Biology subjects

Grabenhorst, F.

Publications and source records attributed to Grabenhorst, F..

8 recordsLinked to original sources

bioRxiv2025

Value coding by primate amygdala neurons complies with economic choice theory

Primate amygdala neurons are implicated in coding the value of choice options for decision-making. However, it remains unclear whether amygdala value signals comply with the notion that decision makers maximize the value of rewards, as formalized by key axioms of economic theory. In particular, the continuity axiom of Expected Utility Theory (EUT) postulates that reward maximization is reflected by a trade-off between reward probability and magnitude. Our experiment tested this trade-off in two male macaque monkeys who subjectively ranked three gambles in choices, thus revealing their subjective value. Axiom compliance was defined by choice indifference between the intermediate gamble and a probabilistic combination of the other two gambles. We found that the animals choices between safe and gamble options reflected the integration of probability and magnitude into scalar values consistent with the continuity axiom. In a non-choice task, responses of individual amygdala neurons to separate probability and magnitude cues reflected the monkeys individual preferences: neuronal responses were lower for non-preferred gambles (relative to safe options), higher for preferred gambles and equal at subjective indifference between safe and gamble options. In a choice task, amygdala neurons integrated probability and magnitude into subjective values that reflected individual preferences. In some neurons, value signals transitioned to signals coding the monkeys economic choices and chosen values. These findings identify amygdala neurons as substrates for encoding behavior-matching subjective values according to the continuity axiom of EUT, and for translating these values into economic choices. SIGNIFICANCE STATEMENTThe amygdala--a brain structure in the temporal lobe--has long been associated with emotional responses. More recently, primate amygdala neurons have been implicated in economic decision-making. Here we show that amygdala neurons respond to visual cues indicating reward probability and magnitude in a manner that reflects the integration of these variables into subjective values. Importantly, these value signals complied with the notion that decision makers maximize the value of rewards, as formalized by key axioms of economic theory. Beyond valuation, some neurons processed subjective values into signals predictive of economic choices. Dysfunctional processing of values and choices by the amygdala could play a role in psychiatric and behavioral disorders affecting mental health, in which the amygdala is implicated.

neuroscience↗

bioRxiv2024

Social risk coding by amygdala activity and connectivity with dorsal anterior cingulate cortex

Risk is a fundamental factor affecting individual and social economic decisions, but its neural correlates are largely unexplored in the social domain. The amygdala, together with the dorsal anterior cingulate cortex (dACC), is thought to play a central role in risk taking. Here, we investigated in human volunteers (n=20; 11 females) how risk (defined as variance of reward probability distributions) in a social situation affects decisions and concomitant neural activity as measured with fMRI. We found social variance-risk signals in the amygdala. Activity in lateral parts of the amygdala increased parametrically with social reward variance of the presented options. Behaviorally, 75% of participants were averse to social risk as estimated in a Becker-DeGroot-Marschak auction-like procedure. The stronger this aversion, the more negative was the coupling between risk-related amygdala regions and dACC. This negative relation was significant for social risk attitude but not for the attitude towards variance-risk in juice outcomes. Our results indicate that the amygdala and its coupling with dACC process objective and subjectively evaluated social risk. Moreover, while social risk can be captured with a framework originally established by finance theory for individual risk, the amygdala appears to processes social risk largely separately from individual risk.

neuroscience↗

bioRxiv2024

Dynamic coding and sequential integration of multiple reward attributes by primate amygdala neurons

The value of visual stimuli guides learning, decision-making and motivation. Although stimulus values often depend on multiple attributes, how neurons extract and integrate distinct value components from separate cues remains unclear. Here we recorded the activity of amygdala neurons while monkeys viewed sequential cues indicating the probability and magnitude of expected rewards. Amygdala neurons frequently signalled reward probability in an abstract, stimulus-independent code that generalized across cue formats. While some probability-coding neurons were insensitive to magnitude, signalling pure probability rather than value, many neurons showed biphasic responses that signalled probability and magnitude in a dynamic (temporally-patterned) and flexible (reversible) value code. Specific neurons integrated these reward attributes into risk signals that quantified the uncertainty of expected rewards, distinct from value. Population codes were accurate, mutually transferable between value components and expressed differently across amygdala nuclei. Our findings identify amygdala neurons as a substrate for the sequential integration of multiple reward attributes into value and risk.

