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Qiao L, Zhang L, Chen A. Control dilemma: Evidence of the stability-flexibility trade-off. Int J Psychophysiol 2023; 191:29-41. [PMID: 37499985 DOI: 10.1016/j.ijpsycho.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023]
Abstract
Cognitive control can be applied flexibly when task goals or environments change (i.e., cognitive flexibility), or stably to pursue a goal in the face of distraction (i.e., cognitive stability). Whether these seemingly contradictory characteristics have an inverse relationship has been controversial, as some studies have suggested a trade-off mechanism between cognitive flexibility and cognitive stability, while others have not found such reciprocal associations. This study investigated the possible antagonistic correlation between cognitive flexibility and stability using a novel version of the flexibility-stability paradigm and the classic cued task switching paradigm. In Experiment 1, we showed that cognitive flexibility was inversely correlated with cognitive stability, as increased distractor proportions were associated with decreased cognitive flexibility and greater cognitive stability. Moreover, cognitive flexibility and stability were regulated by a single control system instead of two independent control mechanisms, as the model selection results indicated that the reciprocally regulated model with one integration parameter outperformed all other models, and the model parameter was inversely linked to cognitive flexibility and stability. We found similar results using the classic cued task switching paradigm in Experiment 2. Therefore, a trade-off between cognitive flexibility and stability was observed from the paradigms used in this study.
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Affiliation(s)
- Lei Qiao
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Lijie Zhang
- School of Education Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
| | - Antao Chen
- Department of Psychology, Shanghai University of Sport, Shanghai, China
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2
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Qiao L, Zhang L, Chen A. Brain connectivity modulation by Bayesian surprise in relation to control demand drives cognitive flexibility via control engagement. Cereb Cortex 2023; 33:1985-2000. [PMID: 35553644 DOI: 10.1093/cercor/bhac187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
Human control is characterized by its flexibility and adaptability in response to the conditional probability in the environment. Previous studies have revealed that efficient conflict control could be attained by predicting and adapting to the changing control demand. However, it is unclear whether cognitive flexibility could also be gained by predicting and adapting to the changing control demand. The present study aimed to explore this issue by combining the model-based analyses of behavioral and neuroimaging data with a probabilistic cued task switching paradigm. We demonstrated that the Bayesian surprise (i.e. unsigned precision-weighted prediction error [PE]) negatively modulated the connections among stimulus processing brain regions and control regions/networks. The effect of Bayesian surprise modulation on these connections guided control engagement as reflected by the control PE effect on behavior, which in turn facilitated cognitive flexibility. These results bridge a gap in the literature by illustrating the neural and behavioral effect of control demand prediction (or PE) on cognitive flexibility and offer novel insights into the source of switch cost and the mechanism of cognitive flexibility.
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Affiliation(s)
- Lei Qiao
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Lijie Zhang
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Antao Chen
- Department Psychology, Shanghai Univ Sport, Shanghai 200438, Peoples R China
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3
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Baklushev ME, Nazarova MA, Novikov PA, Nikulin VV. [Methods for assessing aberrant and adaptive salience]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:30-35. [PMID: 37655407 DOI: 10.17116/jnevro202312308130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The term «salience» is most often used to describe «aberrant salience», which means assigning false significance to insignificant facts and details, that is inherent to patients with schizophrenia. Most often it is used in combination with «aberrant salience», which is understood as the assignment of false significance to insignificant facts and details. The term «adaptive salience» is less commonly used and means the «correct» assignment of the significance to important biological information. It is believed that in schizophrenia there is a decrease of adaptive salience in combination with an increase of aberrant salience. The concepts of aberrant and adaptive salience are a kind of link between the dopamine imbalance underlying the pathogenesis of schizophrenia and the diverse clinic of the disease. This article provides a review of the literature on methods for assessing, including quantitatively assessment, salience in schizophrenia. The comparison of these methods and their possible clinical and scientific application are provided.
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4
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Katthagen T, Fromm S, Wieland L, Schlagenhauf F. Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches. Front Psychiatry 2022; 13:814111. [PMID: 35492702 PMCID: PMC9039658 DOI: 10.3389/fpsyt.2022.814111] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 02/18/2022] [Indexed: 11/20/2022] Open
Abstract
To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.
