Pub Date : 2022-04-06eCollection Date: 2022-01-01DOI: 10.5334/cpsy.82
Rebecca Kazinka, Iris Vilares, Angus W MacDonald
This study modelled spite sensitivity, the worry that others are willing to incur a loss to hurt you, which is thought to undergird suspiciousness and persecutory ideation. Two samples performed a parametric, non-iterative trust game known as the Minnesota Trust Game (MTG). The MTG distinguishes suspicious decision-making from otherwise rational mistrust by incentivizing the player to trust in certain situations but not others. In Sample 1, 243 undergraduates who completed the MTG showed less trust as the amount of money they could lose increased. However, only for choices where partners had a financial disincentive to betray the player was variation in the willingness to trust associated with suspicious beliefs. We modified the Fehr-Schmidt (1999) inequity aversion model, which compares unequal outcomes in social decision-making tasks, to include the possibility for spite sensitivity. An anticipated partner's dislike of advantageous inequity (i.e., guilt) parameter included negative values, with negative guilt indicating spite. We hypothesized that the anticipated guilt parameter would be strongly related to suspicious beliefs. Our modification of the Fehr-Schmidt model improved estimation of MTG behavior. Furthermore, the estimation of partner's spite-guilt was highly correlated with choices associated with beliefs in persecution. We replicated our findings in a second sample. This parameter was weakly correlated with a self-reported measure of persecutory ideation in Sample 2. The "Suspiciousness" condition, unique to the MTG, can be modeled to isolate spite sensitivity, suggesting differentiation from inequity aversion or risk aversion. The MTG offers promise for future studies to quantify persecutory beliefs in clinical populations.
{"title":"A Computational Model of Non-optimal Suspiciousness in the Minnesota Trust Game.","authors":"Rebecca Kazinka, Iris Vilares, Angus W MacDonald","doi":"10.5334/cpsy.82","DOIUrl":"10.5334/cpsy.82","url":null,"abstract":"<p><p>This study modelled <i>spite sensitivity</i>, the worry that others are willing to incur a loss to hurt you, which is thought to undergird suspiciousness and persecutory ideation. Two samples performed a parametric, non-iterative trust game known as the Minnesota Trust Game (MTG). The MTG distinguishes suspicious decision-making from otherwise rational mistrust by incentivizing the player to trust in certain situations but not others. In Sample 1, 243 undergraduates who completed the MTG showed less trust as the amount of money they could lose increased. However, only for choices where partners had a financial <i>dis</i>incentive to betray the player was variation in the willingness to trust associated with suspicious beliefs. We modified the Fehr-Schmidt (1999) inequity aversion model, which compares unequal outcomes in social decision-making tasks, to include the <i>possibility for spite sensitivity</i>. An anticipated partner's dislike of advantageous inequity (i.e., guilt) parameter included negative values, with negative guilt indicating <i>spite</i>. We hypothesized that the anticipated guilt parameter would be strongly related to suspicious beliefs. Our modification of the Fehr-Schmidt model improved estimation of MTG behavior. Furthermore, the estimation of partner's spite-guilt was highly correlated with choices associated with beliefs in persecution. We replicated our findings in a second sample. This parameter was weakly correlated with a self-reported measure of persecutory ideation in Sample 2. The \"Suspiciousness\" condition, unique to the MTG, can be modeled to isolate spite sensitivity, suggesting differentiation from inequity aversion or risk aversion. The MTG offers promise for future studies to quantify persecutory beliefs in clinical populations.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-31eCollection Date: 2022-01-01DOI: 10.5334/cpsy.80
Povilas Karvelis, Andreea O Diaconescu
Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers - hopelessness, Pavlovian bias and active-escape bias - are interrelated via the drive to maximize one's model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus - norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus - serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.
