A framework and toolkit for understanding impulsive action
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: Sch of Psychology
Abstract
What is the importance?
Impulsivity is a key factor in understanding individual behaviour in many spheres, from economics to health and wellbeing, from psychiatry to social disorder. It is associated with many problem behaviours, such as alcoholism, obesity, ADHD, binge drinking or eating, drug misuse, gambling, aggression, violent or sexual abuse and risky sex. Advances in understanding the different aspects of impulsivity - and how best to measure them - will have immediate application for attempts to modify these behaviours.
Our focus.
Tasks that measure impulsivity divide into two types: those measuring performance in the control or inhibition of responses, and those measuring preferences for risk or reward immediacy. We concentrate on the former category: response control.
What is the need?
Response control tasks are increasingly popular, since they are thought to tap basic mechanisms underlying a variety of problematic behaviours (as mentioned above). However, progress is inefficient because we lack a clear framework for understanding why people differ in these tasks, and what such differences actually mean for real-world behaviour. The basic conclusion is that people differ in their ability to inhibit habitual responses, but this leaves unexplained why people's performance does not correlate very well across different ways of testing this ability. As increasing numbers of studies use these measures, research money is wasted because there is no commensurate advance in understanding.
How will we solve this problem?
1. Underpinning. Part of the problem is that some essential work on these tasks has not been done. Most of the tasks have been transferred from studies of group averages, where individual differences are treated as irrelevant. Thus we do not even know which tasks best reflect stable traits (and how many trials are needed to do so). Nor do we know whether the important thing for generalisable response control is how quickly people catch on to the task, or how well they perform once the task is well practised.
2. Applying computational modelling to provide a new framework. Part of the problem is that the tasks assess surrogates of what we really want to measure - the underlying cognitive mechanisms that contribute to impulsive behaviour. There are at least two separate factors related to impulsivity that influence performance on these tasks - how cautious someone is and how good they are at inhibiting the wrong response (or selecting the right one). When these factors interact, the results become difficult to interpret, and the approach of using patterns of correlation across different tasks to extract underlying variables has proved insufficient in this case. We will instead use computational models of action decision, which utilise the full richness of the behavioural data, and can help us to understand us how complex and counterintuitive results occur.
3. Tools to inform intervention. In order to successfully help people with different kinds of detrimental impulsivity (whether in the context of addiction, violence, debt, school performance or many other areas), researchers, professional psychologists, economists, clinicians, etc., need tools that distil the best scientific knowledge into packages that can be easily administered without specialist knowledge (e.g., of computational modelling). We will translate our findings into such tools: the Model-Based Impulsivity Toolkit (ModBIT).
Impulsivity is a key factor in understanding individual behaviour in many spheres, from economics to health and wellbeing, from psychiatry to social disorder. It is associated with many problem behaviours, such as alcoholism, obesity, ADHD, binge drinking or eating, drug misuse, gambling, aggression, violent or sexual abuse and risky sex. Advances in understanding the different aspects of impulsivity - and how best to measure them - will have immediate application for attempts to modify these behaviours.
Our focus.
Tasks that measure impulsivity divide into two types: those measuring performance in the control or inhibition of responses, and those measuring preferences for risk or reward immediacy. We concentrate on the former category: response control.
What is the need?
Response control tasks are increasingly popular, since they are thought to tap basic mechanisms underlying a variety of problematic behaviours (as mentioned above). However, progress is inefficient because we lack a clear framework for understanding why people differ in these tasks, and what such differences actually mean for real-world behaviour. The basic conclusion is that people differ in their ability to inhibit habitual responses, but this leaves unexplained why people's performance does not correlate very well across different ways of testing this ability. As increasing numbers of studies use these measures, research money is wasted because there is no commensurate advance in understanding.
How will we solve this problem?
1. Underpinning. Part of the problem is that some essential work on these tasks has not been done. Most of the tasks have been transferred from studies of group averages, where individual differences are treated as irrelevant. Thus we do not even know which tasks best reflect stable traits (and how many trials are needed to do so). Nor do we know whether the important thing for generalisable response control is how quickly people catch on to the task, or how well they perform once the task is well practised.
