The Effects of Naloxone on Flavor-Calorie Preference Learning Indicate Involvement of Opioid Reward Systems

1996 ◽  
Vol 46 (3) ◽  
pp. 435-450 ◽  
Author(s):  
Ron Mehiel
Keyword(s):  
Preference Learning ◽  
Reward Systems

scholarly journals Role of gut nutrient sensing in stimulating appetite and conditioning food preferences

2012 ◽  
Vol 302 (10) ◽  
pp. R1119-R1133 ◽  
Author(s):  
Anthony Sclafani ◽  
Karen Ackroff
Keyword(s):  
Food Preferences ◽  
Nutrient Utilization ◽  
Nutrient Sensing ◽  
Vagal Afferents ◽  
Preference Learning ◽  
Flavor Preference ◽  
Reward Systems ◽  
Additional Function ◽  
Umami Taste ◽  
Flavor Preference Conditioning

The discovery of taste and nutrient receptors (chemosensors) in the gut has led to intensive research on their functions. Whereas oral sugar, fat, and umami taste receptors stimulate nutrient appetite, these and other chemosensors in the gut have been linked to digestive, metabolic, and satiating effects that influence nutrient utilization and inhibit appetite. Gut chemosensors may have an additional function as well: to provide positive feedback signals that condition food preferences and stimulate appetite. The postoral stimulatory actions of nutrients are documented by flavor preference conditioning and appetite stimulation produced by gastric and intestinal infusions of carbohydrate, fat, and protein. Recent findings suggest an upper intestinal site of action, although postabsorptive nutrient actions may contribute to flavor preference learning. The gut chemosensors that generate nutrient conditioning signals remain to be identified; some have been excluded, including sweet (T1R3) and fatty acid (CD36) sensors. The gut-brain signaling pathways (neural, hormonal) are incompletely understood, although vagal afferents are implicated in glutamate conditioning but not carbohydrate or fat conditioning. Brain dopamine reward systems are involved in postoral carbohydrate and fat conditioning but less is known about the reward systems mediating protein/glutamate conditioning. Continued research on the postoral stimulatory actions of nutrients may enhance our understanding of human food preference learning.

Why a Focus on Eminence is Misguided: A Call to Return to Basic Scientific Values

2017 ◽  
Author(s):  
Katherine S. Corker
Keyword(s):  
Scientific Method ◽  
Information Source ◽  
Current Paper ◽  
Human Cognition ◽  
Reward Systems ◽  
The Moon ◽  
Eradicate Polio ◽  
And Behavior

The scientific method has been used to eradicate polio, send humans to the moon, and enrich understanding of human cognition and behavior. It produced these accomplishments not through magic or appeals to authority, but through open, detailed, and reproducible methods. To call something “science” means there are clear ways to independently and empirically evaluate research claims. There is no need to simply trust an information source. Scientific values thus prioritize transparency and universalism, emphasizing that it matters less who has made a discovery than how it was done. Yet, scientific reward systems are based on identifying individual eminence. The current paper contrasts this focus on individual eminence with reforms to scientific rewards systems that help these systems better align with scientific values.

Bayesian preference learning for interactive multi-objective optimisation

2021 ◽  
Author(s):  
Kendall Taylor ◽  
Huong Ha ◽  
Minyi Li ◽  
Jeffrey Chan ◽  
Xiaodong Li
Keyword(s):  
Preference Learning ◽  
Multi Objective

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems

Games ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 62
Author(s):  
Ralph S. Redden ◽  
Greg A. Gagliardi ◽  
Chad C. Williams ◽  
Cameron D. Hassall ◽  
Olave E. Krigolson
Keyword(s):  
Reinforcement Learning ◽  
Chess Player ◽  
Reward Systems ◽  
The Third ◽  
Reward Positivity ◽  
Eeg Data ◽  
Attentional Engagement ◽  
Level Of Difficulty ◽  
Competitive Games ◽  
Reinforcement Model

When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

Why Don't Public Administrators Take Productivity More Seriously?

1974 ◽  
Vol 3 (4) ◽  
pp. 318-324 ◽  
Author(s):  
Walter L. Balk
Keyword(s):  
Political Institutions ◽  
Reward Systems ◽  
Public Administrators ◽  
The Public ◽  
Low Level

“Until government managers, employees, legislators and the public become intensely involved in the processes of controls, reward systems and political institutions, motivation is liable to remain at a low level.”

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