{"title":"A Computational Model of Hopelessness and Active-Escape Bias in Suicidality.","authors":"Povilas Karvelis, Andreea O Diaconescu","doi":"10.5334/cpsy.80","DOIUrl":"10.5334/cpsy.80","url":null,"abstract":"<p><p>Currently, psychiatric practice lacks reliable predictive tools and a sufficiently detailed mechanistic understanding of suicidal thoughts and behaviors (STB) to provide timely and personalized interventions. Developing computational models of STB that integrate across behavioral, cognitive and neural levels of analysis could help better understand STB vulnerabilities and guide personalized interventions. To that end, we present a computational model based on the active inference framework. With this model, we show that several STB risk markers - hopelessness, Pavlovian bias and active-escape bias - are interrelated via the drive to maximize one's model evidence. We propose four ways in which these effects can arise: (1) increased learning from aversive outcomes, (2) reduced belief decay in response to unexpected outcomes, (3) increased stress sensitivity and (4) reduced sense of stressor controllability. These proposals stem from considering the neurocircuits implicated in STB: how the locus coeruleus - norepinephrine (LC-NE) system together with the amygdala (Amy), the dorsal prefrontal cortex (dPFC) and the anterior cingulate cortex (ACC) mediate learning in response to acute stress and volatility as well as how the dorsal raphe nucleus - serotonin (DRN-5-HT) system together with the ventromedial prefrontal cortex (vmPFC) mediate stress reactivity based on perceived stressor controllability. We validate the model by simulating performance in an Avoid/Escape Go/No-Go task replicating recent behavioral findings. This serves as a proof of concept and provides a computational hypothesis space that can be tested empirically and be used to distinguish planful versus impulsive STB subtypes. We discuss the relevance of the proposed model for treatment response prediction, including pharmacotherapy and psychotherapy, as well as sex differences as it relates to stress reactivity and suicide risk.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42589426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-11eCollection Date: 2022-01-01DOI: 10.5334/cpsy.78
Daniel S Barron, Stephen Heisig, Carla Agurto, Raquel Norel, Brittany Quagan, Albert Powers, Michael L Birnbaum, Todd Constable, Guillermo Cecchi, John H Krystal
We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.
{"title":"Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions.","authors":"Daniel S Barron, Stephen Heisig, Carla Agurto, Raquel Norel, Brittany Quagan, Albert Powers, Michael L Birnbaum, Todd Constable, Guillermo Cecchi, John H Krystal","doi":"10.5334/cpsy.78","DOIUrl":"10.5334/cpsy.78","url":null,"abstract":"<p><p>We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43491911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-05-24DOI: 10.5334/cpsy.61
Carly A Lasagna, Timothy J Pleskac, Cynthia Z Burton, Melvin G McInnis, Stephan F Taylor, Ivy F Tso
Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups-18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD-), and 33 healthy comparisons (HC)-completed the BART. We modeled behavior using 4 competing hierarchical Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD- and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.
躁郁症(BD)与过度追求快感的冒险行为有关,这通常是其临床表现的特征。然而,人们对躁狂症患者冒险行为的机制还不甚了解。最近的数据表明,躁狂症患者先前的药物使用障碍(SUD)可能代表了某些特质水平的冒险行为脆弱性。本研究通过对气球模拟风险任务(BART)中的行为进行数学建模,研究了BD中冒险行为的机制和SUD的作用。共有三组人完成了气球模拟风险任务(BART),他们分别是:18 名既往有药物依赖的嗜睡症患者(BD+)、15 名既往无药物依赖的嗜睡症患者(BD-)和 33 名健康对比组(HC)。我们使用 4 个相互竞争的分层贝叶斯模型对行为进行建模,模型比较结果倾向于指数-权重均方差模型(EWMV),该模型包含并划分了风险承担的五个认知成分:先验信念、学习率、风险偏好、损失规避和行为一致性。两个 BD 组,无论是否有药物滥用史,其行为一致性均低于 HC 组。在 BART 风险承担过程中,BD+ 组表现出更悲观的先验信念(相对于 BD- 组和 HC 组)和更低的损失规避(相对于 HC 组)。BART 风险承担的传统测量方法(调整泵、总分、总分)未发现任何群体差异。这些研究结果表明,行为一致性降低是 BD 风险决策的一个重要特征,即使情绪症状和药物使用得到缓解,BD 的 SUD 史也可能预示着风险行为的额外特质脆弱性。这项研究还强调了在研究 BD 等复杂疾病时使用数学建模来理解行为的价值。
{"title":"Mathematical modeling of risk-taking in bipolar disorder: Evidence of reduced behavioral consistency, with altered loss aversion specific to those with history of substance use disorder.","authors":"Carly A Lasagna, Timothy J Pleskac, Cynthia Z Burton, Melvin G McInnis, Stephan F Taylor, Ivy F Tso","doi":"10.5334/cpsy.61","DOIUrl":"10.5334/cpsy.61","url":null,"abstract":"<p><p>Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups-18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD-), and 33 healthy comparisons (HC)-completed the BART. We modeled behavior using 4 competing hierarchical Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD- and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897236/pdf/nihms-1864043.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10662584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-08-26DOI: 10.5334/cpsy.89
Holly Sullivan-Toole, Nathaniel Haines, Kristina Dale, Thomas M Olino