2. Applying computational modelling to provide a new framework. Part of the problem is that the tasks assess surrogates of what we really want to measure - the underlying cognitive mechanisms that contribute to impulsive behaviour. There are at least two separate factors related to impulsivity that influence performance on these tasks - how cautious someone is and how good they are at inhibiting the wrong response (or selecting the right one). When these factors interact, the results become difficult to interpret, and the approach of using patterns of correlation across different tasks to extract underlying variables has proved insufficient in this case. We will instead use computational models of action decision, which utilise the full richness of the behavioural data, and can help us to understand us how complex and counterintuitive results occur.
3. Tools to inform intervention. In order to successfully help people with different kinds of detrimental impulsivity (whether in the context of addiction, violence, debt, school performance or many other areas), researchers, professional psychologists, economists, clinicians, etc., need tools that distil the best scientific knowledge into packages that can be easily administered without specialist knowledge (e.g., of computational modelling). We will translate our findings into such tools: the Model-Based Impulsivity Toolkit (ModBIT).
Planned Impact
Who will benefit from this research, and how?
Beyond the academics involved in impulsivity research (see Academic Beneficiaries), our Model-Based Impulsivity Toolkit (ModBIT) will benefit charities, social workers, professional psychologists (educational, occupational, clinical), economists, and health and rehabilitation professionals who are engaged in developing interventions for problem behaviours associated with impulsivity and response control. These impulsivity-related behaviours are widespread, and include gambling, excess alcohol consumption, drug-taking, smoking, excessive eating, risky sexual behaviour, aggression and violence, poor school attendance and performance. Impulsivity is also implicated in most mental health problems, many crimes, severe debt and marital breakdown. How exactly different aspects of impulsivity, and particularly response control (upon which we focus here), underlie each of these areas is not yet well understood, and neither is it well understood how individual differences in response control should be best measured. Successful interventions are severely hampered by this lack of knowledge. As such, the project has direct potential to improve the nation's health and wealth.
The ultimate beneficiaries will be those people who are currently suffering as a result of impulsivity-related behaviour. The sufferers include both the individuals carrying out the impulsive behaviours, and those affected by them, whether they are direct victims of crime or aggression, or more widely, belong to families or communities affected, for example, by debt, excessive alcohol consumption or any other of the impulsivity related problems. The majority of the population will be directly or indirectly affected by at least one of these issues, or have friends and relatives who are. Financially, impulsivity-related behaviour costs the nation billions of pounds annually (alcohol-related cost alone is estimated to be £20bn-55bn by the House of Commons Health Committee, 2009-10).
Better interventions and better knowledge of these problems will allow policy makers and budget holders to be better informed for optimally directing policy and efficiently making use of limited budgets.
The timescale is likely to be as follows. Within the project itself, as soon as objective 1 is complete, other ongoing research projects will be better informed, and therefore will benefit from improved likelihood of success. Cumulatively, this effect could be very great, since there are over 1000 research publications on impulsivity each year, many of these using response control tasks and currently limited by the lack of guidance for their optimal use (see e.g. the CNTRICS project for an example of how researchers in high-cost large-scale projects must currently choose tasks without good psychometric information, http://schizophreniabulletin.oxfordjournals.org/content/35/1/115.full).
After the completion of objectives 2 and 3 (i.e., towards the end of the project), the researcher-oriented parts of our toolkit (ModBIT) will be available to further support this research effort, based on the successful example set by brain imaging research (see pathways to impact). At this point, the prototype will be developed for wider use by non-academics: charities, social workers, professional psychologists (educational, occupational, clinical), economists, and health and rehabilitation professionals. This is the point at which marked improvements to intervention, and more precise measurement of such improvements, will become possible. We will apply for knowledge transfer funds to further develop the toolkit in conjunction with specific users.
