scholarly journals Opening Burton's Clock: Psychiatric insights from computational cognitive models

2018 ◽  
Author(s):  
Daniel Bennett ◽  
Yael Niv
Keyword(s):  
Psychiatric Illness ◽  
Computational Models ◽  
Model Comparison ◽  
Model Fitting ◽  
Computational Modelling ◽  
Quantitative Model ◽  
Cognitive Models ◽  
Qualitative Comparison ◽  
Computational Psychiatry ◽  
Reinforcement Learning Models

Computational psychiatry is a nascent field that seeks to use computational tools from neuroscience and cognitive science to understand psychiatric illness. In this chapter, we make the case for computational cognitive models as a bridge between the cognitive and affective deficits experienced by those with a psychiatric illness and the neurocomputational dysfunctions that underlie these deficits. We first review the history of computational modelling in psychiatry and conclude that a key moment of maturation in this field occurred with the transition from qualitative comparison between computational models and human behaviour to formal quantitative model fitting and model comparison. We then summarise current research at one of the most exciting frontiers of computational psychiatry: reinforcement learning models of mood disorders. We review state-of-the-art applications of such models to major depression and bipolar disorder, and outline important open questions to be addressed by the coming wave of research in computational psychiatry.

scholarly journals Toward a formal theory of proactivity

2021 ◽  
Author(s):  
F. Lieder ◽  
G. Iwama
Keyword(s):  
Computational Models ◽  
Model Comparison ◽  
Formal Theory ◽  
Quantitative Model ◽  
Proactive Behavior ◽  
Continuous Performance Task ◽  
Proactive Control ◽  
Reactive Control ◽  
Future Situation ◽  
Future Work

AbstractBeyond merely reacting to their environment and impulses, people have the remarkable capacity to proactively set and pursue their own goals. The extent to which they leverage this capacity varies widely across people and situations. The goal of this article is to propose and evaluate a model of proactivity and reactivity. We proceed in three steps. First, we model proactivity in a widely used cognitive control task known as the AX Continuous Performance Task (AX-CPT). Our theory formalizes an important aspect of proactivity as meta-control over proactive and reactive control. Second, we perform a quantitative model comparison to identify the number and nature of meta-control decisions that are involved in the regulation of proactive behavior. Our findings suggest that individual differences in proactivity are governed by two independent meta-control decisions, namely deciding whether to set an intention for what to do in a future situation and deciding whether to recall one’s intentions when the situation occurs. Third, we test the assumptions and qualitative predictions of the winning model against data from numerous experiments varying the incentives, cognitive load, and statistical structure of the task. Our results suggest that proactivity can be understood in terms of computational models of meta-control. Future work will extend our models from proactive control in the AX-CPT to proactive goal creation and goal pursuit in the real world.

scholarly journals Notions of similarity for computational biology models

2016 ◽  
Author(s):  
Ron Henkel ◽  
Robert Hoehndorf ◽  
Tim Kacprowski ◽  
Christian Knüpfer ◽  
Wolfram Liebermeister ◽  
...  
Keyword(s):  
Computational Models ◽  
Model Comparison ◽  
Model Building ◽  
Similarity Measures ◽  
Quantitative Model ◽  
Complex Model ◽  
General Notion ◽  
Model Combination ◽  
Potential Applications ◽  
Model Similarity

AbstractComputational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of “similarity” may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users’ intuition about model similarity, and to support complex model searches in databases.

scholarly journals The Predictive Power of Spatial Relational Reasoning Models: A New Evaluation Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Ragni ◽  
Daniel Brand ◽  
Nicolas Riesterer
Keyword(s):  
Cognitive Modeling ◽  
Computational Models ◽  
Predictive Power ◽  
Model Comparison ◽  
Cognitive Models ◽  
Relational Reasoning ◽  
Response Patterns ◽  
Research Focus ◽  
Evaluation Approach ◽  
The Individual

In the last few decades, cognitive theories for explaining human spatial relational reasoning have increased. Few of these theories have been implemented as computational models, however, even fewer have been compared computationally to each other. A computational model comparison requires, among other things, a still missing quantitative benchmark of core spatial relational reasoning problems. By presenting a new evaluation approach, this paper addresses: (1) developing a benchmark including raw data of participants, (2) reimplementation, adaptation, and extension of existing cognitive models to predict individual responses, and (3) a thorough evaluation of the cognitive models on the benchmark data. The paper shifts the research focus of cognitive modeling from reproducing aggregated response patterns toward assessing the predictive power of models for the individual reasoner. It demonstrate that not all psychological effects can discern theories. We discuss implications for modeling spatial relational reasoning.