Poor psychometrics, particularly low test-retest reliability, pose a major challenge for using behavioral tasks in individual differences research. Here, we demonstrate that full generative modeling of the Iowa Gambling Task (IGT) substantially improves test-retest reliability and may also enhance the IGT's validity for use in characterizing internalizing pathology, compared to the traditional analytic approach. IGT data was collected across two sessions, one month apart. Our full generative model incorporated (1) the Outcome Representation Learning (ORL) computational model at the person-level and (2) a group-level model that explicitly modeled test-retest reliability, along with other group-level effects. Compared to the traditional 'summary score' (proportion good decks selected), the ORL model provides a theoretically rich set of performance metrics (Reward Learning Rate , Punishment Learning Rate , Win Frequency Sensitivity , Perseveration Tendency , Memory Decay ), capturing distinct psychological processes. While test-retest reliability for the traditional summary score was only moderate (, BCa 95% CI [.04, .63]), test-retest reliabilities for ORL performance metrics produced by the full generative model were substantially improved, with test-retest correlations ranging between for the five ORL parameters. Further, while summary scores showed no substantial associations with internalizing symptoms, ORL parameters were significantly associated with internalizing symptoms. Specifically, Punishment Learning Rate was associated with higher self-reported depression and Perseveration Tendency was associated with lower self-reported anhedonia. Generative modeling offers promise for advancing individual differences research using the IGT, and behavioral tasks more generally, through enhancing task psychometrics.
{"title":"Enhancing the Psychometric Properties of the Iowa Gambling Task Using Full Generative Modeling.","authors":"Holly Sullivan-Toole, Nathaniel Haines, Kristina Dale, Thomas M Olino","doi":"10.5334/cpsy.89","DOIUrl":"10.5334/cpsy.89","url":null,"abstract":"<p><p>Poor psychometrics, particularly low test-retest reliability, pose a major challenge for using behavioral tasks in individual differences research. Here, we demonstrate that full generative modeling of the Iowa Gambling Task (IGT) substantially improves test-retest reliability and may also enhance the IGT's validity for use in characterizing internalizing pathology, compared to the traditional analytic approach. IGT data <math><mo>(</mo><mi>n</mi><mo>=</mo><mn>50</mn><mo>)</mo></math> was collected across two sessions, one month apart. Our full generative model incorporated (1) the Outcome Representation Learning (ORL) computational model at the person-level and (2) a group-level model that explicitly modeled test-retest reliability, along with other group-level effects. Compared to the traditional '<i>summary score</i>' (proportion good decks selected), the ORL model provides a theoretically rich set of performance metrics (<i>Reward Learning Rate</i> <math><mo>(</mo><mi>A</mi><mo>+</mo><mo>)</mo></math>, <i>Punishment Learning Rate</i> <math><mo>(</mo><mi>A</mi><mo>-</mo><mo>)</mo></math>, <i>Win Frequency Sensitivity</i> <math><mo>(</mo><mi>β</mi><mi>f</mi><mo>)</mo></math>, <i>Perseveration Tendency</i> <math><mo>(</mo><mi>β</mi><mi>p</mi><mo>)</mo></math>, <i>Memory Decay</i> <math><mo>(</mo><mi>K</mi><mo>)</mo></math>), capturing distinct psychological processes. While test-retest reliability for the traditional summary score was only moderate (<math><mi>r</mi><mo>=</mo><mspace></mspace><mo>.</mo><mn>37</mn></math>, BCa 95% CI [.04, .63]), test-retest reliabilities for ORL performance metrics produced by the full generative model were substantially improved, with test-retest correlations ranging between <math><mi>r</mi><mo>=</mo><mspace></mspace><mo>.</mo><mn>64</mn><mo>-</mo><mo>.</mo><mn>82</mn></math> for the five ORL parameters. Further, while summary scores showed no substantial associations with internalizing symptoms, ORL parameters were significantly associated with internalizing symptoms. Specifically, <i>Punishment Learning Rate</i> was associated with higher self-reported depression and <i>Perseveration Tendency</i> was associated with lower self-reported anhedonia. Generative modeling offers promise for advancing individual differences research using the IGT, and behavioral tasks more generally, through enhancing task psychometrics.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275579/pdf/nihms-1902411.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9839189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-02-03DOI: 10.5334/cpsy.67
Emma L Lawrance, Christopher R Gagne, Jill X O'Reilly, Janine Bijsterbosch, Sonia J Bishop
