Categories
News

New Paper on Beliefs, Compulsion and Control is out in PlosCB!

PEAC Lab

Psychiatry, Embodiment and Computation Lab

Beliefs, compulsive behavior and reduced confidence in control

NEW PAPER ALERT!

OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threats or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple symptom-provoking scenario. Our results demonstrate that a single belief disturbance–a lack of confidence in the effectiveness of one’s preventive (harm-avoiding) actions–can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD, including the role of not-just-right feelings, the link to intolerance to uncertainty, perfectionism, and overestimation of threat, and deficits in reversal and state learning. Our simulations shed new light on which underlying beliefs drive compulsive behavior and highlight the important role of perceived ability to exert control for OCD.

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News

New Preprint on the Internal Basis of Reward

PEAC Lab

Psychiatry, Embodiment and Computation Lab

Rethinking reinforcement learning: The interoceptive origin of reward

NEW PREPRING ALERT!

Rewards play a crucial role in sculpting all motivated behavior. Traditionally, research on reinforcement learning has centered on how rewards guide learning and decision-making. Here, we examine the origins of rewards themselves. Specifically, it is now well-recognized that the critical reinforcing signal for food is generated internally and subliminally during the process of digestion. As such, a shift in our understanding of rewards as an immediate sensory gratification to a state-dependent evaluation of an action’s impact on vital physiological processes is called for. We integrate this perspective into a revised reinforcement learning framework that recognizes the subliminal nature of biological rewards and their dependency on internal states and goals.

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News

Dr. Petzschner wins MIT prize

PEAC Lab

Psychiatry, Embodiment and Computation Lab

Dr. Petzschner win’s MIT Faculty Founder Prize

AWARD

THE SOMA TEAM WON THE PITCH COMPETITION OF THE NEMIC MED TECH LEADERSHIP PROGRAM

Dr. Petzschner has received a $100,000 prize for her work to commercialize an app to better identify and treat chronic pain. She received the prize from MIT’s Faculty Founder Initiative during its award ceremony on May 2.

MIT launched the Faculty Founder Initiative in 2020 to help increase the number of woman-founded startups. This year, the initiative invited Brown University faculty to participate and pitch their innovative ideas to a group of leaders from academia, biotechnology, and venture capital.  

“Through the Future Founders Initiative, MIT and our funders are investing in amazing women faculty and their labs to provide a path for women’s discoveries and make it all the way to impact the challenging work of company creation,” said MIT President Emerita Susan Hockfield at Thursday’s ceremony.  

Petzschner was recognized as a runner-up in this year’s competition for her work to develop the SOMA app. Petzschner is also the co-director of BRAINSTORM, a program within the Carney Institute’s Center for Computational Brain Science. The BRAINSTORM program supports the application and commercialization of tools from computational brain science to improve mental health and well-being.

“One out of five adults in the United States are currently suffering from chronic pain and many end up in pain for over a decade without getting the appropriate care,” she said. “As pain becomes chronic, it is accompanied by plastic changes at the level of your brain that then lead to the persistence of pain even after the tissue has already healed. “  

“Instead of exclusively focusing on treating the body, we need to focus on the brain as well. The SOMA App is a digital therapeutic that focuses on delivering cognitive practices to chronic pain patients. Exercises, mind body exercises, treatments and psychoeducation are delivered directly to the patient via smartphone,” said Petzschner.  

“Our founding team of neuroscientists, physicians and AI developers is building active partnerships with pharma companies and hospital networks with the long-term goal of creating a platform for physician pain care with the right patient getting the right treatment at the right time.”  

Check out the announcement by MIT:

 

SOMA App is freely available on the App Store and Google Play Store.

 

Categories
News

SOMA App wins Pitch Competition

PEAC Lab

Psychiatry, Embodiment and Computation Lab

SOMA win’s the Pitch Competitions

AWARD

THE SOMA TEAM WON THE PITCH COMPETITION OF THE NEMIC MED TECH LEADERSHIP PROGRAM

Dr. Frederike Petzschner presenting SOMA at NEMIC Med Tech Leadership Program.

 

Check out the announcement by NEMIC:

 

 

SOMA App is freely available on the App Store and Google Play Store:

 

Categories
News

The SOMA App is out now!

PEAC Lab

Psychiatry, Embodiment and Computation Lab

The SOMA App is out now!

New Release

ABOUT SOMA

Every fifth American suffers from Chronic pain – that is pain that persists for more than 3-6 months – and many more are at risk of developing Chronic pain after severe injuries or surgeries or infections such as COVID-19. Once pain has become chronic, it is very hard to treat, resulting in devastating consequences for the individuals, their families and society as a whole including a non-negligible contribution to the opioid crisis in this country.

