Sensitivity to expected negative outcomes during approach-avoidance conflict in a trans-diagnostic patient sample: a computational (active inference) modeling approach

Background: Sacrificing rewarding aspects of one’s life due to potential aversive outcomes is an important characteristic of multiple psychiatric disorders. Such decisions occur during approach-avoidance conflict (AAC), which has become the topic of a growing number of behavioral and neuroimaging studies. Here we describe a novel computational modeling approach to studying AAC.Methods: A previously-validated AAC task was completed by 479 participants including healthy controls (HCs), and individuals with depression, anxiety, and/or substance use disorders (SUDs), as part of the Tulsa 1000 study. An active inference model was utilized to identify parameters corresponding to the subjective aversiveness of affective stimuli (VNegative), the subjective value of points that could be won (VPoints), and decision uncertainty (β). We used correlational analyses to examine relationships to self-reported experiences during the task, analyses of variance to examine diagnostic group differences (depression/anxiety, substance use, HCs), and exploratory machine learning analyses to examine the contribution of dimensional clinical and neuropsychological measures.Results: Model parameters correlated with self-reported experience and reaction times during the task in expected directions. Relatve to HCs, both clinical groups showed higher VNegative values, and the SUD group exhibited less decision uncertainty (lower β values). Machine learning analyses highlighted several clinical domains (i.e., alcohol use, personality, working memory) potentially contributing to task parameters.Conclusions: Our results suggest that avoidance behavior in individuals with depression, anxiety, and SUDs may be driven by increased sensitivity to predicted negative outcomes and that insufficient decision uncertainty (overconfidence) may also further contribute to avoidance in substance use disorder.

Download Full-text

Sensitivity to Expected Negative Outcomes during Approach-Avoidance Conflict in a Trans-Diagnostic Patient Sample: A Computational (Active Inference) Modeling Approach

Biological Psychiatry ◽

10.1016/j.biopsych.2020.02.862 ◽

2020 ◽

Vol 87 (9) ◽

pp. S336

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer Stewart ◽

James Touthang ◽

Sahib Khalsa ◽

...

Keyword(s):

Active Inference ◽

Negative Outcomes ◽

Patient Sample ◽

Modeling Approach ◽

Approach Avoidance

Download Full-text

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

Journal of Psychiatry and Neuroscience ◽

10.1503/jpn.200032 ◽

2020 ◽

Vol 46 (1) ◽

pp. E74-E87

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer L. Stewart ◽

James Touthang ◽

Rayus Kuplicki ◽

...

Keyword(s):

Substance Use ◽

Anxiety Disorders ◽

Psychiatric Disorders ◽

Substance Use Disorders ◽

Computational Modelling ◽

Healthy Controls ◽

Full Sample ◽

Decision Uncertainty ◽

Emotional Conflict ◽

Approach Avoidance

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). Methods: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. Results: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). Limitations: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. Conclusion: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Download Full-text

Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modeling approach

10.31234/osf.io/t2dhn ◽

2020 ◽

Author(s):

Ryan Smith ◽

Namik Kirlic ◽

Jennifer L. Stewart ◽

James Touthang ◽

Rayus Kuplicki ◽

...

Keyword(s):

Computational Modeling ◽

Psychiatric Disorders ◽

Clinical Sample ◽

Negative Outcomes ◽

Full Sample ◽

Decision Uncertainty ◽

Emotional Conflict ◽

Model Free ◽

Validation Parameters ◽

Approach Avoidance

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g. sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modeling to examine two factors often not distinguished within model-free analyses of AAC: decision uncertainty (DU) and sensitivity to negative outcomes vs. reward (emotional conflict; EC).Methods: A previously-validated AAC task was completed by 477 participants, including healthy controls (HCs; N=59), individuals with substance use disorders (SUDs; N=159) and individuals with depression and/or anxiety (DEP/ANX; N=260) disorders without SUDs. Using an active inference model, we estimated individual-level values for a model parameter (β) reflecting DU as well as another reflecting EC. Analyses were also repeated in a subsample propensity matched on age and general intelligence.Results: The model showed high accuracy (73%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. EC further correlated with self-reported anxiety during the task (r=0.32, p<0.001), while DU correlated with self-reported difficulty making decisions (r=0.45, p<0.001). Compared to HCs, both DEP/ANX and SUDs showed higher DU in the propensity matched sample (t=2.16, p = .03; and t=2.88, p = .005, respectively), with analogous results in the full sample; SUDs also showed lower EC in the full sample (t=3.17, p=0.002). Limitations: This study is limited by clinical sample heterogeneity and an inability to examine learning.Conclusions: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviors in psychiatric disorders.