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Affiliation(s)
- Teresa Katthagen
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sophie Fromm
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Lara Wieland
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
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5
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Millman ZB, Schiffman J, Gold JM, Akouri-Shan L, Demro C, Fitzgerald J, Rakhshan Rouhakhtar PJ, Klaunig M, Rowland LM, Waltz JA. Linking Salience Signaling With Early Adversity and Affective Distress in Individuals at Clinical High Risk for Psychosis: Results From an Event-Related fMRI Study. SCHIZOPHRENIA BULLETIN OPEN 2022; 3:sgac039. [PMID: 35799887 PMCID: PMC9250803 DOI: 10.1093/schizbullopen/sgac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract
Evidence suggests dysregulation of the salience network in individuals with psychosis, but few studies have examined the intersection of stress exposure and affective distress with prediction error (PE) signals among youth at clinical high-risk (CHR). Here, 26 individuals at CHR and 19 healthy volunteers (HVs) completed a monetary incentive delay task in conjunction with fMRI. We compared these groups on the amplitudes of neural responses to surprising outcomes—PEs without respect to their valence—across the whole brain and in two regions of interest, the anterior insula and amygdala. We then examined relations of these signals to the severity of depression, anxiety, and trauma histories in the CHR group. Relative to HV, youth at CHR presented with aberrant PE-evoked activation of the temporoparietal junction and weaker deactivation of the precentral gyrus, posterior insula, and associative striatum. No between-group differences were observed in the amygdala or anterior insula. Among youth at CHR, greater trauma histories were correlated with stronger PE-evoked amygdala activation. No associations were found between affective symptoms and the neural responses to PE. Our results suggest that unvalenced PE signals may provide unique information about the neurobiology of CHR syndromes and that early adversity exposure may contribute to neurobiological heterogeneity in this group. Longitudinal studies of young people with a range of risk syndromes are needed to further disentangle the contributions of distinct aspects of salience signaling to the development of psychopathology.
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Affiliation(s)
- Zachary B Millman
- Psychotic Disorders Division, McLean Hospital , 115 Mill Street, Belmont, MA 02478 , USA
- Department of Psychiatry, Harvard Medical School , 25 Shattuck Street, Boston, MA 02114 , USA
| | - Jason Schiffman
- Department of Psychological Science, University of California, Irvine , 4201 Social and Behavioral Sciences Gateway, Irvine, CA 92697-7085 , USA
- Department of Psychology, University of Maryland , Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 , USA
| | - James M Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine , 55 Wade Avenue, Catonsville, MD 21228 , USA
| | - LeeAnn Akouri-Shan
- Department of Psychology, University of Maryland , Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 , USA
| | - Caroline Demro
- Department of Psychology, University of Minnesota, 75 East River Parkway , Minneapolis, MN 55455 , USA
| | - John Fitzgerald
- Department of Psychology, University of Maryland , Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 , USA
| | - Pamela J Rakhshan Rouhakhtar
- Department of Psychology, University of Maryland , Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 , USA
| | - Mallory Klaunig
- Department of Psychology, University of Maryland , Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 , USA
| | - Laura M Rowland
- Maryland Psychiatric Research Center, University of Maryland School of Medicine , 55 Wade Avenue, Catonsville, MD 21228 , USA
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine , 55 Wade Avenue, Catonsville, MD 21228 , USA
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6
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Kesby JP, Murray GK, Knolle F. Neural Circuitry of Salience and Reward Processing in Psychosis. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 3:33-46. [PMID: 36712572 PMCID: PMC9874126 DOI: 10.1016/j.bpsgos.2021.12.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 02/01/2023] Open
Abstract
The processing of salient and rewarding stimuli is integral to engaging our attention, stimulating anticipation for future events, and driving goal-directed behaviors. Widespread impairments in these processes are observed in psychosis, which may be associated with worse functional outcomes or mechanistically linked to the development of symptoms. Here, we summarize the current knowledge of behavioral and functional neuroimaging in salience, prediction error, and reward. Although each is a specific process, they are situated in multiple feedback and feedforward systems integral to decision making and cognition more generally. We argue that the origin of salience and reward processing dysfunctions may be centered in the subcortex during the earliest stages of psychosis, with cortical abnormalities being initially more spared but becoming more prominent in established psychotic illness/schizophrenia. The neural circuits underpinning salience and reward processing may provide targets for delaying or preventing progressive behavioral and neurobiological decline.