scholarly journals The Importance of Group Specification in Computational Modelling of Behaviour

2020 ◽  
Author(s):  
Vincent Valton ◽  
Toby Wise ◽  
Oliver Joe Robinson
Keyword(s):  
Model Fitting ◽  
Computational Modelling ◽  
Group Differences ◽  
Group Level ◽  
Parameter Recovery ◽  
Fitting Procedure ◽  
Computational Psychiatry ◽  
Diagnostic Labels ◽  
Data Generating Process ◽  
Parameter Values

Introduction: Hierarchical model fitting has become commonplace in cognitive neuroscience. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. In computational psychiatry, we frequently encounter situations with multiple distinct groups of subjects, which makes this specification non-trivial. Specifically, we must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they will undoubtedly affect the resulting inference (parameter recovery) and conflate or mask true group-level differences. Methods: We used simulations to test these assumptions on synthetic multi-group behavioural data from commonly used multi-armed bandit tasks. We first demonstrate the influence of hierarchical vs non hierarchical approaches on parameter recovery. We then examine recovery of group differences in parameter values under different modelling approaches: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluate the robustness of these approaches to variations in data quality and in prior specification.Results: We confirm, firstly, that hierarchical approaches lead to better parameter recovery. Secondly, we argue that fitting groups separately (Model 2) provides the most robust inference across all conditions and perturbations. Thirdly, we confirm these assumptions have less of an impact with higher numbers of trials and subjects. Finally, we show that the advice to fit groups separately can fall down when extreme assumptions are made about group variance.Conclusions: Our results suggest that when dealing with data from multiple clinical groups, researchers should use a hierarchical approach to parameter fitting, but account for hypothesised group differences in the model fitting procedure. In other words, under most normal circumstances, researchers should fit patient and control groups separately.

scholarly journals Troubleshooting Bayesian cognitive models: A tutorial with matstanlib

2021 ◽  
Author(s):  
Beth Baribault ◽  
Anne Collins
Keyword(s):  
Cognitive Processes ◽  
Bayesian Model ◽  
Cognitive Modeling ◽  
Computational Models ◽  
Model Fitting ◽  
Psychological Research ◽  
Cognitive Models ◽  
Model Output ◽  
Mcmc Sampling ◽  
Bayesian Cognitive Modeling

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software such as Stan and PyMC3 that efficiently automates the Markov chain Monte Carlo (MCMC) sampling used for Bayesian model fitting. Unfortunately, Bayesian cognitive models can struggle to pass the computational checks required of all Bayesian models. If any failures are left undetected, inferences about cognition based on model output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and MCMC sampling, we outline the diagnostic metrics, procedures, and plots necessary to identify problems in model output with an emphasis on how these requirements have recently been improved. Throughout, we explain how the most commonly encountered problems may be remedied with specific, practical solutions. We also introduce matstanlib, our MATLAB modeling support library, and demonstrate how it facilitates troubleshooting of an example hierarchical Bayesian model of reinforcement learning implemented in Stan. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models.All code is freely available from github.com/baribault/matstanlib.

scholarly journals Limitations of Proposed Signatures of Bayesian Confidence

2018 ◽  
Vol 30 (12) ◽  
pp. 3327-3354 ◽  
Author(s):  
William T. Adler ◽  
Wei Ji Ma
Keyword(s):  
Bayesian Model ◽  
Posterior Probability ◽  
Model Comparison ◽  
Sufficient Conditions ◽  
Internal Noise ◽  
Quantitative Model ◽  
Alternative Form ◽  
Stimulus Magnitude ◽  
Animal Data ◽  
Average Confidence

The Bayesian model of confidence posits that confidence reflects the observer's posterior probability that the decision is correct. Hangya, Sanders, and Kepecs ( 2016 ) have proposed that researchers can test the Bayesian model by deriving qualitative signatures of Bayesian confidence (i.e., patterns that one would expect to see if an observer were Bayesian) and looking for those signatures in human or animal data. We examine two proposed signatures, showing that their derivations contain hidden assumptions that limit their applicability and that they are neither necessary nor sufficient conditions for Bayesian confidence. One signature is an average confidence of 0.75 on trials with neutral evidence. This signature holds only when class-conditioned stimulus distributions do not overlap and when internal noise is very low. Another signature is that as stimulus magnitude increases, confidence increases on correct trials but decreases on incorrect trials. This divergence signature holds only when stimulus distributions do not overlap or when noise is high. Navajas et al. ( 2017 ) have proposed an alternative form of this signature; we find no indication that this alternative form is expected under Bayesian confidence. Our observations give us pause about the usefulness of the qualitative signatures of Bayesian confidence. To determine the nature of the computations underlying confidence reports, there may be no shortcut to quantitative model comparison.

