A Neurocognitive Investigation of the Role of Reinforcement Learning in Updating Dysfunctional Self-schema in Depression: A Putative Mechanism for Ant
Lead Research Organisation:
University of Bath
Department Name: Psychology
Abstract
Context of Research
Depression is commonly characterised by negative evaluations of the self (e.g. I feel like I am a failure). Cognitive theories emphasise the importance of these negative self-schemas in the development and maintenance of depression (1). Negative self-schemas have been found to be an independent risk factor for depression (2). However, the mechanisms underlying the development of these schemas remain unclear.
Social reinforcement learning is one process thought to develop and maintain self-schemas (3). Social interactions are dependent on ambiguous social cues (e.g. facial expressions) and are therefore open to interpretation (e.g. this person likes or dislikes me) (4). Individuals with depression have been found to exhibit negative emotional processing biases, such as greater recall for negative words and selective attention towards negative facial expressions (5). However, to date little research has been done on emotional biases in relation to social reinforcement learning specifically.
Treatments for depression, such as antidepressants, are thought to work in part by remediating negative emotional processing biases (6). Rather than interpreting social cues as indicative of negative evaluation by others (e.g. that person laughed at what I said, they must think I am stupid), cues are instead positively processed (e.g. that person laughed at what I said, they must think I am funny). However, again little research has been undertaken to test this hypothesis.
Aims & Objectives
Aims: To understand the role of reinforcement learning in updating dysfunctional self-schemas in depression.
Objectives:
1. To examine the relationship between reinforcement learning and depressive self-schemas.
2. To examine changes in self-biases within reinforcement learning over the course of antidepressant treatment.
Applications and Benefits
While effective treatments are available for depression, large numbers of patients do not achieve remission, and relapse is common (7, 8). Understanding the role of negative self-schemas in antidepressant treatment would provide us with important information regarding treatment mechanisms, contributing to the development of more effective treatments. Establishing the relationship between social reinforcement learning and negative self-schemas may also allow us to develop preventative treatments to prevent the development of negative self-schemas and subsequently depression.
Relevance to the research council
This research can be categorised under the MRC's Neuroscience and Mental Health funding area, specifically the 'mental health research' and 'cognitive and behavioural neuroscience and cognitive systems' subsections. This project meets the MRC's aims to 'understand the causes and drivers of mental health' and contribute towards an 'enhanced understanding of the causes of mental illness' (9).
Note: references can be provided upon request as they exceeded required word count.
Depression is commonly characterised by negative evaluations of the self (e.g. I feel like I am a failure). Cognitive theories emphasise the importance of these negative self-schemas in the development and maintenance of depression (1). Negative self-schemas have been found to be an independent risk factor for depression (2). However, the mechanisms underlying the development of these schemas remain unclear.
Social reinforcement learning is one process thought to develop and maintain self-schemas (3). Social interactions are dependent on ambiguous social cues (e.g. facial expressions) and are therefore open to interpretation (e.g. this person likes or dislikes me) (4). Individuals with depression have been found to exhibit negative emotional processing biases, such as greater recall for negative words and selective attention towards negative facial expressions (5). However, to date little research has been done on emotional biases in relation to social reinforcement learning specifically.
Treatments for depression, such as antidepressants, are thought to work in part by remediating negative emotional processing biases (6). Rather than interpreting social cues as indicative of negative evaluation by others (e.g. that person laughed at what I said, they must think I am stupid), cues are instead positively processed (e.g. that person laughed at what I said, they must think I am funny). However, again little research has been undertaken to test this hypothesis.
Aims & Objectives
Aims: To understand the role of reinforcement learning in updating dysfunctional self-schemas in depression.
Objectives:
1. To examine the relationship between reinforcement learning and depressive self-schemas.
2. To examine changes in self-biases within reinforcement learning over the course of antidepressant treatment.
Applications and Benefits
While effective treatments are available for depression, large numbers of patients do not achieve remission, and relapse is common (7, 8). Understanding the role of negative self-schemas in antidepressant treatment would provide us with important information regarding treatment mechanisms, contributing to the development of more effective treatments. Establishing the relationship between social reinforcement learning and negative self-schemas may also allow us to develop preventative treatments to prevent the development of negative self-schemas and subsequently depression.
Relevance to the research council
This research can be categorised under the MRC's Neuroscience and Mental Health funding area, specifically the 'mental health research' and 'cognitive and behavioural neuroscience and cognitive systems' subsections. This project meets the MRC's aims to 'understand the causes and drivers of mental health' and contribute towards an 'enhanced understanding of the causes of mental illness' (9).
Note: references can be provided upon request as they exceeded required word count.
