scholarly journals Dream to explore: 5-HT2a as adaptive temperature parameter for sophisticated affective inference

2021 ◽  
Author(s):  
Adam Safron ◽  
Zahra Sheikhbahaee
Keyword(s):  
Machine Learning ◽  
Prior Work ◽  
Active Inference ◽  
Policy Selection ◽  
Temperature Parameter ◽  
Uncertainty Parameter ◽  
Absorbing States

Relative to other neuromodulators, serotonin (5-HT) has received far less attention in machine learning and active inference. We will review prior work interpreting 5-HT1a signaling as an uncertainty parameter with opponency to dopamine. We will then discuss how 5-HT2a receptors may promote more exploratory policy selection by enhancing imaginative planning (as sophisticated affective inference). Finally, we will briefly comment on how qualitatively different effects may be observed across low and high levels of 5-HT2a signaling, where the latter may help agents to change self-adversarial policies and break free of maladaptive absorbing states in POMDPs.

scholarly journals The Diversity–Innovation Paradox in Science

2020 ◽  
Vol 117 (17) ◽  
pp. 9284-9291 ◽  
Author(s):  
Bas Hofstra ◽  
Vivek V. Kulkarni ◽  
Sebastian Munoz-Najar Galvez ◽  
Bryan He ◽  
Dan Jurafsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Racial Minorities ◽  
Academic Careers ◽  
Prior Work ◽  
Underrepresented Groups ◽  
Scientific Careers ◽  
Faculty Positions ◽  
Majority Groups ◽  
Scientific Innovations

Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia.

scholarly journals ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data

2020 ◽  
Vol 34 (01) ◽  
pp. 865-872
Author(s):  
Soham Pal ◽  
Yash Gupta ◽  
Aditya Shukla ◽  
Aditya Kanade ◽  
Shirish Shevade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Domain Knowledge ◽  
Third Party ◽  
Prior Work ◽  
Model Extraction ◽  
Public Data ◽  
Adversarial Examples ◽  
Application Programming ◽  
Public Datasets

Machine learning models are increasingly being deployed in practice. Machine Learning as a Service (MLaaS) providers expose such models to queries by third-party developers through application programming interfaces (APIs). Prior work has developed model extraction attacks, in which an attacker extracts an approximation of an MLaaS model by making black-box queries to it. We design ActiveThief – a model extraction framework for deep neural networks that makes use of active learning techniques and unannotated public datasets to perform model extraction. It does not expect strong domain knowledge or access to annotated data on the part of the attacker. We demonstrate that (1) it is possible to use ActiveThief to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA.

scholarly journals APPROXIMATING SMALLEST ENCLOSING BALLS WITH APPLICATIONS TO MACHINE LEARNING

2009 ◽  
Vol 19 (05) ◽  
pp. 389-414 ◽  
Author(s):  
FRANK NIELSEN ◽  
RICHARD NOCK
Keyword(s):  
Machine Learning ◽  
Hybrid Algorithms ◽  
Decision Problems ◽  
Dual Method ◽  
Euclidean Ball ◽  
Prior Work ◽  
Euclidean Spaces ◽  
Information Theoretic ◽  
Primal Dual ◽  
Covering Set

In this paper, we first survey prior work for computing exactly or approximately the smallest enclosing balls of point or ball sets in Euclidean spaces. We classify previous work into three categories: (1) purely combinatorial, (2) purely numerical, and (3) recent mixed hybrid algorithms based on coresets. We then describe two novel tailored algorithms for computing arbitrary close approximations of the smallest enclosing Euclidean ball. These deterministic heuristics are based on solving relaxed decision problems using a primal-dual method. The primal-dual method is interpreted geometrically as solving for a minimum covering set, or dually as seeking for a minimum piercing set. Finally, we present some applications in machine learning of the exact and approximate smallest enclosing ball procedure, and discuss about its extension to non-Euclidean information-theoretic spaces.

scholarly journals Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lars Sandved-Smith ◽  
Casper Hesp ◽  
Jérémie Mattout ◽  
Karl Friston ◽  
Antoine Lutz ◽  
...  
Keyword(s):  
Attentional Control ◽  
Formal Model ◽  
Hierarchical Level ◽  
Active Inference ◽  
Mental Action ◽  
Perceptual Object ◽  
Cognitive States ◽  
Policy Selection ◽  
Current Content ◽  
And Control

Abstract Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data.

scholarly journals Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Stevie Chancellor ◽  
Steven A Sumner ◽  
Corinne David-Ferdon ◽  
Tahirah Ahmad ◽  
Munmun De Choudhury
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Social Media ◽  
Protective Factors ◽  
Suicide Risk ◽  
Risk And Protective Factors ◽  
Prior Work ◽  
Annotation Scheme ◽  
Social Media Data ◽  
Media Data