Download Full-text

Aff_Faces

10.31234/osf.io/kdsu4 ◽

2018 ◽

Author(s):

Luiz G Gawryszewski ◽

Mikael Cavallet

Keyword(s):

Compatibility Effect ◽

Negative Stimulus ◽

Positive Stimulus ◽

Spatial Compatibility ◽

Affective Valence ◽

Task Set ◽

Affective Stimuli ◽

Fearful Faces ◽

Approach Avoidance ◽

Avoidance Reactions

Conde et al (2011) reported a modulation of the spatial compatibility effect by the affective valence of soccer team figures. For Favorite team, it was faster to respond by pressing the key located on the stimulus side than the opposite key (ipsi- and contralateral keys, respectively). For Rival team, this pattern was reversed. These findings were interpreted as being due to approach and avoidance reactions which facilitate both the ipsilateral response to a positive stimulus and the contralateral response to a negative one and vice-versa. This hypothesis was challenged by arguing that there is no spatial compatibility effect when a mixed-rule task was used and that approach/avoidance reactions are not elicited when a keyboard was employed to execute the responses. Alternatively, it was proposed that Conde et al. (2011) results were due to task-set effects. Here, emotional faces (Happy, Angry and Fearful) faces were used to test the generality of effects elicited by affective stimuli and to disentangle task-set and approach/avoidance reactions hypotheses. We found that there is no task-set effect when the Happiness-Anger pair was used. Moreover, for the Happiness/Fear pair, there was an interaction between valence and spatial compatibility within a block of trials. These results suggest that: (i) the interaction between valence and spatial compatibility in the Affective SC task modulates the spatial compatibility effect; (ii) this modulation elicits a task-set effect that varies according to the pair of affective stimuli and (iv) the task-set effect may be due to an automatic orientation of the visual attention to the positive stimulus which facilitates the ipsilateral response conjoined with an inhibition of the ipsilateral response to the aversive stimulus, simulating a reversed compatibility effect to the negative stimulus.

Download Full-text

Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽

10.3390/en14051265 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1265 ◽

Cited By ~ 1

Author(s):

Johanna Geis-Schroer ◽

Sebastian Hubschneider ◽

Lukas Held ◽

Frederik Gielnik ◽

Michael Armbruster ◽

...

Keyword(s):

Low Voltage ◽

Measurement Data ◽

Analytical Calculation ◽

Laboratory Measurement ◽

Model Parameters ◽

Calculation Methods ◽

Modeling Approach ◽

Measurement Results ◽

Simulation Results ◽

Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

Download Full-text

Trigonometric Inference Providing Learning in Deep Neural Networks

Applied Sciences ◽

10.3390/app11156704 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6704

Author(s):

Jingyong Cai ◽

Masashi Takemoto ◽

Yuming Qiu ◽

Hironori Nakajo

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Neural Networks ◽

Activation Function ◽

Trigonometric Approximation ◽

Model Parameters ◽

Training Algorithms ◽

Activation Functions ◽

Classical Training ◽

Sum Formula

Despite being heavily used in the training of deep neural networks (DNNs), multipliers are resource-intensive and insufficient in many different scenarios. Previous discoveries have revealed the superiority when activation functions, such as the sigmoid, are calculated by shift-and-add operations, although they fail to remove multiplications in training altogether. In this paper, we propose an innovative approach that can convert all multiplications in the forward and backward inferences of DNNs into shift-and-add operations. Because the model parameters and backpropagated errors of a large DNN model are typically clustered around zero, these values can be approximated by their sine values. Multiplications between the weights and error signals are transferred to multiplications of their sine values, which are replaceable with simpler operations with the help of the product to sum formula. In addition, a rectified sine activation function is utilized for further converting layer inputs into sine values. In this way, the original multiplication-intensive operations can be computed through simple add-and-shift operations. This trigonometric approximation method provides an efficient training and inference alternative for devices with insufficient hardware multipliers. Experimental results demonstrate that this method is able to obtain a performance close to that of classical training algorithms. The approach we propose sheds new light on future hardware customization research for machine learning.

Download Full-text

Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

Scientific Reports ◽

10.1038/s41598-021-81307-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ryan Smith ◽

◽

Justin S. Feinstein ◽

Rayus Kuplicki ◽

Katherine L. Forthman ◽

...

Keyword(s):

Machine Learning ◽

Substance Use ◽

Individual Differences ◽

Substance Use Disorders ◽

Breath Hold ◽

Healthy Individuals ◽

Performance Improvements ◽

Heartbeat Perception ◽

Minimal Improvement ◽

Healthy Participants

AbstractThis study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

Download Full-text

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.1c00856 ◽

2021 ◽

Vol 61 (9) ◽

pp. 4266-4279 ◽

Cited By ~ 1

Author(s):

Kuo Hao Lee ◽

Andrew D. Fant ◽

Jiqing Guo ◽

Andy Guan ◽

Joslyn Jung ◽

...

Keyword(s):

Machine Learning ◽

Molecular Modeling ◽

Modeling Approach

Download Full-text

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽

10.3390/info12030109 ◽

2021 ◽

Vol 12 (3) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Iman Rahimi ◽

Amir H. Gandomi ◽

Panagiotis G. Asteris ◽

Fang Chen

Keyword(s):

Machine Learning ◽

Logistic Function ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Model Parameters ◽

The Novel ◽

Chinese City ◽

Limited Memory ◽

Increasing Trend ◽

Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

Download Full-text