Theoretical accounts have linked anxiety to intolerance of ambiguity. However, this relationship has not been well operationalized empirically. Here, we used computational and neuro-imaging methods to characterize anxiety-related differences in aversive decision-making under ambiguity and associated patterns of cortical activity. Adult human participants chose between two urns on each trial. The ratio of tokens ('O's and 'X's) in each urn determined probability of electrical stimulation receipt. A number above each urn indicated the magnitude of stimulation that would be received if a shock was delivered. On ambiguous trials, one of the two urns had tokens occluded. By varying the number of tokens occluded, we manipulated the extent of missing information. At higher levels of missing information, there is greater second order uncertainty, i.e., more uncertainty as to the probability of pulling a given type of token from the urn. Adult human participants demonstrated avoidance of ambiguous options which increased with level of missing information. Extent of 'information-level dependent' ambiguity aversion was significantly positively correlated with trait anxiety. Activity in both the dorsal anterior cingulate cortex and inferior frontal sulcus during the decision-making period increased as a function of missing information. Greater engagement of these regions, on high missing information trials, was observed when participants went on to select the ambiguous option; this was especially apparent in high trait anxious individuals. These findings are consistent with individuals vulnerable to anxiety requiring greater activation of frontal regions supporting rational decision-making to overcome a predisposition to engage in ambiguity avoidance at high levels of missing information.
{"title":"The Computational and Neural Substrates of Ambiguity Avoidance in Anxiety.","authors":"Emma L Lawrance, Christopher R Gagne, Jill X O'Reilly, Janine Bijsterbosch, Sonia J Bishop","doi":"10.5334/cpsy.67","DOIUrl":"10.5334/cpsy.67","url":null,"abstract":"<p><p>Theoretical accounts have linked anxiety to intolerance of ambiguity. However, this relationship has not been well operationalized empirically. Here, we used computational and neuro-imaging methods to characterize anxiety-related differences in aversive decision-making under ambiguity and associated patterns of cortical activity. Adult human participants chose between two urns on each trial. The ratio of tokens ('O's and 'X's) in each urn determined probability of electrical stimulation receipt. A number above each urn indicated the magnitude of stimulation that would be received if a shock was delivered. On ambiguous trials, one of the two urns had tokens occluded. By varying the number of tokens occluded, we manipulated the extent of missing information. At higher levels of missing information, there is greater second order uncertainty, i.e., more uncertainty as to the probability of pulling a given type of token from the urn. Adult human participants demonstrated avoidance of ambiguous options which increased with level of missing information. Extent of 'information-level dependent' ambiguity aversion was significantly positively correlated with trait anxiety. Activity in both the dorsal anterior cingulate cortex and inferior frontal sulcus during the decision-making period increased as a function of missing information. Greater engagement of these regions, on high missing information trials, was observed when participants went on to select the ambiguous option; this was especially apparent in high trait anxious individuals. These findings are consistent with individuals vulnerable to anxiety requiring greater activation of frontal regions supporting rational decision-making to overcome a predisposition to engage in ambiguity avoidance at high levels of missing information.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40403396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-29eCollection Date: 2021-01-01DOI: 10.5334/cpsy.69
R Randeniya, I Vilares, J B Mattingley, M I Garrido
A general consensus persists that sensory-perceptual differences in autism, such as hypersensitivities to light or sound, result from an overreliance on new (rather than prior) sensory observations. However, conflicting Bayesian accounts of autism remain unresolved as to whether such alterations are caused by more precise sensory observations (precise likelihood model) or by forming a less precise model of the sensory context (hypo-priors model). We used a decision-under-uncertainty paradigm that manipulated uncertainty in both likelihoods and priors. Contrary to model predictions we found no differences in reliance on likelihood in autistic group (AS) compared to neurotypicals (NT) and found no differences in subjective prior variance between groups. However, we found reduced context adjustment in the AS group compared to NT. Further, the AS group showed heightened variability in their relative weighting of sensory information (vs. prior) on a trial-by-trial basis. When participants were aligned on a continuum of autistic traits, we found no associations with likelihood reliance or prior variance but found an increase in likelihood precision with autistic traits. These findings together provide empirical evidence for intact priors, precise likelihood, reduced context updating and heightened variability during sensory learning in autism.
