High dietary restraint improves performance on a food-motivated probabilistic selection task

2019 ◽  
Author(s):  
Jennifer R Sadler ◽  
Grace Elisabeth Shearrer ◽  
Nichollette Acosta ◽  
Kyle Stanley Burger
Keyword(s):  
Reinforcement Learning ◽  
Instrumental Conditioning ◽  
Food Reinforcement ◽  
Dietary Restraint ◽  
Memory Task ◽  
Brain Response ◽  
Anthropometric Measures ◽  
Instrumental Task ◽  
The Impact ◽  
Reward And Punishment

BACKGROUND: Dietary restraint represents an individual’s intent to limit their food intake and has been associated with impaired passive food reinforcement learning. However, the impact of dietary restraint on an active, response dependent learning is poorly understood. In this study, we tested the relationship between dietary restraint and food reinforcement learning using an active, instrumental conditioning task. METHODS: A sample of ninety adults completed a response-dependent instrumental conditioning task with reward and punishment using sweet and bitter tastes. Brain response via functional MRI was measured during the task. Participants also completed anthropometric measures, reward/motivation related questionnaires, and a working memory task. Dietary restraint was assessed via the Dutch Restrained Eating Scale. RESULTS: Two groups were selected from the sample: high restraint (n=29, score >2.5) and low restraint (n=30; score <1.85). High restraint was associated with significantly higher BMI (p=0.003) and lower N-back accuracy (p=0.045). The high restraint group also was marginally better at the instrumental conditioning task (p=0.066, r=0.37). High restraint was also associated with significantly greater brain response in the intracalcarine cortex (MNI: 15, -69, 12; k=35, pfwe< 0.05) to bitter taste, compared to neutral taste.CONCLUSIONS: High restraint was associated with improved performance on an instrumental task testing how individuals learn from reward and punishment. This may be mediated by greater brain response in the primary visual cortex, which has been associated with mental representation. Results suggest that dietary restraint does not impair response-dependent reinforcement learning.

scholarly journals Network organization during probabilistic learning via taste outcomes

2020 ◽  
Author(s):  
Jennifer R Sadler ◽  
Grace Elisabeth Shearrer ◽  
Nichollette Acosta ◽  
Afroditi Papantoni ◽  
Jessica R. Cohen ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Reinforcement Learning ◽  
Network Topology ◽  
Bitter Taste ◽  
Instrumental Conditioning ◽  
Learning Task ◽  
Sweet Taste ◽  
Network Organization ◽  
Brain Response ◽  
Whole Brain

Reinforcement learning guides food decisions, yet how the brain learns from taste in humans is not fully understood. Existing research examines reinforcement learning from taste using passive condition paradigms, but response-dependent instrumental conditioning better reflects natural eating behavior. Here, we examined brain response during a taste-motivated reinforcement learning task and how measures of task-based network structure were related to behavioral outcomes. During a functional MRI scan, 85 participants completed a probabilistic selection task with feedback via sweet taste or bitter taste. Whole brain response and functional network topology measures, including identification of communities and community segregation, were examined during choice, sweet taste, and bitter taste conditions. Relative to the bitter taste, sweet taste was associated with increased whole brain response in the hippocampus, oral somatosensory cortex, and orbitofrontal cortex. Sweet taste was also related to differential community assignment of the ventromedial prefrontal cortex and ventrolateral prefrontal cortex compared to bitter taste. During choice, increasing segregation of a community containing the amygdala, hippocampus, and right fusiform gyrus was associated with increased sensitivity to punishment on the task’s posttest. Further, healthy BMI was associated with differential community structure compared to overweight and obese BMI, where high BMI reflected increased connectivity of visual regions. Together, results demonstrate that network topology of learning and memory regions during choice is related to avoid a bitter taste and that BMI is associated with increased connectivity of area involved in processing external stimuli. Network organization and topology provides unique insight into individual differences in brain response to instrumental conditioning via taste reinforcers.

scholarly journals An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Adam Bignold ◽  
Francisco Cruz ◽  
Richard Dazeley ◽  
Peter Vamplew ◽  
Cameron Foale
Keyword(s):  
Reinforcement Learning ◽  
Information Source ◽  
Human Interaction ◽  
Evaluation Methodology ◽  
External Information ◽  
Preliminary Evaluation ◽  
Learning Agents ◽  
Learning Agent ◽  
Knowledge Bias ◽  
The Impact

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

scholarly journals Exploring optimal control of epidemic spread using reinforcement learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abu Quwsar Ohi ◽  
M. F. Mridha ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid
Keyword(s):  
Reinforcement Learning ◽  
Economic Crisis ◽  
Transmission Rate ◽  
Optimal Solution ◽  
Short Length ◽  
Reproduction Rate ◽  
Economic Factors ◽  
Epidemic Spread ◽  
The Impact ◽  
Individual Human

AbstractPandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent’s performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.

scholarly journals Does Feedback-Related Brain Response during Reinforcement Learning Predict Socio-motivational (In-)dependence in Adolescence?