New neuroscientific research suggests that chronic pain may be a manifestation of plastic changes in brain circuits that are involved in learning and memory. Yet most treatments for pain still focus almost exclusively on the periphery and not the brain. Understanding the role of learning and expectations for pain may be key to predicting who is at risk of transitioning from acute to chronic pain and building better treatments for those that already suffer from persistent pain. 

This is why we spent the last 2 years on developing SOMA.

SOMA is designed to directly support individuals with chronic pain while also gathering the data that will help researchers better predict which patients’ pain will become chronic. The name stems from the Greek word for “body, entire person,” signifying that the app takes a holistic approach to pain. SOMA allows users to track their pain, treatment, and activities—including which efforts affect their pain in positive or negative ways. 

The SOMA App is freely available on the App Store and Google Play Store:

 

Categories
Task

Distance Estimation Task

PEAC Lab

Psychiatry, Embodiment and Computation Lab

Distance Estimation Task​

TASK DESCRIPTION

SUMMARY

The distance estimation task is a simple production-reproduction paradigm. Each trial started with an instruction for participants to move forward along a linear path while keeping track of their self-displacement. Direction of movement during production was indicated by a visual cue at the horizon (the red ball). When participants reached the sample distance dp, movement was automatically stopped and disabled for a few seconds. Subsequently, participants were instructed to reproduce the perceived distance and indicate their final position via button press. In all trials, velocity was kept constant during movement,
but changed randomly up to plus/minus 60% (scaling factor drawn from a normal distribution) between production and reproduction phases to exclude time estimation strategies to solve the task.

Distance Estimation Task

Experimental Design

In the paper listed below, participants were invited to return on seperate days and to test the effect of prior experience only, the settings for the three conditions were the same except that the sample distances were drawn from three different underlying uniform sample distributions, specified as small displacements (dp = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] m), intermediate displacements (dp = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14] m) and large displacements range (dp = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19] m).

The sample distributions of the three conditions were chosen to be partially overlapping to test whether displacement estimation behavior differed significantly for the same sample stimulus depending on the previously experienced displacements. Participants had no knowledge about the amount of displacement they had to reach during the production phase and were naive to the condition in which they were tested.

Turning angle estimation experiment. The stimulus and settings in the angle estimation (AE) experiment were identical to the distance estimation (DE) experiment, with the following exception: participants turned
on the spot to a previously indicated direction. Turning direction was kept constant between production and reproduction to preclude the use of external cues to solve the task. The sample turning angles, alpha_p, for the three prior experience conditions were in analogy drawn from three different sample distributions specified as small displacements (alpha_p = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]°), intermediate displacements (alpha_p = [50, 60, 70, 80, 90, 100, 110, 120, 130, 140]°) and large displacements range
(alpha_p = [90, 100, 110, 120, 130, 140, 150, 160, 170, 180]°). All participants performed both types of experiment. The three conditions for DE and AE were tested in separate sessions, resulting in six test
sessions per participant.

Each session lasted between 45 and 60 min and was composed of 200 trials. The first 20 training trials per experimental condition served to familiarize participants with the VR. Feedback on the performance was given after the reproduction by displaying an object in the VR at the correct distance or turning angle and asking subjects to navigate toward this location. In the following 180 test trials, no feedback was given. Only test trials were used for data analysis. Two sessions of the same experiment type, AE or DE, were separated by at least 1 h and up to a few days. Within sessions, participants had a short break of 100 s after
100 and 150 trials. Each sample displacement was repeated 20 times per condition in randomized order. The same trial order within one condition was maintained for all participants. The order in which the three conditions for DE and AE were tested was randomized for each participant.

Experimental Setup

We presented stimuli binocular on a computer monitor (resolution, 1280 x 800; frame rate, 59 Hz) driven by an ATI Mobility Radeon HD 3400 graphics card. Experiments were conducted in complete darkness except for the illumination by the monitor. The real-time virtual reality (VR) was created using Vizard 3.0 (Worldviz) and depicted an artificial stone desert consisting of a textured ground plane, 200 scattered stones,
and a textured sky. The orientation of the ground plane texture, the position of the stones, and the starting position of the participant within the VR were randomized in each trial to prevent participants from using any of these as potential cues. The sky was simulated as a 3D dome centered on the participant’s current position so that the distance to the horizon was kept constant. The eye height in the VR was adjusted individually to the true eye height of each participant (Daum and Hecht, 2009). Participants used a multidirectional movable joystick (SPEEDLINK) to navigate.

Please cite this paper

Petzschner F.H., Glasauer S. Iterative Bayesian estimation as an explanation for regression and range effects – a study on human path integration. Journal of Neuroscience 31(47):17220-9, 2011