{"title":"Reduced Context Updating but Intact Visual Priors in Autism.","authors":"R Randeniya, I Vilares, J B Mattingley, M I Garrido","doi":"10.5334/cpsy.69","DOIUrl":"10.5334/cpsy.69","url":null,"abstract":"<p><p>A general consensus persists that sensory-perceptual differences in autism, such as hypersensitivities to light or sound, result from an overreliance on new (rather than prior) sensory observations. However, conflicting Bayesian accounts of autism remain unresolved as to whether such alterations are caused by more precise sensory observations (precise likelihood model) or by forming a less precise model of the sensory context (hypo-priors model). We used a decision-under-uncertainty paradigm that manipulated uncertainty in both likelihoods and priors. Contrary to model predictions we found no differences in reliance on likelihood in autistic group (AS) compared to neurotypicals (NT) and found no differences in subjective prior variance between groups. However, we found reduced context adjustment in the AS group compared to NT. Further, the AS group showed heightened variability in their relative weighting of sensory information (vs. prior) on a trial-by-trial basis. When participants were aligned on a continuum of autistic traits, we found no associations with likelihood reliance or prior variance but found an increase in likelihood precision with autistic traits. These findings together provide empirical evidence for intact priors, precise likelihood, reduced context updating and heightened variability during sensory learning in autism.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-21eCollection Date: 2021-01-01DOI: 10.5334/cpsy.79
Tomislav D Zbozinek, Caroline J Charpentier, Song Qi, Dean Mobbs
Most of life's decisions involve risk and uncertainty regarding whether reward or loss will follow. Decision makers often face uncertainty not only about the likelihood of outcomes (what are the chances that I will get a raise if I ask my supervisor? What are the chances that my supervisor will be upset with me for asking?) but also the magnitude of outcomes (if I do get a raise, how large will it be? If my supervisor gets upset, how bad will the consequences be for me?). Only a few studies have investigated economic decision making with ambiguous likelihoods, and even fewer have investigated ambiguous outcome magnitudes. In the present report, we investigated the effects of ambiguous outcome magnitude, risk, and gains/losses in an economic decision-making task with low stakes (Study 1; $3.60-$5.70; N = 367) and high stakes (Study 2; $6-$48; N = 210) using a within-subjects design. We conducted computational modeling to determine individuals' preferences/aversions for ambiguous outcome magnitudes, risk, and gains/losses. We additionally investigated the association between trait anxiety and trait depression and decision-making parameters. Our results show that increasing stakes increased ambiguous gain aversion and unambiguous risk aversion but increased ambiguous sure loss preference; participants also became more averse to ambiguous sure gains relative to unambiguous risky gains. There were no significant effects of trait anxiety or trait depression on economic decision making. Our results suggest that as stakes increase, people tend to avoid uncertainty in the gain domain (especially ambiguous gains) but prefer ambiguous vs unambiguous sure losses.
{"title":"Economic Decisions with Ambiguous Outcome Magnitudes Vary with Low and High Stakes but Not Trait Anxiety or Depression.","authors":"Tomislav D Zbozinek, Caroline J Charpentier, Song Qi, Dean Mobbs","doi":"10.5334/cpsy.79","DOIUrl":"10.5334/cpsy.79","url":null,"abstract":"<p><p>Most of life's decisions involve risk and uncertainty regarding whether reward or loss will follow. Decision makers often face uncertainty not only about the likelihood of outcomes (what are the chances that I will get a raise if I ask my supervisor? What are the chances that my supervisor will be upset with me for asking?) but also the magnitude of outcomes (if I do get a raise, how large will it be? If my supervisor gets upset, how bad will the consequences be for me?). Only a few studies have investigated economic decision making with ambiguous likelihoods, and even fewer have investigated ambiguous outcome magnitudes. In the present report, we investigated the effects of ambiguous outcome magnitude, risk, and gains/losses in an economic decision-making task with low stakes (Study 1; $3.60-$5.70; N = 367) and high stakes (Study 2; $6-$48; N = 210) using a within-subjects design. We conducted computational modeling to determine individuals' preferences/aversions for ambiguous outcome magnitudes, risk, and gains/losses. We additionally investigated the association between trait anxiety and trait depression and decision-making parameters. Our results show that increasing stakes increased ambiguous gain aversion and unambiguous risk aversion but increased ambiguous sure loss preference; participants also became more averse to ambiguous sure gains relative to unambiguous risky gains. There were no significant effects of trait anxiety or trait depression on economic decision making. Our results suggest that as stakes increase, people tend to avoid uncertainty in the gain domain (especially ambiguous gains) but prefer ambiguous vs unambiguous sure losses.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-19DOI: 10.1101/2021.10.18.21265152
Ryan Smith, S. Taylor, J. Stewart, S. Guinjoan, M. Ironside, N. Kirlic, H. Ekhtiari, Evan J. White, Haixia Zheng, R. Kuplicki, M. Paulus
Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicate these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of these baseline differences. We also examine whether baseline modelling measures can predict symptoms at follow-up. Bayesian analyses indicate that: (a) group differences in learning rates were stable over time (posterior probability = 1); (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 < ICCs < .54); and (c) learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 < rs < .43, .002 < ps < .02). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.
