The role of effective connectivity between the task-positive and task-negative network for evidence gathering [Evidence gathering and connectivity]

&NA; Reports linking a ‘jumping‐to‐conclusions’ bias to delusions have led to growing interest in the neurobiological correlates of probabilistic reasoning. Several brain areas have been implicated in probabilistic reasoning; however, findings are difficult to integrate into a coherent account. The present study aimed to provide additional evidence by investigating, for the first time, effective connectivity among brain areas involved in different stages of evidence gathering. We investigated evidence gathering in 25 healthy individuals using fMRI and a new paradigm (Box Task) designed such as to minimize the effects of cognitive effort and reward processing. Decisions to collect more evidence (‘draws’) were contrasted to decisions to reach a final choice (‘conclusions’) with respect to BOLD activity. Psychophysiological interaction analysis was used to investigate effective connectivity. Conclusion events were associated with extensive brain activations in widely distributed brain areas associated with the task‐positive network. In contrast, draw events were characterized by higher activation in areas assumed to be part of the task‐negative network. Effective connectivity between the two networks decreased during draws and increased during conclusion events. Our findings indicate that probabilistic reasoning may depend on the balance between the task‐positive and task‐negative network, and that shifts in connectivity between the two may be crucial for evidence gathering. Thus, abnormal connectivity between the two systems may significantly contribute to the jumping‐to‐conclusions bias. HighlightsWe examined fMRI activity and connectivity patterns during evidence gathering.Activations in the task‐negative network (TNN) characterized evidence collection.Activations in the task‐positive network (TPN) marked the time‐point of conclusion.Effective connectivity between TNN and TPN increased during conclusion events.Probabilistic reasoning may depend on the balance between TPN and TNN.

[1]  J. Ford,et al.  Default mode network activity and connectivity in psychopathology. , 2012, Annual review of clinical psychology.

[2]  Peter Kirsch,et al.  Activation of Midbrain and Ventral Striatal Regions Implicates Salience Processing during a Modified Beads Task , 2013, PloS one.

[3]  J. Gallinat,et al.  Dopamine effects on evidence gathering and integration. , 2015, Journal of psychiatry & neuroscience : JPN.

[4]  T. Woodward,et al.  Different sides of the same coin? Intercorrelations of cognitive biases in schizophrenia , 2010, Cognitive neuropsychiatry.

[5]  Vincent D Costa,et al.  Frontal-parietal and limbic-striatal activity underlies information sampling in the best choice problem. , 2015, Cerebral cortex.

[6]  Bruno B Averbeck,et al.  Jumping to conclusions in schizophrenia , 2015, Neuropsychiatric disease and treatment.

[7]  M. Raichle,et al.  On the existence of a generalized non-specific task-dependent network , 2015, Front. Hum. Neurosci..

[8]  Bruno B Averbeck,et al.  Parietal Cortex and Insula Relate to Evidence Seeking Relevant to Reward-Related Decisions , 2011, The Journal of Neuroscience.

[9]  D. Sheehan,et al.  The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. , 1998, The Journal of clinical psychiatry.

[10]  Daniel L. Schacter,et al.  Solving future problems: Default network and executive activity associated with goal-directed mental simulations , 2011, NeuroImage.

[11]  Karl J. Friston,et al.  Psychophysiological and Modulatory Interactions in Neuroimaging , 1997, NeuroImage.

[12]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Katie M. Lavigne,et al.  Functional connectivity in a frontoparietal network involving the dorsal anterior cingulate cortex underlies decisions to accept a hypothesis , 2013, Neuropsychologia.

[14]  R. Nathan Spreng,et al.  The Fallacy of a “Task-Negative” Network , 2012, Front. Psychology.

[15]  Daniel Durstewitz,et al.  Area-Specific Information Processing in Prefrontal Cortex during a Probabilistic Inference Task: A Multivariate fMRI BOLD Time Series Analysis , 2015, PloS one.

