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Computational psychiatry

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71 papers 25 to 100 followers
By Abraham Nunes Psychiatry resident interested in computational neuroscience, forensic psychiatry, and neuropsychiatry.
Jeffrey Annis, Thomas J Palmeri
Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable...
March 2018: Wiley Interdisciplinary Reviews. Cognitive Science
Níall Lally, Quentin J M Huys, Neir Eshel, Paul Faulkner, Peter Dayan, Jonathan P Roiser
Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes...
October 18, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Kimberly L Stachenfeld, Matthew M Botvinick, Samuel J Gershman
A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation...
November 2017: Nature Neuroscience
Mathias Pessiglione, Fabien Vinckier, Sébastien Bouret, Jean Daunizeau, Raphaël Le Bouc
Motivation deficits, such as apathy, are pervasive in both neurological and psychiatric diseases. Even when they are not the core symptom, they reduce quality of life, compromise functional outcome and increase the burden for caregivers. They are currently assessed with clinical scales that do not give any mechanistic insight susceptible to guide therapeutic intervention. Here, we present another approach that consists of phenotyping the behaviour of patients in motivation tests, using computational models...
November 29, 2017: Brain: a Journal of Neurology
Evan M Russek, Ida Momennejad, Matthew M Botvinick, Samuel J Gershman, Nathaniel D Daw
Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning...
September 2017: PLoS Computational Biology
Julie J Lee, Mehdi Keramati
Decision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values and transition contingencies can determine the relative influence of the two systems in reinforcement learning, but few studies have manipulated the latter...
September 2017: PLoS Computational Biology
Jaron T Colas, Wolfgang M Pauli, Tobias Larsen, J Michael Tyszka, John P O'Doherty
Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e...
October 2017: PLoS Computational Biology
Miriam Sebold, Stephan Nebe, Maria Garbusow, Matthias Guggenmos, Daniel J Schad, Anne Beck, Soeren Kuitunen-Paul, Christian Sommer, Robin Frank, Peter Neu, Ulrich S Zimmermann, Michael A Rapp, Michael N Smolka, Quentin J M Huys, Florian Schlagenhauf, Andreas Heinz
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients...
December 1, 2017: Biological Psychiatry
Anita Cservenka, Kelly E Courtney, Dara G Ghahremani, Kent E Hutchison, Lara A Ray
Aims: To advance translational studies of the role of reward prediction error (PE) in alcohol use disorder, the present study sought to develop and conduct an initial test of an alcohol-specific PE task paradigm using functional magnetic resonance imaging in humans. Methods: Alcohol dependent or social drinkers received small tastes of their preferred alcohol beverage or control beverage, with preceding visual cues indicating whether alcohol (or water) would be delivered...
September 1, 2017: Alcohol and Alcoholism: International Journal of the Medical Council on Alcoholism
Albert R Powers, Megan Kelley, Philip R Corlett
The problem of whether and how information is integrated across hierarchical brain networks embodies a fundamental tension in contemporary cognitive neuroscience, and by extension, cognitive neuropsychiatry. Indeed, the penetrability of perceptual processes in a 'top-down' manner by higher-level cognition-a natural extension of hierarchical models of perception-may contradict a strictly modular view of mental organization. Furthermore, some in the cognitive science community have challenged cognitive penetration as an unlikely, if not impossible, process...
September 2016: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Michael T Treadway, Roee Admon, Amanda R Arulpragasam, Malavika Mehta, Samuel Douglas, Gordana Vitaliano, David P Olson, Jessica A Cooper, Diego A Pizzagalli
BACKGROUND: Stress is widely known to alter behavioral responses to rewards and punishments. It is believed that stress may precipitate these changes through modulation of corticostriatal circuitry involved in reinforcement learning and motivation, although the intervening mechanisms remain unclear. One candidate is inflammation, which can rapidly increase following stress and can disrupt dopamine-dependent reward pathways. METHODS: Here, in a sample of 88 healthy female participants, we first assessed the effect of an acute laboratory stress paradigm on levels of plasma interleukin-6 (IL-6), a cytokine known to be both responsive to stress and elevated in depression...