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Affiliation(s)
- James P. Kesby
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia,QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia,Address correspondence to James Kesby, Ph.D.
| | - Graham K. Murray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia,Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Franziska Knolle
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom,Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany,Franziska Knolle, Ph.D.
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7
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Becske M, Marosi C, Molnár H, Fodor Z, Tombor L, Csukly G. Distractor filtering and its electrophysiological correlates in schizophrenia. Clin Neurophysiol 2021; 133:71-82. [PMID: 34814018 DOI: 10.1016/j.clinph.2021.10.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/07/2021] [Accepted: 10/09/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Patients with schizophrenia are characterized by compromised working memory (WM) performance and increased distractibility. Theta synchronization (especially over the frontal midline areas) is related to cognitive control and executive processes during WM encoding and retention. Alpha event-related desynchronization (ERD) is associated with information processing and attention. METHODS Participants (35 patients and 39 matched controls) performed a modified Sternberg WM task, containing salient and non-salient distractor items in the retention period. A high-density 128 channel EEG was recorded during the task. Theta (4-7 Hz) and fast alpha (10-13 Hz) event-related spectral perturbation (ERSP) were analyzed during the retention and encoding period. RESULTS Patients with schizophrenia showed worse WM performance and increased attentional distractibility in terms of lower hit rates and increased distractor-related commission errors compared to healthy controls. Theta synchronization was modulated by condition (learning vs. distractor) in both groups but it was modulated by salience only in controls. Furthermore, salience of distractors modulated less the fast alpha ERD in patients. CONCLUSIONS Our results suggest that patients with schizophrenia process salient and non-salient distracting information less efficiently and show weaker cognitive control compared to controls. SIGNIFICANCE These differences may partly account for diminished WM performance and increased distractibility in schizophrenia.
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Affiliation(s)
- Melinda Becske
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Csilla Marosi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Hajnalka Molnár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - László Tombor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary.
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8
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Piray P, Daw ND. A model for learning based on the joint estimation of stochasticity and volatility. Nat Commun 2021; 12:6587. [PMID: 34782597 PMCID: PMC8592992 DOI: 10.1038/s41467-021-26731-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/08/2021] [Indexed: 02/08/2023] Open
Abstract
Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.
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Affiliation(s)
- Payam Piray
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Nathaniel D Daw
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
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9
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Neural Correlates of Aberrant Salience and Source Monitoring in Schizophrenia and At-Risk Mental States-A Systematic Review of fMRI Studies. J Clin Med 2021; 10:jcm10184126. [PMID: 34575237 PMCID: PMC8468329 DOI: 10.3390/jcm10184126] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/22/2021] [Accepted: 09/07/2021] [Indexed: 01/03/2023] Open
Abstract
Cognitive biases are an important factor contributing to the development and symptom severity of psychosis. Despite the fact that various cognitive biases are contributing to psychosis, they are rarely investigated together. In the current systematic review, we aimed at investigating specific and shared functional neural correlates of two important cognitive biases: aberrant salience and source monitoring. We conducted a systematic search of fMRI studies of said cognitive biases. Eight studies on aberrant salience and eleven studies on source monitoring were included in the review. We critically discussed behavioural and neuroimaging findings concerning cognitive biases. Various brain regions are associated with aberrant salience and source monitoring in individuals with schizophrenia and the risk of psychosis. The ventral striatum and insula contribute to aberrant salience. The medial prefrontal cortex, superior and middle temporal gyrus contribute to source monitoring. The anterior cingulate cortex and hippocampus contribute to both cognitive biases, constituting a neural overlap. Our review indicates that aberrant salience and source monitoring may share neural mechanisms, suggesting their joint role in producing disrupted external attributions of perceptual and cognitive experiences, thus elucidating their role in positive symptoms of psychosis. Account bridging mechanisms of these two biases is discussed. Further studies are warranted.