Beyond the academics involved in impulsivity research (see Academic Beneficiaries), our Model-Based Impulsivity Toolkit (ModBIT) will benefit charities, social workers, professional psychologists (educational, occupational, clinical), economists, and health and rehabilitation professionals who are engaged in developing interventions for problem behaviours associated with impulsivity and response control. These impulsivity-related behaviours are widespread, and include gambling, excess alcohol consumption, drug-taking, smoking, excessive eating, risky sexual behaviour, aggression and violence, poor school attendance and performance. Impulsivity is also implicated in most mental health problems, many crimes, severe debt and marital breakdown. How exactly different aspects of impulsivity, and particularly response control (upon which we focus here), underlie each of these areas is not yet well understood, and neither is it well understood how individual differences in response control should be best measured. Successful interventions are severely hampered by this lack of knowledge. As such, the project has direct potential to improve the nation's health and wealth.
The ultimate beneficiaries will be those people who are currently suffering as a result of impulsivity-related behaviour. The sufferers include both the individuals carrying out the impulsive behaviours, and those affected by them, whether they are direct victims of crime or aggression, or more widely, belong to families or communities affected, for example, by debt, excessive alcohol consumption or any other of the impulsivity related problems. The majority of the population will be directly or indirectly affected by at least one of these issues, or have friends and relatives who are. Financially, impulsivity-related behaviour costs the nation billions of pounds annually (alcohol-related cost alone is estimated to be £20bn-55bn by the House of Commons Health Committee, 2009-10).
Better interventions and better knowledge of these problems will allow policy makers and budget holders to be better informed for optimally directing policy and efficiently making use of limited budgets.
The timescale is likely to be as follows. Within the project itself, as soon as objective 1 is complete, other ongoing research projects will be better informed, and therefore will benefit from improved likelihood of success. Cumulatively, this effect could be very great, since there are over 1000 research publications on impulsivity each year, many of these using response control tasks and currently limited by the lack of guidance for their optimal use (see e.g. the CNTRICS project for an example of how researchers in high-cost large-scale projects must currently choose tasks without good psychometric information, http://schizophreniabulletin.oxfordjournals.org/content/35/1/115.full).
After the completion of objectives 2 and 3 (i.e., towards the end of the project), the researcher-oriented parts of our toolkit (ModBIT) will be available to further support this research effort, based on the successful example set by brain imaging research (see pathways to impact). At this point, the prototype will be developed for wider use by non-academics: charities, social workers, professional psychologists (educational, occupational, clinical), economists, and health and rehabilitation professionals. This is the point at which marked improvements to intervention, and more precise measurement of such improvements, will become possible. We will apply for knowledge transfer funds to further develop the toolkit in conjunction with specific users.
Organisations
- CARDIFF UNIVERSITY (Lead Research Organisation)
- Georgia Institute of Technology (Collaboration)
- Leiden University (Collaboration)
- University of Sheffield (Collaboration)
- University of Zurich (Collaboration)
- University of California, Irvine (Collaboration)
- Arizona State University (Collaboration)
- Dartmouth Institute for Health Policy and Clinical Practice (Collaboration)
- Science Media Centre (Collaboration)
Publications
Harrison JJ
(2015)
Quick phases of infantile nystagmus show the saccadic inhibition effect.
in Investigative ophthalmology & visual science
Hedge C
(2019)
Slow and steady? Strategic adjustments in response caution are moderately reliable and correlate across tasks.
in Consciousness and cognition
Hedge C
(2018)
Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations.
in Psychological bulletin
Hedge C
(2018)
The mapping between transformed reaction time costs and models of processing in aging and cognition.
in Psychology and aging
Hedge C
(2020)
Self-reported impulsivity does not predict response caution.
in Personality and individual differences
Hedge C
(2018)
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.
in Behavior research methods
Hedge C
(2022)
Strategy and processing speed eclipse individual differences in control ability in conflict tasks.
in Journal of experimental psychology. Learning, memory, and cognition
Megardon G
(2017)
Trajectory curvature in saccade sequences: spatiotopic influences vs. residual motor activity.
in Journal of neurophysiology
Mégardon G
(2015)
Limitations of short range Mexican hat connection for driving target selection in a 2D neural field: activity suppression and deviation from input stimuli.