[16]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[17]  G. McCarthy,et al.  Decisions under Uncertainty: Probabilistic Context Influences Activation of Prefrontal and Parietal Cortices , 2005, The Journal of Neuroscience.

[18]  J. Hirsch,et al.  A Neural Representation of Categorization Uncertainty in the Human Brain , 2006, Neuron.

[19]  D. Naber,et al.  Dopaminergic modulation of probabilistic reasoning and overconfidence in errors: a double-blind study. , 2014, Schizophrenia bulletin.

[20]  Giancarlo Valente,et al.  The Default Mode Network and the Working Memory Network Are Not Anti-Correlated during All Phases of a Working Memory Task , 2015, PloS one.

[21]  D. Yves von Cramon,et al.  Predicting events of varying probability: uncertainty investigated by fMRI , 2003, NeuroImage.

[22]  Max Coltheart,et al.  Jumping to Conclusions About the Beads Task? A Meta-analysis of Delusional Ideation and Data-Gathering. , 2015, Schizophrenia bulletin.

[23]  K. Vogeley,et al.  Investigation of decision-making under uncertainty in healthy subjects: A multi-centric fMRI study , 2014, Behavioural Brain Research.

[24]  C. Mulert,et al.  fMRI correlates of jumping-to-conclusions in patients with delusions: Connectivity patterns and effects of metacognitive training , 2018, NeuroImage: Clinical.

[25]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[26]  Daniel L. Schacter,et al.  Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition , 2010, NeuroImage.

[27]  M. Pyka,et al.  Attenuated prefrontal activation during decision-making under uncertainty in schizophrenia: A multi-center fMRI study , 2014, Schizophrenia Research.

[28]  Michael P. Milham,et al.  Distinct neural mechanisms of risk and ambiguity: A meta-analysis of decision-making , 2006, NeuroImage.

[29]  S. Moritz,et al.  A New Paradigm to Measure Probabilistic Reasoning and a Possible Answer to the Question Why Psychosis-Prone Individuals Jump to Conclusions , 2017, Journal of abnormal psychology.

[30]  F. Christian How the Brain Integrates Costs and Benefits During Decision Making , 2010 .

[31]  A. Meyer-Lindenberg,et al.  Reduced activation in ventral striatum and ventral tegmental area during probabilistic decision-making in schizophrenia , 2014, Schizophrenia Research.

[32]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[33]  R. Dudley,et al.  Psychosis, Delusions and the “Jumping to Conclusions” Reasoning Bias: A Systematic Review and Meta-analysis , 2015, Schizophrenia bulletin.

[34]  T. Robbins,et al.  Reflection Impulsivity in Current and Former Substance Users , 2006, Biological Psychiatry.

[35]  P. Garety,et al.  Probabilistic Judgements in Deluded and Non-Deluded Subjects , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[36]  John M. Pearson,et al.  Neurons in Posterior Cingulate Cortex Signal Exploratory Decisions in a Dynamic Multioption Choice Task , 2009, Current Biology.

[37]  C. Andreou,et al.  Beads task vs. box task: The specificity of the jumping to conclusions bias. , 2017, Journal of behavior therapy and experimental psychiatry.

[38]  Benjamin F McLean,et al.  Association of the Jumping to Conclusions and Evidence Integration Biases With Delusions in Psychosis: A Detailed Meta-analysis , 2016, Schizophrenia bulletin.

[39]  P. Fletcher,et al.  Effects of Methamphetamine Administration on Information Gathering during Probabilistic Reasoning in Healthy Humans , 2014, PloS one.

[40]  S. Kapur,et al.  ‘Jumping to conclusions’ and delusions in psychosis: Relationship and response to treatment , 2008, Schizophrenia Research.

[41]  O. Sporns Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[42]  Stephan F Taylor,et al.  Updating Beliefs for a Decision: Neural Correlates of Uncertainty and Underconfidence , 2010, The Journal of Neuroscience.