Food reward derives from nutrient content and sensory qualities

Stuphorn, Veit

doi:10.1073/pnas.2109735118

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent proposals in RL theory extended the reward concept to outcomes under homeostatic regulation (Keramati and Gutkin, 2014) and distinguished hedonic reward components from their post-ingestive consequences (Dayan, 2022). Generally, the importance of linking biological reward components to learning and choice theories is increasingly recognized (Rangel, 2013;Suzuki et al, 2017;Averbeck and Murray, 2020;de Araujo et al, 2020;Stuphorn, 2021). Yet, the field lacks experimental data on how nutrient-reward components influence animals' choices in formal RL paradigms.…”

Section: Introductionmentioning

confidence: 99%

Nutrient-Sensitive Reinforcement Learning in Monkeys

Huang

Grabenhorst

2023

J. Neurosci.

View full text Add to dashboard Cite

In Reinforcement Learning (RL), animals choose by assigning values to options and learn by updating these values from reward outcomes. This framework has been instrumental in identifying fundamental learning variables and their neuronal implementations. However, canonical RL models do not explain how reward values are constructed from biologically critical intrinsic reward components, such as nutrients. From an ecological perspective, animals should adapt their foraging choices in dynamic environments to acquire nutrients that are essential for survival. Here, to advance the biological and ecological validity of RL models, we investigated how (male) monkeys adapt their choices to obtain preferred nutrient rewards under varying reward probabilities. We found that the rewards’ nutrient composition strongly influenced learning and choices. The animals’ preferences for specific nutrients (sugar, fat) affected how they adapted to changing reward probabilities: the history of recent rewards influenced monkeys’ choices more strongly if these rewards contained the monkey’s preferred nutrients (‘nutrient-specific reward history’). The monkeys also chose preferred nutrients even when they were associated with lower reward probability. A nutrient-sensitive RL model captured these processes: it updated the values of individual sugar and fat components of expected rewards based on experience and integrated them into subjective values that explained the monkeys’ choices. Nutrient-specific reward prediction errors guided this value-updating process. Our results identify nutrients as important reward components that guide learning and choice by influencing the subjective value of choice options. Extending RL models with nutrient-value functions may enhance their biological validity and uncover nutrient-specific learning and decision variables.SIGNIFICANCE STATEMENT:Reinforcement learning (RL) is an influential framework that formalizes how animals learn from experienced rewards. Although 'reward’ is a foundational concept in RL theory, canonical RL models cannot explain how learning depends on specific reward properties, such as nutrients. Intuitively, learning should be sensitive to the reward’s nutrient components, to benefit health and survival. Here we show that the nutrient (fat, sugar) composition of rewards affects monkeys’ choices and learning in an RL paradigm, and that key learning variables including ‘reward history’ and ‘reward prediction error’ should be modified with nutrient-specific components to account for monkeys’ behavior in our task. By incorporating biologically critical nutrient rewards into the RL framework our findings help advance the ecological validity of RL models.

show abstract

Section: Introductionmentioning

confidence: 99%

Nutrient-Sensitive Reinforcement Learning in Monkeys

Huang

Grabenhorst

2023

J. Neurosci.

View full text Add to dashboard Cite

show abstract

Tuning Potassium and Magnesium Fertilization of Potato in the South of West Siberia

Yakimenko

Naumova

2021

Agronomy

View full text Add to dashboard Cite

Imbalance of nutrients limits crop yields. Although K fertilization receives sufficient attention in research and practice, Mg supply is rather neglected. The effect of Mg fertilization (0, 5 and 10 g Mg/m2), combined with two K fertilization rates (10 and 15 g K/m2), on potato production and soil exchangeable K and Mg was studied in a three-season microplot field experiment in the Novosibirsk region, Russia. Tuber yield did not respond to the increased K fertilization, but increased at 5 and decreased at 10 g Mg/m2. Total Mg concentration in tubers increased at 15 g K/m2, whereas N, P and K were not affected by fertilization. The tuber yield was maximal (3.6 kg/m2) at 10 g K/m2 and 5 g Mg/m2. Soil exchangeable Mg increased by the year, resulting in preferential development of the aboveground phytomass due to apparently increased Mg availability and K/Mg imbalance. Potato production depended on the year, strongly implicating weather conditions. Therefore, the weather and the chemical nature of K and Mg fertilizers (as pertinent to their release mode from fertilizer in soil), are important for balancing their proportions and amounts while assessing interactions among nutrients in potato production and adjusting regional fertilization strategies.

show abstract

Food reward derives from nutrient content and sensory qualities

Cited by 2 publications

References 20 publications

Nutrient-Sensitive Reinforcement Learning in Monkeys

Nutrient-Sensitive Reinforcement Learning in Monkeys

Tuning Potassium and Magnesium Fertilization of Potato in the South of West Siberia

Contact Info

Product

Resources

About