neuroscience↗

bioRxiv2022

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of entropy-based metrics in studying adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

neuroscience↗

bioRxiv2021

Nutrient-sensitive reinforcement learning in monkeys

Animals make adaptive food choices to acquire nutrients that are essential for survival. In reinforcement learning (RL), animals choose by assigning values to options and update these values with new experiences. This framework has been instrumental for identifying fundamental learning and decision variables, and their neural substrates. However, canonical RL models do not explain how learning depends on biologically critical intrinsic reward components, such as nutrients, and related homeostatic regulation. Here, we investigated this question in monkeys making choices for nutrient-defined food rewards under varying reward probabilities. We found that the nutrient composition of rewards strongly influenced monkeys choices and learning. The animals preferred rewards high in nutrient content and showed individual preferences for specific nutrients (sugar, fat). These nutrient preferences affected how the animals adapted to changing reward probabilities: the monkeys learned faster from preferred nutrient rewards and chose them frequently even when they were associated with lower reward probability. Although more recently experienced rewards generally had a stronger influence on monkeys choices, the impact of reward history depended on the rewards specific nutrient composition. A nutrient-sensitive RL model captured these processes. It updated the value of individual sugar and fat components of expected rewards from experience and integrated them into scalar values that explained the monkeys choices. Our findings indicate that nutrients constitute important reward components that influence subjective valuation, learning and choice. Incorporating nutrient-value functions into RL models may enhance their biological validity and help reveal unrecognized nutrient-specific learning and decision computations.

neuroscience↗

bioRxiv2020

Scalar human brain responses to vectorial economic choice options: a concept-driven approach

Rewarding choice options typically contain multiple components, but neural signals in single brain voxels are scalar and primarily vary up or down. In a previous study, we had designed reward bundles that contained the same two milkshakes with independently set amounts; we had used psychophysics and rigorous economic concepts to estimate two-dimensional choice indifference curves (IC) that represented revealed stochastic preferences for these bundles in a systematic, integrated manner. All bundles on the same ICs were equally revealed preferred (and thus had same utility, as inferred from choice indifference); bundles on higher ICs (higher utility) were preferred to bundles on lower ICs (lower utility). In the current study, we used the established behavior for testing with functional magnetic resonance imaging (fMRI). We now demonstrate neural responses in reward-related brain structures of human female and male participants, including striatum, midbrain and medial orbitofrontal cortex that followed the characteristic pattern of ICs: similar responses along ICs (same utility despite different bundle composition), but monotonic change across ICs (different utility). Thus, these brain structures integrated multiple reward components into a scalar signal, well beyond the known subjective value coding of single-component rewards. Significance StatementRewards have several components, like the taste and size of an apple, but it is unclear how each component contributes to the overall value of the reward. While choice indifference curves of economic theory provide behavioural approaches to this question, it is unclear whether brain responses capture the preference and utility integrated from multiple components. We report activations in striatum, midbrain and orbitofrontal cortex that follow choice indifference curves representing behavioral preferences over and above variations of individual reward components. In addition, the concept-driven approach encourages future studies on natural, multi-component rewards that are prone to irrational choice of normal and brain-damaged individuals.

neuroscience↗

bioRxiv2020

Compliance with the continuity axiom of Expected Utility Theory supports utility maximization in monkeys

Expected Utility Theory (EUT), the first axiomatic theory of risky choice, describes choices as a utility maximization process: decision makers assign a subjective value (utility) to each choice option and choose the one with the highest utility. The continuity axiom, central to EUT and its modifications, is a necessary and sufficient condition for the definition of numerical utilities. The axiom requires decision makers to be indifferent between a gamble and a specific probabilistic combination of a more preferred and a less preferred gamble. While previous studies demonstrated that monkeys choose according to combinations of objective reward magnitude and probability, a concept-driven experimental approach for assessing the axiomatically defined conditions for maximizing subjective utility by animals is missing. We experimentally tested the continuity axiom for a broad class of gamble types in four male rhesus macaque monkeys, showing that their choice behavior complied with the existence of a numerical utility measure as defined by the economic theory. We used the numerical quantity specified in the continuity axiom to characterize subjective preferences in a magnitude-probability space. This mapping highlighted a trade-off relation between reward magnitudes and probabilities, compatible with the existence of a utility function underlying subjective value computation. These results support the existence of a numerical utility function able to describe choices, allowing for the investigation of the neuronal substrates responsible for coding such rigorously defined quantity. SIGNIFICANCE STATEMENTA common assumption of several economic choice theories is that decisions result from the comparison of subjectively assigned values (utilities). This study demonstrated the compliance of monkey behavior with the continuity axiom of Expected Utility Theory, implying a subjective magnitude-probability trade-off relation which supports the existence of numerical subjective utility directly linked to the theoretical economic framework. We determined a numerical utility measure able to describe choices, which can serve as a correlate for the neuronal activity in the quest for brain structures and mechanisms guiding decisions.

animal behavior and cognition↗

bioRxiv2019

How we choose multi-component rewards

Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multi-component choice options? Here, we measured how stochastic choices revealed preferences for two-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the two components as trade-off: parts of one component were given up for obtaining one additional unit of the other component without a change in preference. The well-ordered, non-overlapping curves satisfied leave-one-out tests, followed predictions by machine learning decoders and correlated with single-dimensional Becker-DeGroot-Marschak (BDM) auction-like bids for the two-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In inter-species comparisons, human performance matched that of highly experienced laboratory monkeys, as measured by accuracy of the critical trade-off between bundle components. These data describe the nature of choices of multi-component choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and non-human primates. The results encourage formal behavioral and neural investigations of normal, irrational and pathological economic choices.

neuroscience↗