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34 Neurocomputational Mechanisms of Social Reward Processing in Combat-Exposed Veterans
- Alex F. Skupny, Danielle N. Dun, Katia M. Harle, Alan N. Simmons
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 823-824
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Objective:
Combat exposure is associated with higher rates of depressive symptoms, including anhedonia (i.e., a reduced ability to seek and experience rewards) and feelings of social disconnectedness. While these symptoms are commonly documented in combat-exposed Veterans following deployment, the cognitive mechanisms underlying this pathology is less well understood. Computational modeling can provides detailed mechanistic insights into complex cognition, which may be particularly useful to understand how social reward processing is altered following combat exposure. Here, we use a Bayesian learning model framework to address this question.
Participants and Methods:Thirty-three Operation Enduring Freedom (OEF)/ Operation Iraqi Freedom (OIF)/Operation New Dawn (OND) Veterans (25 Male, 8 Female) between the ages of 18-65 years old (M = 41.61, SD = 10.49) participated in this study. In both classic/monetary and social reward conditions, participants completed a 2-arm bandit task, in which they must choose on each trial between two options (i.e., slot machine vs social partner) with unknown reward rates. While they received monetary outcomes in the classic condition, participants received compliments from different fictitious partners in the social condition. We first compared a learning-independent Win-stay/Lose-shift (WSLS) heuristic and either a Rescorla-Wagner Q-learning or a Bayesian learning model (Dynamic Belief Model/DBM) paired with a Softmax reward maximization policy. DBM+Softmax provided the best fit of the data for most participants (31/33). Individual DBM parameters of prior reward expectation, reward learning (i.e., perceived stability of reward rates), and Softmax reward maximization were estimated and compared across conditions.
Results:Participants did not differ in their reward learning parameters across monetary and social conditions (t(30)= -0.70, p = 0.490), suggesting similar perception of reward stability in both modalities. However, higher Bayesian prior mean (i.e., initial belief of reward rate; t(30)= -2.31, p = 0.028, d=0.42) and greater reward maximization (i.e., Softmax parameter; t(30)= -2.26, p = 0.031, d=0.41) were observed in response to social vs monetary rewards. In the social reward condition, higher self-reported social connectedness was associated with greater model fit of our DBM model (i.e., smaller Bayesian Information Criterion/BIC; r = -0.38, p = 0.041). In this condition, those expecting higher reward rates when initiating reward exploration (those with higher DBM prior mean) endorsed lower self-esteem (Spearman's ρ = -0.43, p = 0.078) and lower positive affect (ρ = -0.32, p = 0.078).
Conclusions:A Bayesian learning modeling framework can characterize mechanistic differences in the processing of social vs non-social reward among combat-exposed Veterans. Individuals with higher social connectedness were more model-based in their performance, consistent with the notion that they are more likely to estimate and anticipate how much social peers have to offer. Combat-exposed individuals with lower self-esteem and positive affect appear to have higher initial expectations of reward from unknown partners, which could reflect greater need for mood and/or self-esteem repair in those individuals. Overall, Bayesian modeling of social reward behavior provides a useful quantitative framework to predict clinically relevant construct of functional outcomes in military populations.
5 - PD games: death comes to planning
- Edited by Olivier Sykes, University of Liverpool, John Sturzaker, University of Hertfordshire
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- Book:
- Planning in a Failing State
- Published by:
- Bristol University Press
- Published online:
- 27 March 2024
- Print publication:
- 23 November 2023, pp 72-86
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Summary
Introduction
It is a truth universally acknowledged that a human must be in want of a healthy home. Planning has been tasked with enabling this want for over 70 years, yet in English planning’s current emaciated state, it is struggling to ensure that housing is healthy. The fortunes of England’s population are growing further apart (ONS, 2021). While all its citizens share the same human rights according to the International Convention on Economic, Social and Cultural Rights as part of the International Bill of Human Rights, the UK’s unwritten constitution does not actually include the ‘right to healthy housing’. Increasingly, it appears that the ability of citizens in England to actuate their international human right is devolved to their financial capacity. Variation in the quality of housing is not new, but the minimum quality for new housing development, which has been required since 1947, has been absent in much of a new form of housing development: permitted development.
