individuals, with negative feelings about oneself predicting pulling urge/frequency in some individuals. Consistent with the Emotion Regulation Model, pulling/urge to pull is positively related to reduction in negative emotion. Beyond the general trends just described, our results highlight how hairpulling experiences vary tremendously across individuals, while demonstrating that SAM² captures them effectively.

We further propose that SAM² can be used as an effective intervention for trichotillomania, decreasing frequency and urge of pulling. Encouraging individuals to process potential pulling situations in terms of the frequency and urge to pull, together with situational predictors of pulling, such as triggers, control, consequences etc., may help them understand what is happening in their specific pulling situations.  As a result of becoming more consciously aware of situational factors associated with pulling, individuals may become better able to cope effectively with them. Essentially, SAM² offers a behaviour change intervention that can translate into helping individuals anticipate their pulling episodes, together with becoming more aware of the situational features relevant to pulling.  By becoming more aware of how their specific form of pulling operates in specific situations, pullers may become better able to regulate their behaviour.  A study to be performed in the coming year will assess whether SAM2 offers an effective behaviour change intervention.

Besides producing an increased understanding of individual hair pulling that promotes effective behaviour change, SAM2 can also be used to provide feedback that directs individuals to effective interventions for moderating the situational factors most relevant for them. If our research demonstrates that SAM² can be an effective behavioural change intervention, we anticipate that an app utilising SAM² could be used by those who wish to understand their hairpulling and potentially reduce its frequency and associated urges.

New PhD project with Casper Pedersen on establishing situated and generalizable models of stress

October 2020

Casper Pedersen has been awarded a PhD studentship from the University of Glasgow’s Center for Doctoral Training in Socially Intelligent Artificial Agents.  Casper’s supervisory team includes Christoph Scheepers and Lawrence Barsalou (University of Glasgow) and Aleksandar Matic (Koa Health). 

In this project, we combine Generalizability Theory and the Situated Assessment Method to build models of an individual’s distress and eustress.  Of central interest is using this model to measure, predict, and change levels of distress and eustress over time.  At multiple timepoints, individuals provide data about distress and eustress in specific life situations, together with data about associated stress mechanisms.  Our modeling tools then provide feedback to individuals about their stress levels, together with the most highly related stress mechanisms. 

Of central interest is using this information help individuals better understand how distress and eustress emerge in specific life situations.  As individuals become increasingly tuned into their stress experience, they can then use this understanding to initiate various coping strategies and interventions for changing it.  A central goal of this work is identifying methods for shifting experiences of distress to eustress.  We also plan to explore the use of wearable technology that captures physiological data to support measuring, predicting, and changing stress.

To further develop this collaborative effort between academia and industry, we have received additional funding from the University of Glasgow’s Knowledge Exchange Fund (along with the funding from the Center for Doctoral Training).

Jing Chen receives PhD for research on relations between initial mindfulness training and eating

Title:  Effects of brief mindfulness training on the neural activity associated with processing food cues

Supervisor:  Lawrence Barsalou

Date:  June 2019

Universities:  The University of Glasgow supported the research performed in this dissertation, with data collected in the fMRI scanner in the Institute for Neuroscience and Psychology.  Emory University awarded the PhD, where Jing Chen was a PhD student.

Abstract:  A functional magnetic resonance imaging experiment assessed effects of a brief mindfulness intervention on the neural mechanisms that underlie food cue processing.  In a blocked design, an initial training phase asked participants to either normally view or mindfully attend to images of tasty and healthy foods.  In a fast event-related design, a subsequent choice phase asked participants to make speeded choices about whether to eat pictured foods (both tasty and healthy, half from the training phase, half novel).  The results largely supported our hypotheses.  Using the breadth of activation relative to well-matched active baselines (rather than signal intensity relative to resting state baselines), we established a large distributed neural network for food processing that grounds the diverse aspects of food consumption simulations, including the ventral food reward network (taste, olfaction, reward, attention), mentalizing (along the cortical midline), and embodiment including action (across the motor system).  This distributed network was active for both training and choice, for both tasty and healthy foods, for both repeated and novel foods.  Left-hemisphere language areas were also active (although not predicted), implicating linguistic processing of food cues, especially during the training phase for the mindful attention group.  As predicted, tasty foods produced greater neural activity across food processing areas than healthy foods during the training phase.  Surprisingly the choice phase exhibited the opposite pattern, with healthy foods producing larger activations.  Most importantly, mindful attention, relative to normal viewing, produced more neural activity while processing foods during the training phase, but much less neural activity during the subsequent choice phase.  Increased up-front processing for mindful attention during training later led to a large processing off-load during food choice.  Moreover, this effect of mindful attention was much larger for tasty foods than for healthy foods, perhaps because tasty foods offer more conceptual content for mindful attention to process.  Finally, mindful attention operated both as a general cognitive set (generalizing to novel foods) and also via food-specific memories (repetition effect), suggesting two mechanisms that underlie mindful attention effects.  These results shed new light on the mechanisms that underlie early mindfulness practice, while raising many issues for future research.

Source:  Emory University Libraries,   https://etd.library.emory.edu/concern/etds/bn9997608?locale=en