scholarly journals Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2306
Author(s):  
Antonis I. Sakellarios ◽  
Panagiotis Siogkas ◽  
Vassiliki Kigka ◽  
Panagiota Tsompou ◽  
Dimitrios Pleouras ◽  
...  
Keyword(s):  
Error Propagation ◽  
Computational Models ◽  
Computational Modelling ◽  
Predictive Modelling ◽  
Prognostic Models ◽  
Current Analysis ◽  
Non Invasive ◽  
Plaque Growth ◽  
Artery Disease ◽  
Diagnostic Index

Assessments of coronary artery disease can be achieved using non-invasive computed tomography coronary angiography (CTCA). CTCA can be further used for the 3D reconstruction of the coronary arteries and the development of computational models. However, image acquisition and arterial reconstruction introduce an error which can be propagated, affecting the computational results and the accuracy of diagnostic and prognostic models. In this work, we investigate the effect of an imaging error, propagated to a diagnostic index calculated using computational modelling of blood flow and then to prognostic models based on plaque growth modelling or binary logistic predictive modelling. The analysis was performed utilizing data from 20 patients collected at two time points with interscan period of six years. The collected data includes clinical and risk factors, biological and biohumoral data, and CTCA imaging. The results demonstrated that the error propagated and may have significantly affected some of the final outcomes. The calculated propagated error seemed to be minor for shear stress, but was major for some variables of the plaque growth model. In parallel, in the current analysis SmartFFR was not considerably affected, with the limitation of only one case located into the gray zone.

scholarly journals Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lea Fritschi ◽  
Johanna Hedlund Lindmar ◽  
Florian Scheidl ◽  
Kerstin Lenk
Keyword(s):  
Computational Models ◽  
Glutamate Release ◽  
Signal Transmission ◽  
Computational Modelling ◽  
Atp Release ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Neuronal Network Activity ◽  
Cell Densities ◽  
Synapse Model

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

scholarly journals The Exponential-Weight Mean-Variance Model: A novel computational model for the Balloon Analogue Risk Task

2019 ◽  
Author(s):  
Harhim Park ◽  
Jaeyeong Yang ◽  
Jasmin Vassileva ◽  
Woo-Young Ahn
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Model Comparison ◽  
Exponential Weight ◽  
Multiple Model ◽  
Variance Model ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Post Hoc ◽  
Mean Variance

The Balloon Analogue Risk Task (BART) is a popular task used to measure risk-taking behavior. To identify cognitive processes associated with choice behavior on the BART, a few computational models have been proposed. However, the extant models are either too simplistic or fail to show good parameter recovery performance. Here, we propose a novel computational model, the exponential-weight mean-variance (EWMV) model, which addresses the limitations of existing models. By using multiple model comparison methods, including post hoc model fits criterion and parameter recovery, we showed that the EWMV model outperforms the existing models. In addition, we applied the EWMV model to BART data from healthy controls and substance-using populations (patients with past opiate and stimulant dependence). The results suggest that (1) the EWMV model addresses the limitations of existing models and (2) heroin-dependent individuals show reduced risk preference than other groups in the BART.

scholarly journals What is the role of computational models in Cognitive Science? A quantitative and qualitative analysis of the history of the TRACE model of speech segmentation

2019 ◽  
Author(s):  
Manisha Chawla ◽  
Richard Shillcock
Keyword(s):  
Qualitative Analysis ◽  
Cognitive Science ◽  
Computational Models ◽  
Computational Modelling ◽  
Speech Segmentation ◽  
Cognitive Modelling ◽  
Trace Model ◽  
History Of ◽  
Central Paradigm

Implemented computational models are a central paradigm of Cognitive Science. How do cognitive scientists really use such models? We take the example of one of the most successful and influential cognitive models, TRACE (McClelland & Elman, 1986), and we map its impact on the field in terms of published, electronically available documents that cite the original TRACE paper over a period of 25 years since its publication. We draw conclusions about the general status of computational cognitive modelling and make critical suggestions regarding the nature of abstraction in computational modelling.

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