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10
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Haarsma J, Knolle F, Griffin JD, Taverne H, Mada M, Goodyer IM, The Nspn Consortium, Fletcher PC, Murray GK. Influence of prior beliefs on perception in early psychosis: Effects of illness stage and hierarchical level of belief. JOURNAL OF ABNORMAL PSYCHOLOGY 2021; 129:581-598. [PMID: 32757602 PMCID: PMC7409392 DOI: 10.1037/abn0000494] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Alterations in the balance between prior expectations and sensory evidence may account for faulty perceptions and inferences leading to psychosis. However, uncertainties remain about the nature of altered prior expectations and the degree to which they vary with the emergence of psychosis. We explored how expectations arising at two different levels—cognitive and perceptual—influenced processing of sensory information and whether relative influences of higher- and lower-level priors differed across people with prodromal symptoms and those with psychotic illness. In two complementary auditory perception experiments, 91 participants (30 with first-episode psychosis, 29 at clinical risk for psychosis, and 32 controls) were required to decipher a phoneme within ambiguous auditory input. Expectations were generated in two ways: an accompanying visual input of lip movements observed during auditory presentation or through written presentation of a phoneme provided prior to auditory presentation. We determined how these different types of information shaped auditory perceptual experience, how this was altered across the prodromal and established phases of psychosis, and how this relates to cingulate glutamate levels assessed by magnetic resonance spectroscopy. The psychosis group relied more on high-level cognitive priors compared to both healthy controls and those at clinical risk for psychosis and relied more on low-level perceptual priors than the clinical risk group. The risk group was marginally less reliant on low-level perceptual priors than controls. The results are consistent with previous theory that influences of prior expectations in perceptions in psychosis differ according to level of prior and illness phase. What we perceive and believe in any given moment will allow us to form expectations about what we will experience in the next. In psychosis, it is believed that the influence of these so-called perceptual and cognitive “prior” expectations on perception are altered, thereby giving rise to the symptoms seen in psychosis. However, research thus far has found mixed evidence, some suggesting an increase in the influence of priors and some finding a decrease. Here we test the hypothesis that perceptual and cognitive priors are differentially affected in individuals at risk for psychosis and individuals with a first episode of psychosis, thereby partially explaining the mixed findings in the literature. We indeed found evidence in favor of this hypothesis, finding weaker perceptual priors in individuals at risk but stronger cognitive priors in individuals with first-episode psychosis.
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Affiliation(s)
| | | | | | | | - Marius Mada
- Department of Psychiatry, University of Cambridge
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11
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Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis. Mol Psychiatry 2021; 26:5320-5333. [PMID: 32576965 PMCID: PMC8589669 DOI: 10.1038/s41380-020-0803-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 04/24/2020] [Accepted: 05/28/2020] [Indexed: 11/08/2022]
Abstract
Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of prediction errors plays a key role in learning and decision-making, is controlled by dopamine and contributes to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation with a placebo, a dopamine receptor agonist bromocriptine or a dopamine receptor antagonist sulpiride (dopamine study n = 59) and in patients with early psychosis (psychosis study n = 74: 20 participants with first-episode psychosis, 30 healthy controls and 24 participants with at-risk mental state attenuated psychotic symptoms). Behavioural computational modelling indicated that precision weighting of prediction errors benefits learning in health and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to their precision in superior frontal cortex (replicated across studies, combined n = 133), which was perturbed by dopaminergic modulation, impaired in psychosis and associated with task performance and schizotypy (schizotypy correlation in 86 healthy volunteers). In contrast to our previous work, we did not observe significant precision-weighting of signed prediction errors, which signal valence, in the midbrain and ventral striatum in the healthy controls (or patients) in the psychosis study. We conclude that healthy people, but not patients with first-episode psychosis, take into account the precision of the environment when updating beliefs. Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.