Since 2010, there have been significant changes in the process of obtaining permission for much new housing development in England. Broadly, these deregulatory national changes have allowed the conversion and extension of housing that would not have been permitted through local planning permission.
This chapter explores the expansion of permitted development rights to allow the conversion of non-residential uses to homes. It does so with a particular focus on neighbourhood health, providing new evidence of the problematic assumption in permitted development that housing should be allowed regardless of local amenities and the built environment of existing buildings. Our neighbourhood-health-based approach should be considered complementary to the excellent extant analysis of the quality of the physical structures of dwellings by Clifford et al (2018), on which this analysis builds directly and for which it seeks to provide corroborating evidence (see, for example, Madeddu and Clifford, 2021).
A pastiche aside
Lizzy was looking forward to seeing her friend again. Travelling south from the fine county of Derbyshire, the train line cut through miles of green, purple and gold manicured moorland. Looking out the windows, which ran almost the full length of the train, Lizzy saw estates merge into pastureland, past isolated oaks and pylons strung together to provide electricity to disparate settlements. The train was quiet.
Predicting risks of physical health deterioration in a place of safety
- Alex Berry, Florence Dalton, Michael Dunning, Freddie Johansson
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- Journal:
- BJPsych Open / Volume 7 / Issue S1 / June 2021
- Published online by Cambridge University Press:
- 18 June 2021, p. S8
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Aims
Healthcare triage for those subject to section 136 powers (MHA 1983/2007) remains challenging. Camden and Islington NHS Foundation Trust opened a dedicated Health-Based Place of Safety (HBPOS) in 2020, situated separately from an emergency department (ED). There was concern that this may lead to physical health problems going unrecognised. We aimed to design a simple, efficient algorithm to be used by non-medically-trained staff to identify those who are subject to s.136 powers who would benefit from medical clearance before being admitted to the HBPOS
MethodWe chaired a consensus meeting with nursing staff, police and emergency medicine consultants when designing the algorithm. Case notes of those presenting under s.136 to the POS over 1 calendar-month in 2019 were reviewed, and the proportion of those who the algorithm would have diverted for medical clearance was calculated. We then reviewed the proportion of cases sent for medical clearance during a single calendar month in 2020, after the HBPOS had opened, to see whether there was a significant difference.
Result37 patients were admitted to the ED-based POS in July 2019, of which 36 records were analysed. 9 patients (25%) were referred for medical clearance, with 2 (6%) requiring medical admission. 8.6% were identified as needing medical clearance when the algorithm was applied retrospectively (positive predictive value 66%, negative predictive value = 79%).
Review of records over 1 calendar-month after the HBPOS was established showed 30.6% of patients had been diverted for medical clearance prior to entering the HBPOS. Of the 65 patients, 1 (2%) required transfer to ED within 48 hours of entry. No statistical difference in the proportion of patients sent for medical clearance was observed since the formation of the HBPOS away from the ED (Chi-squared = 0.549, p = 0.458), suggesting the algorithm successfully identified those patients who needed medical clearance prior to admission.
We observed high rates of intoxication amongst those admitted (30–40%).
ConclusionThe algorithm showed high specificity and negative predictive value, allowing for a degree of confidence when admitting those deemed at low-risk of physical deterioration, though it does not eliminate the need for clinical judgement. Interpretation of the results is complicated by the COVID19 pandemic in 2020, which was not accounted for in the algorithm, which possibly led to deviations from the algorithm in real-world clinical practice.