BACKGROUND Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. OBJECTIVE This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. METHODS We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch—a Reddit community focused on suicide crisis. RESULTS We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. CONCLUSIONS Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks.

scholarly journals Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study

10.2196/24471 ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. e24471
Author(s):  
Stevie Chancellor ◽  
Steven A Sumner ◽  
Corinne David-Ferdon ◽  
Tahirah Ahmad ◽  
Munmun De Choudhury
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Social Media ◽  
Protective Factors ◽  
Suicide Risk ◽  
Risk And Protective Factors ◽  
Prior Work ◽  
Annotation Scheme ◽  
Social Media Data ◽  
Media Data

Background Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. Objective This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. Methods We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch—a Reddit community focused on suicide crisis. Results We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. Conclusions Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks.

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

2019 ◽  
Author(s):  
Ryan Smith ◽  
Namik Kirlic ◽  
Jennifer L. Stewart ◽  
James Touthang ◽  
Rayus Kuplicki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Reaction Times ◽  
Model Parameters ◽  
Active Inference ◽  
Negative Outcomes ◽  
Decision Uncertainty ◽  
Modeling Approach ◽  
Affective Stimuli ◽  
Approach Avoidance

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.

scholarly journals Active inference, stressors, and psychological trauma: A neuroethological model of (mal)adaptive explore-exploit dynamics in ecological context

2019 ◽  
Author(s):  
Adam Linson ◽  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Psychological Trauma ◽  
Active Inference ◽  
Neuronal Responses ◽  
Post Traumatic Stress ◽  
Belief Updating ◽  
Formal Approach ◽  
Trade Off ◽  
Clinical Literature ◽  
Policy Selection ◽  
Post Traumatic

AbstractThis paper offers a formal account of emotional inference and stress-related behaviour, using the notion of active inference. We formulate responses to stressful scenarios in terms of Bayesian belief-updating and subsequent policy selection; namely, planning as (active) inference. Using a minimal model of how creatures or subjects account for their sensations (and subsequent action), we deconstruct the sequences of belief updating and behaviour that underwrite stress-related responses – and simulate the aberrant responses of the sort seen in post-traumatic stress disorder (PTSD). Crucially, the model used for belief-updating generates predictions in multiple (exteroceptive, proprioceptive and interoceptive) modalities, to provide an integrated account of evidence accumulation and multimodal integration that has consequences for both motor and autonomic responses. The ensuing phenomenology speaks to many constructs in the ecological and clinical literature on stress, which we unpack with reference to simulated inference processes and accompanying neuronal responses. A key insight afforded by this formal approach rests on the trade-off between the epistemic affordance of certain cues (that resolve uncertainty about states of affairs in the environment) and the consequences of epistemic foraging (that may be in conflict with the instrumental or pragmatic value of ‘fleeing’ or ‘freezing’). Starting from first principles, we show how this trade-off is nuanced by prior (subpersonal) beliefs about the outcomes of behaviour – beliefs that, when held with unduly high precision, can lead to (Bayes optimal) responses that closely resemble PTSD.

Investigating Group-Specific Models of Hospital Workers’ Well-Being: Implications for Algorithmic Bias

2020 ◽  
Vol 14 (04) ◽  
pp. 477-499
Author(s):  
Vinesh Ravuri ◽  
Projna Paromita ◽  
Karel Mundnich ◽  
Amrutha Nadarajan ◽  
Brandon M. Booth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ambulatory Monitoring ◽  
Real Life ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Prior Work ◽  
Hospital Workers ◽  
Job Responsibilities ◽  
Occupational Role ◽  
Algorithmic Bias

Hospital workers often experience burnout due to the demanding job responsibilities and long work hours. Data yielding from ambulatory monitoring combined with machine learning algorithms can afford us a better understanding of the naturalistic processes that contribute to this burnout. Motivated by the challenges related to the accurate tracking of well-being in real-life, prior work has investigated group-specific machine learning (GS-ML) models that are tailored to groups of participants. We examine a novel GS-ML for estimating well-being from real-life multimodal measures collected in situ from hospital workers. In contrast to the majority of prior work that uses pre-determined clustering criteria, we propose an iterative procedure that refines participant clusters based on the representations learned by the GS-ML models. Motivated by prior work that highlights the differential impact of job demands on well-being, we further explore the participant clusters in terms of demography and job-related attributes. Results indicate that the GS-ML models mostly outperform general models in estimating well-being constructs. The GS-ML models further depict different degrees of predictive power for each participant cluster, as distinguished upon age, education, occupational role, and number of supervisees. The observed discrepancies with respect to the GS-ML model decisions are discussed in association with algorithmic bias.

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