in Frontiers in computational neuroscience
Mégardon G
(2018)
The fate of nonselected activity in saccadic decisions: distinct goal-related and history-related modulation
in Journal of Neurophysiology
Description | We have discovered that laboratory measures of impulsivity are not nearly as reliable as previously thought, and that this is actually expected from the way they are designed. We have discovered that measures that use error rates do not correlate with measures that use reaction time, even within the same task. Moreover, this is explained and expected by decision models. We have shown that common ways to interpret age-related slowing are confounded, according to decision models. We have shown that a common response control trait would not necessarily manifest in visible correlations across tasks, challenging interpretations of correlational structure. We have shown that where correlations are found, they are mainly driven by general speed and strategy, not by impulse control. Together all these findings mean that researchers are changing the way they try to measure cognitive control and other cognitive traits in many research areas (clinical, imaging, behavioural, developmental). |
Exploitation Route | Academics are taking them forward in research design and interpretation (as evidenced in over 500 citations so far of the published works associated with this project). |
Sectors | Communities and Social Services/Policy Education Healthcare Other |
Description | Multi-scale and multi-modal assessment of coupling in the healthy and diseased brain. |
Amount | £4,953,467 (GBP) |
Funding ID | 104943/Z/14/Z |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 06/2016 |
End | 06/2023 |
Description | Improving dissemination of health research in news |
Organisation | Dartmouth Institute for Health Policy and Clinical Practice |
Country | United States |
Sector | Hospitals |
PI Contribution | Collaborative research and impact project |
Collaborator Contribution | data analysis, idea generation, publication. |
Impact | Publication. |
Start Year | 2019 |
Description | Improving health news in Uk and Netherlands |
Organisation | Leiden University |
Country | Netherlands |
Sector | Academic/University |
PI Contribution | research collaboration |
Collaborator Contribution | research collaboration |
Impact | publication |
Start Year | 2017 |
Description | cognitive control network |
Organisation | Arizona State University |
Country | United States |
Sector | Academic/University |
PI Contribution | Invited to contribute data, analysis and discussion to joint paper by a multi-lab network. |
Collaborator Contribution | data, analysis and discussion to joint paper by a multi-lab network. |
Impact | paper in submission. |
Start Year | 2020 |
Description | cognitive control network |
Organisation | Georgia Institute of Technology |
Country | United States |
Sector | Academic/University |
PI Contribution | Invited to contribute data, analysis and discussion to joint paper by a multi-lab network. |
Collaborator Contribution | data, analysis and discussion to joint paper by a multi-lab network. |
Impact | paper in submission. |
Start Year | 2020 |
Description | cognitive control network |
Organisation | University of California, Irvine |
Country | United States |
Sector | Academic/University |
PI Contribution | Invited to contribute data, analysis and discussion to joint paper by a multi-lab network. |
Collaborator Contribution | data, analysis and discussion to joint paper by a multi-lab network. |
Impact | paper in submission. |
Start Year | 2020 |
Description | cognitive control network |
Organisation | University of Sheffield |
Department | Sheffield Biorepository |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Invited to contribute data, analysis and discussion to joint paper by a multi-lab network. |
Collaborator Contribution | data, analysis and discussion to joint paper by a multi-lab network. |
Impact | paper in submission. |
Start Year | 2020 |
Description | cognitive control network |
Organisation | University of Zurich |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | Invited to contribute data, analysis and discussion to joint paper by a multi-lab network. |
Collaborator Contribution | data, analysis and discussion to joint paper by a multi-lab network. |
Impact | paper in submission. |
Start Year | 2020 |
Description | improving UK health press releases |
Organisation | Science Media Centre |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | research design, implementation, dissemination |
Collaborator Contribution | consultation, expertise, dissemination, impact (guideline implementation) |
Impact | publications. |
Start Year | 2016 |
Description | Brain Games and Psychology Conference events in partnership with Techniquest |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | We are involved in Cardiff University Brain Games - and outreach program for inspiring school children to take an interest in psychology and neuroscience. We have led an arm of this partnering with Techniquest to run events for A level children (about 300 children so far). Previously Brain Games was entirely aimed at primary schools. |
Year(s) Of Engagement Activity | 2014,2015,2016,2017,2018 |
Description | CPD for teachers, providing tools for teaching research skills at A level. |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | We ran a CPD for Psychology teachers in South Wales, where we provided new material and training for different parts of the A level syllabus, including making research methods fun using news stories, as well as topics such as mental health and impulsivity. |
Year(s) Of Engagement Activity | 2017 |