An ultra-wide bandwidth (704 to 4 032 MHz) receiver for the Parkes radio telescope
- George Hobbs, Richard N. Manchester, Alex Dunning, Andrew Jameson, Paul Roberts, Daniel George, J. A. Green, John Tuthill, Lawrence Toomey, Jane F. Kaczmarek, Stacy Mader, Malte Marquarding, Azeem Ahmed, Shaun W. Amy, Matthew Bailes, Ron Beresford, N. D. R. Bhat, Douglas C.-J. Bock, Michael Bourne, Mark Bowen, Michael Brothers, Andrew D. Cameron, Ettore Carretti, Nick Carter, Santy Castillo, Raji Chekkala, Wan Cheng, Yoon Chung, Daniel A. Craig, Shi Dai, Joanne Dawson, James Dempsey, Paul Doherty, Bin Dong, Philip Edwards, Tuohutinuer Ergesh, Xuyang Gao, JinLin Han, Douglas Hayman, Balthasar Indermuehle, Kanapathippillai Jeganathan, Simon Johnston, Henry Kanoniuk, Michael Kesteven, Michael Kramer, Mark Leach, Vince Mcintyre, Vanessa Moss, Stefan Osłowski, Chris Phillips, Nathan Pope, Brett Preisig, Daniel Price, Ken Reeves, Les Reilly, John Reynolds, Tim Robishaw, Peter Roush, Tim Ruckley, Elaine Sadler, John Sarkissian, Sean Severs, Ryan Shannon, Ken Smart, Malcolm Smith, Stephanie Smith, Charlotte Sobey, Lister Staveley-Smith, Anastasios Tzioumis, Willem van Straten, Nina Wang, Linqing Wen, Matthew Whiting
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- Journal:
- Publications of the Astronomical Society of Australia / Volume 37 / 2020
- Published online by Cambridge University Press:
- 08 April 2020, e012
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We describe an ultra-wide-bandwidth, low-frequency receiver recently installed on the Parkes radio telescope. The receiver system provides continuous frequency coverage from 704 to 4032 MHz. For much of the band ( ${\sim}60\%$ ), the system temperature is approximately 22 K and the receiver system remains in a linear regime even in the presence of strong mobile phone transmissions. We discuss the scientific and technical aspects of the new receiver, including its astronomical objectives, as well as the feed, receiver, digitiser, and signal processor design. We describe the pipeline routines that form the archive-ready data products and how those data files can be accessed from the archives. The system performance is quantified, including the system noise and linearity, beam shape, antenna efficiency, polarisation calibration, and timing stability.
Contributors
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- By Graeme J.M. Alexander, Heung Bae Kim, Michael Burch, Andrew J. Butler, Tanveer Butt, Roy Calne, Edward Cantu, Robert B. Colvin, Paul Corris, Charles Crawley, Hiroshi Date, Francis L. Delmonico, Bimalangshu R. Dey, Kate Drummond, John Dunning, John D. Firth, John Forsythe, Simon M. Gabe, Robert S. Gaston, William Gelson, Paul Gibbs, Alex Gimson, Leo C. Ginns, Samuel Goldfarb, Ryoichi Goto, Walter K. Graham, Simon J.F. Harper, Koji Hashimoto, David G. Healy, Hassan N. Ibrahim, David Ip, Fadi G. Issa, Neville V. Jamieson, David P. Jenkins, Dixon B. Kaufman, Kiran K. Khush, Heung Bae Kim, Andrew A. Klein, John Klinck, Camille Nelson Kotton, Vineeta Kumar, Yael B. Kushner, D. Frank. P. Larkin, Clive J. Lewis, Yvonne H. Luo, Richard S. Luskin, Ernest I. Mandel, James F. Markmann, Lorna Marson, Arthur J. Matas, Mandeep R. Mehra, Stephen J. Middleton, Giorgina Mieli-Vergani, Charles Miller, Sharon Mulroy, Faruk Özalp, Can Ozturk, Jayan Parameshwar, J.S. Parmar, Hari K. Parthasarathy, Nick Pritchard, Cristiano Quintini, Axel O. Rahmel, Chris J. Rudge, Stephan V.B. Schueler, Maria Siemionow, Jacob Simmonds, Peter Slinger, Thomas R. Spitzer, Stuart C. Sweet, Nina E. Tolkoff-Rubin, Steven S.L. Tsui, Khashayar Vakili, R.V. Venkateswaran, Hector Vilca-Melendez, Vladimir Vinarsky, Kathryn J. Wood, Heidi Yeh, David W. Zaas, Jonathan G. Zaroff
- Edited by Andrew A. Klein, Clive J. Lewis, Joren C. Madsen
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- Book:
- Organ Transplantation
- Published online:
- 07 September 2011
- Print publication:
- 11 August 2011, pp vii-x
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