Biology subjects

Schultz, W.

Publications and source records attributed to Schultz, W..

18 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↗

bioRxiv2025

Coding of the basic components of subjective value in primate dopamine neurons: subjectively weighted reward amount and probability

Behavioral choices of uncertain rewards suggest that agents construct subjective reward value by combining the basic value components of utility and weighted probability. Despite the general acceptance of this evaluation mechanism for explaining economic choice, knowledge about its neuronal implementation is fractionated and remains essentially unknown. We investigated in monkeys whether reward signals in dopamine neurons might represent subjective reward value based on these two fundamental value components. Despite some heterogeneity across individual neurons, the dopamine population signal reliably represented the axiomatically defined integration of utility and weighted probability into subjective value in a way that closely matched the animal-specific choice behavior. In particular, we identified a crucial contribution of subjectively weighted probability to the dopamine signal of subjective reward value. These data demonstrate a neuronal implementation of subjective value constructed from the two most basic subjective reward components. Preprint ServerAn earlier version of this manuscript has been uploaded to bioRxiv in February 2025 (CC-BY).

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↗

bioRxiv2023

Systematic comparison of risky choices in humans and monkeys

The past decades have seen tremendous progress in fundamental studies on economic choice in humans. However, elucidation of the underlying neuronal processes requires invasive neurophysiological studies that are met with difficulties in humans. Monkeys as evolutionary closest relatives offer a solution. The animals display sophisticated and well-controllable behavior that allows to implement key constructs of proven economic choice theories. However, the similarity of economic choice between the two species has never been systematically investigated. We investigated compliance with the independence axiom (IA) of expected utility theory as one of the most demanding choice tests and compared IA violations between humans and monkeys. Using generalized linear modeling and cumulative prospect theory (CPT), we found that humans and monkeys made comparable risky choices, although their subjective values (utilities) differed. These results suggest similar fundamental choice mechanism across these primate species and encourage to study their underlying neurophysiological mechanisms.

neuroscience↗

bioRxiv2023

Dopamine signals encode internally determined subjective value regardless of externally indicated reward attributes

The dopamine reward prediction error signal is known to be subjective but has so far only been assessed in aggregate choices. However, personal choices fluctuate across trials and thus reflect the instantaneous subjective reward value. In the well-established Becker-DeGroot-Marschak (BDM) auction-like mechanism, participants are encouraged to place bids that accurately reveal their instantaneous subjective reward value; inaccurate bidding results in suboptimal reward ( incentive compatibility). In our experiment, male rhesus monkeys became experienced over several years to place accurate BDM bids for juice rewards without specific external constraints. Their bids for physically identical rewards varied trial by trial and increased overall for larger rewards. In these highly experienced animals, responses of midbrain dopamine neurons followed the trial-by-trial variations of bids despite constant, explicitly predicted reward amounts. Inversely, dopamine responses were similar with similar bids for different physical reward amounts. Support Vector Regression demonstrated accurate prediction of the animals bids by as few as twenty dopamine neurons. Thus, the phasic dopamine reward signal reflects instantaneous subjective reward value.

neuroscience↗

bioRxiv2023

Worth the work? Monkeys discount rewards by a subjective adapting effort cost

All life must solve how to allocate limited energy resources to maximise benefits from scarce opportunities. Economic theory posits decision makers optimise choice by maximising the subjective benefit (utility) of reward minus the subjective cost (disutility) of the required effort. While successful in many settings, this model does not fully account for how experience can alter reward-effort trade-offs. Here we test how well the subtractive model of effort disutility explains the behavior of two non-human primates (Macaca mulatta) in a binary choice task in which reward quantity and physical effort to obtain were varied.Applying random utility modelling to independently estimate reward utility and effort disutility, we show the subtractive effort model better explains out-of-sample choice behavior when compared to parabolic and exponential effort discounting. Furthermore, we demonstrate that effort disutility is dependent on previous experience of effort: in analogy to work from behavioral labour economics, we develop a model of reference-dependent effort disutility to explain the increased willingness to expend effort following previous experience of effortful options in a session. The result of this analysis suggests that monkeys discount reward by an effort cost that is measured relative to an expected effort learned from previous trials. When this subjective cost of effort, a function of context and experience, is accounted for, trial-by-trial choice behavior can be explained by the subtractive cost model of effort.Therefore, in searching for net utility signals that may underpin effort-based decision-making in the brain, careful measurement of subjective effort costs is an essential first step. SignificanceAll decision-makers need to consider how much effort they need to expend when evaluating potential options. Economic theories suggest that the optimal way to choose is by cost-benefit analysis of reward against effort. To be able to do this efficiently over many decision contexts, this needs to be done flexibly, with appropriate adaptation to context and experience. Therefore, in aiming to understand how this might be achieved in the brain, it is important to first carefully measure the subjective cost of effort. Here we show monkeys make reward-effort cost-benefit decisions, subtracting the subjective cost of effort from the subjective value of rewards. Moreover, the subjective cost of effort is dependent on the monkeys experience of effort in previous trials.