People |
ORCID iD |
| Catherine HOBBS (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013794/1 | 01/10/2016 | 30/09/2025 | |||
| 1939169 | Studentship | MR/N013794/1 | 01/10/2017 | 11/02/2022 | Catherine HOBBS |
| Description | GW4 Biomed MRC DTP UK National Productivity Investment Fund |
| Amount | £6,268 (GBP) |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 01/2020 |
| Description | MQ 2018 Mental Health Science Meeting Travel Award |
| Amount | £250 (GBP) |
| Organisation | MQ Mental Health Research |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 02/2018 |
| End | 02/2018 |
| Description | MQ 2019 Mental Health Science Meeting Travel Award |
| Amount | £300 (GBP) |
| Organisation | MQ Mental Health Research |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 02/2019 |
| End | 02/2019 |
| Title | Dataset for "Is depression associated with reduced optimistic belief updating?" |
| Description | We evaluated optimistic belief updating (the finding that healthy individuals update their beliefs more following good news than bad) for positive and negative life events in individuals experiencing depression (n = 54) and healthy controls (n = 56). We have provided the raw questionnaire data (depression, anxiety, optimism) and belief updating task, as well as the cleaned data used for analysis. Code for cleaning and analysing data is available for use in R. Based on this data we found that whereas healthy participants updated their beliefs more following good news than bad, individuals experiencing depression lacked this bias. Our findings for positive life events were inconclusive, but on balance suggested that reduced optimistic belief updating in depression did not occur. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Accompanying publication |
| URL | https://researchdata.bath.ac.uk/id/eprint/1078 |
| Title | Dataset for "The effect of acute citalopram on self-referential emotional processing and social cognition in healthy volunteers" |
| Description | This dataset is for a study examining whether acute administration of citalopram is associated with an increase in positive affective learning biases about the self and increases in prosocial behaviour. 41 healthy volunteers were randomised to either an acute 20 mg dose of citalopram (n = 20) or matched placebo (n = 21) in a between-subjects double-blind design. Participants completed computer-based cognitive tasks designed to measure referential affective processing, social cognition and expression of prosocial behaviours. This included a prisoners' dilemma task, the social evaluation learning task, a referential categorisation and recall task, an affective go/no-go association task and simple associative learning tasks. Participants also completed trait measures of mood and personality at baseline, and state measures of mood and side effects at baseline, post-drug and post-testing timepoints. Data for all questionnaire and cognitive task measures are included in this dataset in both raw and aggregated formats. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Accompanying publication |
| URL | https://researchdata.bath.ac.uk/id/eprint/891 |
| Title | Dataset for 'Self-processing in relation to emotion and reward processing in depression' |
| Description | This dataset is for a study examining the role of self processing in relation to emotion and reward processing in depression. Participants (n = 144) with varying levels of depression symptoms completed cognitive tasks measuring self processing independently and in combination with emotion and reward processing over two session approximately one week apart. This dataset contains the raw trial level and cleaned aggregate data used for analysis for each of these cognitive tasks (Associative Learning, Go/No-Go Self-Esteem, Social Evaluation Learning), self-report questionnaire data for mood, demographics and output from the clinical interview schedule-revised (.csv, .xlsx and .RDA files). Code used for cleaning and analysis is also provided in the form of R Notebook files. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Accompanying publication |
| URL | https://researchdata.bath.ac.uk/id/eprint/924 |
| Description | Visiting PhD Student Placement with the Psychopharmacology and Emotion Research Laboratory, Department of Psychiatry, University of Oxford |
| Organisation | University of Oxford |
| Department | Department of Psychiatry |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | I undertook a 3 month placement at the Psychopharmacology and Emotion Research Laboratory, Department of Psychiatry, University of Oxford. During this placement I conducted a randomised placebo-controlled double blind study of the influence of acute citalopram on self-referential emotional processing biases in healthy volunteers. My role in this research included developing the research question and study design, setting up the study including ethics and creation of materials, pre-registration of the study on open science framework, recruitment, screening and data collection. I will also be responsible for data analysis and writing up the study for publication. |
| Collaborator Contribution | Professor Catherine Harmer at the Psychopharmacology and Emotion Research Laboratory, Department of Psychiatry, University of Oxford hosted the placement. PERL provided the testing facilities, medical supervision and drug randomization and encapsulation. |
| Impact | Hobbs, C., Button, K. S., Munafo, M. R., Sui, J., Kessler, D., & Murphy, S. E. (2019, November 1). Study: [Citalopram and self emotional processing] The effect of acute citalopram on self-referential emotional processing and social cognition in healthy volunteers . Preregistation on Open Science Framework. Retrieved from osf.io/rn79v |
| Start Year | 2019 |
| Description | Bath Taps into Science |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Schools |
| Results and Impact | The Bath Taps into Science festival focuses on inspiring and engaging children, families, and adults with science, technology, engineering and maths (STEM) and runs each year during British Science Week. Launched in Science Week 2000, Bath Taps has grown each year to a record-breaking 8,500 attendees in 2017. With 60 exciting events, there is something for everyone during the week which includes hands-on workshops, demos, activities, talks, and school-based projects. At this event I ran a demonstration of the associative learning task used as part of my PhD research. Children were able to practice the task and learn how we associate neutral objects with ourselves and others. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.bath.ac.uk/guides/bath-taps-into-science/ |
| Description | Patient and Public Involvement for an NHS study predicting antidepressant response |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Patients, carers and/or patient groups |
| Results and Impact | A patient and public involvement forum was conducted with previous service users of Bristol Wellbeing Therapies who were currently or had previously used antidepressants. The service users advised on recruitment, the tests used to measure emotional processing, and participant materials. Emphasis was placed by the service users on recruiting participants in a sensitive manner due to the difficulties faced when first beginning antidepressant treatment. The majority of service users in the forum stated that they would have considered participating in the study due to wanting to help improve current treatment for depression. |
| Year(s) Of Engagement Activity | 2018 |