October 15, 2017: Biological Psychiatry
Cristina Martinelli, Francesco Rigoli, Bruno Averbeck, Sukhwinder S Shergill
Influential models of schizophrenia suggest that patients experience incoming stimuli as excessively novel and motivating, with important consequences for hallucinatory experience and delusional belief. However, whether schizophrenia patients exhibit excessive novelty value and whether this interferes with adaptive behaviour has not yet been formally tested. Here, we employed a three-armed bandit task to investigate this hypothesis. Schizophrenia patients and healthy controls were first familiarised with a group of images and then asked to repeatedly choose between familiar and unfamiliar images associated with different monetary reward probabilities...
May 8, 2017: Schizophrenia Research
Marcus Rothkirch, Jonas Tonn, Stephan Köhler, Philipp Sterzer
According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants...
April 1, 2017: Brain: a Journal of Neurology
David H Zald, Michael T Treadway
Abnormal reward processing is a prominent transdiagnostic feature of psychopathology. The present review provides a framework for considering the different aspects of reward processing and their assessment, and highlights recent insights from the field of neuroeconomics that may aid in understanding these processes. Although altered reward processing in psychopathology has often been treated as a general hypo- or hyperresponsivity to reward, increasing data indicate that a comprehensive understanding of reward dysfunction requires characterization within more specific reward-processing domains, including subjective valuation, discounting, hedonics, reward anticipation and facilitation, and reinforcement learning...
May 8, 2017: Annual Review of Clinical Psychology
Elizabeth V Goldfarb, Grant S Shields, Nathaniel D Daw, George M Slavich, Elizabeth A Phelps
Exposure to stress throughout life can cumulatively influence later health, even among young adults. The negative effects of high cumulative stress exposure are well-known, and a shift from episodic to stimulus-response memory has been proposed to underlie forms of psychopathology that are related to high lifetime stress. At the other extreme, effects of very low stress exposure are mixed, with some studies reporting that low stress leads to better outcomes, while others demonstrate that low stress is associated with diminished resilience and negative outcomes...
April 2017: Learning & Memory
Marisa DeGuzman, Megan E Shott, Tony T Yang, Justin Riederer, Guido K W Frank
OBJECTIVE: Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. METHOD: Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15...
June 1, 2017: American Journal of Psychiatry
Jonathan D Cohen, Nathaniel Daw, Barbara Engelhardt, Uri Hasson, Kai Li, Yael Niv, Kenneth A Norman, Jonathan Pillow, Peter J Ramadge, Nicholas B Turk-Browne, Theodore L Willke
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis...
February 23, 2017: Nature Neuroscience
Alec Solway, Terry Lohrenz, P Read Montague
The laboratory study of how humans and other animals trade-off value and time has a long and storied history, and is the subject of a vast literature. However, despite a long history of study, there is no agreed upon mechanistic explanation of how intertemporal choice preferences arise. Several theorists have recently proposed model-based reinforcement learning as a candidate framework. This framework describes a suite of algorithms by which a model of the environment, in the form of a state transition function and reward function, can be converted on-line into a decision...
February 22, 2017: Scientific Reports
Christina Ioannou, Marwa El Zein, Valentin Wyart, Isabelle Scheid, Frédérique Amsellem, Richard Delorme, Coralie Chevallier, Julie Grèzes
Although, the quest to understand emotional processing in individuals with Autism Spectrum Disorders (ASD) has led to an impressive number of studies, the picture that emerges from this research remains inconsistent. Some studies find that Typically Developing (TD) individuals outperform those with ASD in emotion recognition tasks, others find no such difference. In this paper, we move beyond focusing on potential group differences in behaviour to answer what we believe is a more pressing question: do individuals with ASD use the same mechanisms to process emotional cues? To this end, we rely on model-based analyses of participants' accuracy during an emotion categorisation task in which displays of anger and fear are paired with direct vs...
February 20, 2017: Scientific Reports
Abby Tabor, Michael A Thacker, G Lorimer Moseley, Konrad P Körding
Perception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline a Bayesian inference model, incorporating the key components of cue combination, causal inference, and temporal integration, which highlights the statistical problems in everyday perception. It is from this platform that we are able to review the pain literature, providing evidence from experimental, acute, and persistent phenomena to demonstrate the advantages of adopting a statistical account in pain...
January 2017: PLoS Computational Biology
2017-01-14 12:19:07
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