neuroscience↗

bioRxiv2022

Formal cooperation in macaque monkeys obtained via single-payoff change from formal coordination games

Coordination and cooperation are hallmarks of the behavior of social animals. Coordination requires common choices to obtain maximum benefit, whereas cooperation requires to forgo immediate selfish outcome for later common maximum benefit. A well validated economic game for investigating cooperation is Prisoner Dilemma (PD). Recent studies show that monkeys cooperate to a limited extent when playing an iterated PD. In our experiment, macaque monkeys made choices on a touchscreen to obtain juice reward whose amount depended on the choices of both animals. We designed four coordination games and two cooperation games (iterated PD) that differed only in a single payoff (the so-called temptation) while all other payoffs remained constant. The increasing temptation payoff resulted in performance that varied somewhat in the coordination game (probability of common choice between p = 0.55 and p = 0.70) but dropped in both cooperation games while nevertheless remaining significant (p = 0.28 to p = 0.68). The response time of the second player increased significantly when the first player chose the cooperative option across all games, suggesting reciprocation; further, the animals seemed to benefit from seeing the action of the other player, indicating that the choices incorporated a social component. Taken together, our results demonstrate good cooperation in the iterated PD by macaque monkeys after being primed with coordination games.

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

Probability weighting explains Independence Axiom violations of Expected Utility Theory in monkeys

Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serve as basis for investigating neuronal decision mechanisms.

animal behavior and cognition↗

bioRxiv2021

Reliable population utility signal from diverse economic value coding in orbitofrontal neurons

Behavior-related neuronal signals often vary between neurons. Despite the unreliability of individual neurons, brains are able to accurately represent and drive behavior. The notion may also apply to economic ( value-based) choices and the underlying reward signals. Reward value is subjective and can be defined by nonlinear weighting of magnitude (utility) and probability. Using a wide variety of reward magnitude and probability, we assessed subjective reward value at choice indifference between safe and risky rewards as prescribed by the continuity axiom that provides stringent criteria for meaningful choice. We found that individual neurons in the orbitofrontal cortex (OFC) of monkeys carry unreliable and heterogeneous neuronal signals for subjective value that largely fails to match the animals choice. However, the averaged neuronal signals matched well the animals choices, suggesting reliable subjective economic value encoding by the observed population of unreliable neurons. HighlightsO_LIDifferent from widely held views, reliable neuronal information processing may not require reliable processors. C_LIO_LINeurons in monkey orbitofrontal cortex (OFC) process reward magnitude and probability heterogeneously and unreliably. C_LIO_LIDespite unreliable neuronal processing, OFC population activity codes choices reliably. C_LIO_LIReliability systems performance from unreliable elements seems to be a broad feature of neuronal reward coding. C_LI In briefUsing stringent concepts of behavioral choice, Ferrari-Toniolo and Schultz describe unreliable individual reward neurons in monkey orbitofrontal cortex whose activity combines to a reliable population code for economic choice.

neuroscience↗

bioRxiv2021

Mechanisms Underlying Economic Revealed Preference Of Multi-Component Bundles In Primate Orbitofrontal Cortex