{"title":"Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and their Potential Predictive Utility","authors":"Ryan Smith, S. Taylor, J. Stewart, S. Guinjoan, M. Ironside, N. Kirlic, H. Ekhtiari, Evan J. White, Haixia Zheng, R. Kuplicki, M. Paulus","doi":"10.1101/2021.10.18.21265152","DOIUrl":"https://doi.org/10.1101/2021.10.18.21265152","url":null,"abstract":"Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicate these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of these baseline differences. We also examine whether baseline modelling measures can predict symptoms at follow-up. Bayesian analyses indicate that: (a) group differences in learning rates were stable over time (posterior probability = 1); (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 < ICCs < .54); and (c) learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 < rs < .43, .002 < ps < .02). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45695457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andreas Hula, Michael Moutoussis, Geert-Jan Will, Danae Kokorikou, Andrea M Reiter, Gabriel Ziegler, E D Bullmore, Peter B Jones, Ian Goodyer, Peter Fonagy, P Read Montague, Raymond J Dolan
Investing in strangers in a socio-economic exchange is risky, as we may be uncertain whether they will reciprocate. Nevertheless, the potential rewards for cooperating can be great. Here, we used a cross sectional sample (n = 784) to study how the challenges of cooperation versus defection are negotiated across an important period of the lifespan: from adolescence to young adulthood (ages 14 to 25). We quantified social behaviour using a multi round investor-trustee task, phenotyping individuals using a validated model whose parameters characterise patterns of real exchange and constitute latent social characteristics. We found highly significant differences in investment behaviour according to age, sex, socio-economic status and IQ. Consistent with the literature, we showed an overall trend towards higher trust from adolescence to young adulthood but, in a novel finding, we characterized key cognitive mechanisms explaining this, especially regarding socio-economic risk aversion. Males showed lower risk-aversion, associated with greater investments. We also found that inequality aversion was higher in females and, in a novel relation, that socio-economic deprivation was associated with more risk averse play.
{"title":"Multi-Round Trust Game Quantifies Inter-Individual Differences in Social Exchange from Adolescence to Adulthood.","authors":"Andreas Hula, Michael Moutoussis, Geert-Jan Will, Danae Kokorikou, Andrea M Reiter, Gabriel Ziegler, E D Bullmore, Peter B Jones, Ian Goodyer, Peter Fonagy, P Read Montague, Raymond J Dolan","doi":"10.5334/cpsy.65","DOIUrl":"10.5334/cpsy.65","url":null,"abstract":"<p><p>Investing in strangers in a socio-economic exchange is risky, as we may be uncertain whether they will reciprocate. Nevertheless, the potential rewards for cooperating can be great. Here, we used a cross sectional sample (n = 784) to study how the challenges of cooperation versus defection are negotiated across an important period of the lifespan: from adolescence to young adulthood (ages 14 to 25). We quantified social behaviour using a multi round investor-trustee task, phenotyping individuals using a validated model whose parameters characterise patterns of real exchange and constitute latent social characteristics. We found highly significant differences in investment behaviour according to age, sex, socio-economic status and IQ. Consistent with the literature, we showed an overall trend towards higher trust from adolescence to young adulthood but, in a novel finding, we characterized key cognitive mechanisms explaining this, especially regarding socio-economic risk aversion. Males showed lower risk-aversion, associated with greater investments. We also found that inequality aversion was higher in females and, in a novel relation, that socio-economic deprivation was associated with more risk averse play.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}