This project analyzes factors that influence repeated choices between two options, each of which has two possible outcomes. The options differ in their expected value, which is not known to the participant in advance. The project includes three experiments in which several factors were manipulated.

Goal

The project aims to determine the relative contribution of each manipulated factor to a participant’s subsequent choice; it can also define whether the outcome of a choice could have caused disappointment and/or regret.

Researchers

• Yaakov Kareev
• Judith Avrahami
• Maor Moshe

What we did

In progress. Data were collected, key variables include the characteristics of each option (possible values and their probabilities), the participant’s choice, the observed outcome (translated into gain or loss), the counterfactual outcome that would have occurred if the participant had chosen the other option in that round, and the participant’s subsequent choice.

This project develops person-centered models of walkability for vulnerable urban populations. The research combines a large-scale survey (about 1,000 participants) with spatial mapping of neighborhood characteristics and individual-level attributes, and aims to apply AI methods to predict the level of walkability that a specific person is likely to experience in a given neighborhood. Results will be cross-referenced with existing activity datasets to validate and refine predictions.

Goal

The project aims to build personalized walkability models that estimate, for each individual, the likelihood they will walk versus use other transport modes under specific environmental conditions by mapping spatial neighborhood characteristics alongside individual-level features and applying artificial-intelligence methods to predict a person’s walkability level; it will use a comprehensive survey of roughly 1,000 participants and cross-reference the survey results with existing activity datasets to evaluate and refine the predictive models and to identify the environmental and personal drivers of walkability for vulnerable populations.

Researchers

• Michal Farkash
• Or Rappel-Kroyzer

What we did

In progress.

This project aims to map and characterize “hot spots” of chronic pain using self-reported body-map data. The dataset includes about 200 community participants who meet chronic-pain inclusion criteria; each participant marks painful segments out of 74 body segments and assigns an intensity [0–10] to marked regions (0 used also for unmarked regions).

Goal

1) To characterize "hot spots" of pain within patterns of pain distribution and intensity (self-reported on body maps). 2) To identify clusters of people with chronic pain who share similar patterns of pain "hot spots" and/or similar distributions of pain intensity across the body. 3) To examine whether these clusters are meaningful with respect to demographic, clinical, and psychological measures, controlling for the known effect that a greater number of painful body regions is associated with worse pain outcomes.

Researchers

• Gadi Gilam
• Or Rappel-Kroyzer

What we did

In progress.

This project analyses large scale coin data to reconstruct how Byzantine mints supplied different regions across time, while explicitly accounting for biases in archaeological discovery and recording. Using the FLAME database and focused pilot datasets, we combine maps by time periods, grid-based visualizations, and statistical models to highlight meaningful changes in coin flows (for example, shifts in which mint supplies a region).

Goal

We want to (1) characterize how different mints distributed coins across regions and over time, (2) identify spatial patterns (which mints supplied which places) and temporal changes in those patterns, and (3) understand and document biases in the data so the analysis focus on questions that can be answered with the available records.

Researchers

• Lee Mordechai
• Ariel Karlinsky

What we did

In progress.

This project studies whether positive emotions that patients feel during therapy sessions help with better session experiences and improvements afterwards. The study was done on 46 patients who each received 16 sessions of Accelerated Experiential Dynamic Psychotherapy (AEDP). Patients completed questionnaires after every session about the emotions they experienced, the session’s quality, and their functioning that week. Final reports were that session positive emotions relate to better session ratings and to improved functioning the following week, and that higher average positive emotions during treatment predicted some post-treatment gains.

Goal

To test whether the amount of positive emotion patients report during AEDP sessions predicts session quality, functioning the following week, and overall treatment outcomes at post-treatment and follow-up.

Researchers

• Niv Gross
• Ariel Karlinsky

What we did

We ran a naturalistic study of 46 patients, 16 AEDP sessions each. After every session patient completed short questionnaires about emotions, session quality, and weekly functioning. The researchers analyzed the session-level data with multilevel models and a cross-lagged panel model to test whether session positive emotions predicted next-week functioning, and used regression analyses to link average positive emotions across treatment to standard outcome measures.