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12
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Howes OD, Hird EJ, Adams RA, Corlett PR, McGuire P. Aberrant Salience, Information Processing, and Dopaminergic Signaling in People at Clinical High Risk for Psychosis. Biol Psychiatry 2020; 88:304-314. [PMID: 32430200 DOI: 10.1016/j.biopsych.2020.03.012] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 01/24/2023]
Abstract
The aberrant salience hypothesis proposes that striatal dopamine dysregulation causes misattribution of salience to irrelevant stimuli leading to psychosis. Recently, new lines of preclinical evidence on information coding by subcortical dopamine coupled with computational models of the brain's ability to predict and make inferences about the world (predictive processing) provide a new perspective on this hypothesis. We review these and summarize the evidence for dopamine dysfunction, reward processing, and salience abnormalities in people at clinical high risk of psychosis (CHR) relative to findings in patients with psychosis. This review identifies consistent evidence for dysregulated subcortical dopamine function in people at CHR, but also indicates a number of areas where neurobiological processes are different in CHR subjects relative to patients with psychosis, particularly in reward processing. We then consider how predictive processing models may explain psychotic symptoms in terms of alterations in prediction error and precision signaling using Bayesian approaches. We also review the potential role of environmental risk factors, particularly early adverse life experiences, in influencing the prior expectations that individuals have about their world in terms of computational models of the progression from being at CHR to frank psychosis. We identify a number of key outstanding questions, including the relative roles of prediction error or precision signaling in the development of symptoms and the mechanism underlying dopamine dysfunction. Finally, we discuss how the integration of computational psychiatry with biological investigation may inform the treatment for people at CHR of psychosis.
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Affiliation(s)
- Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, United Kingdom; Medical Research Council London Institute of Medical Sciences, Hammersmith Hospital, London, United Kingdom; Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom.
| | - Emily J Hird
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Rick A Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | - Philip R Corlett
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; National Institute of Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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13
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A simple model for learning in volatile environments. PLoS Comput Biol 2020; 16:e1007963. [PMID: 32609755 PMCID: PMC7329063 DOI: 10.1371/journal.pcbi.1007963] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 05/18/2020] [Indexed: 11/19/2022] Open
Abstract
Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorithm to volatile environments. The proposed model is algorithmically simple and encompasses the Kalman filter as a special case. Specifically, in addition to the error-correcting rule of Kalman filter for learning observations, the VKF learns volatility according to a second error-correcting rule. These dual updates echo and contextualize classical psychological models of learning, in particular hybrid accounts of Pearce-Hall and Rescorla-Wagner. At the computational level, compared with existing models, the VKF gives up some flexibility in the generative model to enable a more faithful approximation to exact inference. When fit to empirical data, the VKF is better behaved than alternatives and better captures human choice data in two independent datasets of probabilistic learning tasks. The proposed model provides a coherent account of learning in stable or volatile environments and has implications for decision neuroscience research.
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14
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Miasnikova A, Perevoznyuk G, Martynova O, Baklushev M. Cross-frequency phase coupling of brain oscillations and relevance attribution as saliency detection in abstract reasoning. Neurosci Res 2020; 166:26-33. [PMID: 32479775 DOI: 10.1016/j.neures.2020.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 05/02/2020] [Accepted: 05/25/2020] [Indexed: 11/30/2022]
Abstract
reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals' thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.
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Affiliation(s)
- Aleksandra Miasnikova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, 5A Butlerova St., 117485 Moscow, Russia.
| | - Gleb Perevoznyuk
- MSU, Faculty of Fundamental Medicine, 31-5 Lomonosovsky Prospekt, 117192 Moscow, Russia
| | - Olga Martynova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, 5A Butlerova St., 117485 Moscow, Russia; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Russian Federation, 20 Myasnitskaya, 101000 Moscow, Russia
| | - Mikhail Baklushev
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, 5A Butlerova St., 117485, Moscow, Russia
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Kaminski J, Katthagen T, Schlagenhauf F. [Computational psychiatry : Data-driven vs. mechanistic approaches]. DER NERVENARZT 2019; 90:1117-1124. [PMID: 31538209 DOI: 10.1007/s00115-019-00796-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The emerging research field of so-called computational psychiatry attempts to contribute to an understanding of complex psychiatric phenomena by applying computational methods and to promote the translation of neuroscientific research results into clinical practice. This article presents this field of research using selected examples based on the distinction between data-driven and theory-driven approaches. Exemplary for a data-driven approach are studies to predict clinical outcome, for example, in persons with a high-risk state for psychosis or on the response to pharmacological treatment for depression. Theory-driven approaches attempt to describe the mechanisms of altered information processing as the cause of psychiatric symptoms at the behavioral and neuronal level. In computational models possible mechanisms can be described that may have produced the measured behavioral or neuronal data. For example, in schizophrenia patients the clinical phenomenon of aberrant salience has been described as learning irrelevant information or cognitive deficits have been linked to connectivity changes in frontoparietal networks. Computational psychiatry can make important contributions to the prediction of individual clinical courses as well as to a mechanistic understanding of psychiatric symptoms. For this a further development of reliable and valid methods across different disciplines is indispensable.