2016 ◽  
Vol 7 ◽  
Author(s):  
Diana Raufelder ◽  
Rebecca Boehme ◽  
Lydia Romund ◽  
Sabrina Golde ◽  
Robert C. Lorenz ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Brain Response

T73. Altered Reinforcement Learning From Reward and Punishment in Women With Anorexia Nervosa

2018 ◽  
Vol 83 (9) ◽  
pp. S157
Author(s):  
Christina Wierenga ◽  
Amanda Bischoff-Grethe ◽  
Emily Romero ◽  
Danika Peterson ◽  
Tiffany Brown ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Reinforcement Learning ◽  
Reward And Punishment

Searching for the ‘Right’ Legend: The Impact of Legend Position on Legend Decoding in a Cartographic Memory Task

2018 ◽  
Vol 57 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Dennis Edler ◽  
Julian Keil ◽  
Marie-Christin Tuller ◽  
Anne-Kathrin Bestgen ◽  
Frank Dickmann
Keyword(s):  
Memory Task ◽  
The Right ◽  
The Impact

Effects of CSF Properties on Brain Response Under Impact Loading

2009 ◽  
Author(s):  
M. S. Chafi ◽  
V. Dirisala ◽  
G. Karami ◽  
M. Ziejewski
Keyword(s):  
Human Head ◽  
Brain Response ◽  
Pia Mater ◽  
Mechanical Shocks ◽  
The Central Nervous System ◽  
Simultaneous Loading ◽  
The Impact ◽  
Impact Experiments ◽  
The Brain

In the central nervous system, the subarachnoid space is the interval between the arachnoid membrane and the pia mater. It is filled with a clear, watery liquid called cerebrospinal fluid (CSF). The CSF buffers the brain against mechanical shocks and creates buoyancy to protect it from the forces of gravity. The relative motion of the brain due to a simultaneous loading is caused because the skull and brain have different densities and the CSF surrounds the brain. The impact experiments are usually carried out on cadavers with no CSF included because of the autolysis. Even in the cadaveric head impact experiments by Hardy et al. [1], where the specimens are repressurized using artificial CSF, this is not known how far this can replicate the real functionality of CSF. With such motivation, a special interest lies on how to model this feature in a finite element (FE) modeling of the human head because it is questionable if one uses in vivo CSF properties (i.e. bulk modulus of 2.19 GPa) to validate a FE human head against cadaveric experimental data.

scholarly journals Multikulturalisasi Pendidikan Islam Sejak Dini di Era Digital

FIKROTUNA ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 818-833
Author(s):  
Mufiqur Rahman
Keyword(s):  
Social Environment ◽  
Direct Method ◽  
Education Institution ◽  
Islamic Education ◽  
Digital Culture ◽  
The Impact ◽  
Reward And Punishment

The failure to response digitalization evidenzed by the violence of child in the education institution and social environment that KPAI declared as the impact of digital culture and civilization that indicated instead of the failure of home’s education. This paper will discuss and share an idea with literature view methode with the urgent of multicultural Islamic education. The Values of Multiculturalsm can be tought early like humanity, tolerance, respecting minority, loving a weak, keeping unity and peace, mentaining the culture. Those values can be implemented by the following methode such as;  1. al-Awamiru wa an-Nawahy (order and forbid),. 2. Taqdimu al-Qudwah al-Toyyibah (modelling), 3. Al-Tsawabu wa al-I’qobu (reward and punishment). 4. Al-Iyha’u (direct method), . 5. Metode Qisshoh(story).

scholarly journals Relationship between systolic blood pressure and decision-making during emotional processing

2021 ◽  
Author(s):  
Sophie Jacqueline Andree Betka ◽  
David Watson ◽  
Sarah N Garfinkel ◽  
Gaby Pfeifer ◽  
Henrique Sequeira ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Decision Making ◽  
Systolic Blood Pressure ◽  
Emotional Processing ◽  
Subjective Experience ◽  
Short Term Memory ◽  
Memory Task ◽  
Emotional States ◽  
Physiological Changes ◽  
The Impact

Objective: Emotional states are expressed in body and mind through subjective experience of physiological changes. In previous work, subliminal priming of anger prior to lexical decisions increased systolic blood pressure (SBP). This increase predicted the slowing of response times (RT), suggesting that baroreflex-related autonomic changes and their interoceptive (feedback) representations, influence cognition. Alexithymia is a subclinical affective dysfunction characterized by difficulty in identifying emotions. Atypical autonomic and interoceptive profiles are observed in alexithymia. Therefore, we sought to identify mechanisms through which SBP fluctuations during emotional processing might influence decision-making, including whether alexithymia contributes to this relationship. Methods Thirty-two male participants performed an affect priming paradigm and completed the Toronto Alexithymia Scale. Emotional faces were briefly presented (20ms) prior a short-term memory task. RT, accuracy and SBP were recorded on a trial-by-trial basis. Generalized mixed-effects linear models were used to evaluate the impact of emotion, physiological changes, alexithymia score, and their interactions, on performances. Results A main effect of emotion was observed on accuracy. Participants were more accurate on trials with anger primes, compared to neutral priming. Greater accuracy was related to increased SBP. An interaction between SBP and emotion was observed on RT: Increased SBP was associated with RT prolongation in the anger priming condition, yet this relationship was absent under the sadness priming. Alexithymia did not significantly moderate the above relationships. Conclusions Our data suggest that peripheral autonomic responses during affective challenges guide cognitive processes. We discuss our findings in the theoretical framework proposed by Lacey and Lacey (1970).

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