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Affiliation(s)
- Jakob Kaminski
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland.,Berlin Institute of Health, Berlin, Deutschland
| | - Teresa Katthagen
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland
| | - Florian Schlagenhauf
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland. .,Bernstein Center for Computational Neuroscience, Berlin, Deutschland.
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Stefanics G, Stephan KE, Heinzle J. Feature-specific prediction errors for visual mismatch. Neuroimage 2019; 196:142-151. [PMID: 30978499 DOI: 10.1016/j.neuroimage.2019.04.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/30/2019] [Accepted: 04/04/2019] [Indexed: 01/08/2023] Open
Abstract
Predictive coding (PC) theory posits that our brain employs a predictive model of the environment to infer the causes of its sensory inputs. A fundamental but untested prediction of this theory is that the same stimulus should elicit distinct precision weighted prediction errors (pwPEs) when different (feature-specific) predictions are violated, even in the absence of attention. Here, we tested this hypothesis using functional magnetic resonance imaging (fMRI) and a multi-feature roving visual mismatch paradigm where rare changes in either color (red, green), or emotional expression (happy, fearful) of faces elicited pwPE responses in human participants. Using a computational model of learning and inference, we simulated pwPE and prediction trajectories of a Bayes-optimal observer and used these to analyze changes in blood oxygen level dependent (BOLD) responses to changes in color and emotional expression of faces while participants engaged in a distractor task. Controlling for visual attention by eye-tracking, we found pwPE responses to unexpected color changes in the fusiform gyrus. Conversely, unexpected changes of facial emotions elicited pwPE responses in cortico-thalamo-cerebellar structures associated with emotion and theory of mind processing. Predictions pertaining to emotions activated fusiform, occipital and temporal areas. Our results are consistent with a general role of PC across perception, from low-level to complex and socially relevant object features, and suggest that monitoring of the social environment occurs continuously and automatically, even in the absence of attention.
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Affiliation(s)
- Gabor Stefanics
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032, Zurich, Switzerland; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Blümlisalpstrasse 10, 8006, Zurich, Switzerland.
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032, Zurich, Switzerland; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Blümlisalpstrasse 10, 8006, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032, Zurich, Switzerland
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Dopaminergic basis for signaling belief updates, but not surprise, and the link to paranoia. Proc Natl Acad Sci U S A 2018; 115:E10167-E10176. [PMID: 30297411 DOI: 10.1073/pnas.1809298115] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Distinguishing between meaningful and meaningless sensory information is fundamental to forming accurate representations of the world. Dopamine is thought to play a central role in processing the meaningful information content of observations, which motivates an agent to update their beliefs about the environment. However, direct evidence for dopamine's role in human belief updating is lacking. We addressed this question in healthy volunteers who performed a model-based fMRI task designed to separate the neural processing of meaningful and meaningless sensory information. We modeled participant behavior using a normative Bayesian observer model and used the magnitude of the model-derived belief update following an observation to quantify its meaningful information content. We also acquired PET imaging measures of dopamine function in the same subjects. We show that the magnitude of belief updates about task structure (meaningful information), but not pure sensory surprise (meaningless information), are encoded in midbrain and ventral striatum activity. Using PET we show that the neural encoding of meaningful information is negatively related to dopamine-2/3 receptor availability in the midbrain and dexamphetamine-induced dopamine release capacity in the striatum. Trial-by-trial analysis of task performance indicated that subclinical paranoid ideation is negatively related to behavioral sensitivity to observations carrying meaningful information about the task structure. The findings provide direct evidence implicating dopamine in model-based belief updating in humans and have implications for understating the pathophysiology of psychotic disorders where dopamine function is disrupted.
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