Classic neuro-economic studies suggest that people and animals alike assign values to individual attributes arriving at a total summed valuation and therefore overall appraisal of the offer (Kahnt et al., 2011; Lak et al., 2014). However, further work is needed to determine whether such individual attribute appraisals can predict choice between multi-component bundles. In this paper we show the importance and applications of economic theory to decision neurophysiology concerning multi-component attribute options or bundles. We applied random utility modelling (McFadden, 1973) to choices amongst 10 different two-component bundles in non-human primates (Macacca mulatta). We found that behavioural random utility (RUM) complies well with revealed preference theory and recapitulates the fundamental properties of empirically obtained indifference curves (IC). Bundles positioned in the same IC display equal choice preference and similar utility whereas bundles positioned across different ICs display different choice preferences. RUM also captures the characteristic shape of IC on each bundle revealing the synergetic interactions between components. We extended RUM to neurophysiological data and focused our investigation to orbitofrontal cortex (OFC), area 13. Neuronal RUM complied with behavioural RUM at single neuron and population level (N: 54). Neural coding of utility was present at target onset, choice, and reward. However, a distinct and separate group of neurons (N: 26) showed partial compliance with utility in either the chosen or the unchosen bundle options and correlated with both utility and probability of choice. We show that relative chosen-utility coding constitutes a separate type of computation and suggest a role for this type of neuron in value-to-choice processing in OFC.

neuroscience↗

bioRxiv2021

Comparing utility functions between risky and riskless choice in rhesus monkeys

Decisions can be risky or riskless, depending on the outcomes of the choice. Expected Utility Theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the options value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of macaque monkeys in riskless and risky decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for riskless and risky choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

animal behavior and cognition↗

bioRxiv2020

Reduced neuronal value signals in monkey orbitofrontal cortex during relative reward-specific satiety

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural on-going consumption affects reward functions in learning and approach behavior. More specifically, satiety reduces the subjective economic value of individual rewards during choice between options that typically contain multiple reward components. The unconfounded assessment of economic reward value requires tests at choice indifference between two options, which is difficult to achieve with sated rewards. By conceptualizing choices between options with multiple reward components ( bundles), Revealed Preference Theory may offer a solution. Despite satiety, choices against an unaltered reference bundle may remain indifferent when the reduced value of a sated bundle reward is compensated by larger amounts of an unsated reward of the same bundle; then the value loss of the sated reward is indicated by the amount of the added unsated reward. Here we show psychophysically titrated choice indifference in monkeys between bundles of differently sated rewards. Neuronal chosen value signals in orbitofrontal cortex (OFC) followed closely the subjective value change within recording periods of individual neurons. A neuronal classifier distinguishing the bundles and predicting choice substantiated the subjective value change. Choice between conventional single rewards confirmed the neuronal changes seen with two-reward bundles. Thus, reward-specific satiety reduces subjective reward value signals in OFC. With satiety being an important factor of subjective reward value, these results extend the notion of subjective economic reward value coding in OFC neurons. SignificanceOn-going consumption reduces the subjective value of rewards to different degrees depending on their individual properties, a phenomenon referred to as sensory-specific satiety. Such value change should be manifested in economic choices, and neuronal signals for subjective economic reward value should be sensitive to reward-specific satiety. We tested monkeys during choice between two options that each contained two different rewards ( bundles); the two rewards were prone to different degrees of satiety. On-going reward consumption affected choices in a way that indicated satiety-induced reward-specific change of subjective economic value. Neuronal responses in the monkey orbitofrontal cortex (OFC) followed the differential reduction of subjective economic value. These results satisfy a crucial requirement for subjective reward value coding in OFC neurons.

neuroscience↗

bioRxiv2020

Adaptation of utility functions to reward distribution in rhesus monkeys

This study investigated the influence of experienced reward distributions on the shape of utility functions inferred from economic choice. Utility is the hypothetical variable that appears to be maximized by the choice. Despite the generally accepted notion that utility functions are not insensitive to external references, the exact occurrence of such changes remains largely unknown. Here we benefitted from the capacity to perform thorough and extensive experimental tests of one of our evolutionary closest, experimentally viable and intuitively understandable species, the rhesus macaque monkey. Data from thousands of binary choices demonstrated that the animals preferences changed dependent on the statistics of recently experienced rewards and adapted to future expected rewards. The elicited utility functions shifted and extended their shape with several months of changes in the mean and range of reward distributions. However, the adaptations were usually not complete, suggesting that past experiences remained present when anticipating future rewards. Through modelling, we found that reinforcement learning provided a strong basis for explaining these adaptations. Thus, rather than having stable and fixed preferences assumed by normative economic models, rhesus macaques flexibly shaped their preferences to optimize decision-making according to the statistics of the environment.

animal behavior and cognition↗

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

Let a monkey do your bidding: Reward valuation in an auction-like mechanism

Economic choice is thought to involve the elicitation of the subjective values of the choice options. Thus far, value estimation in animals has relied upon stochastic choices between multiple options presented in repeated trials and expressed from averages of dozens of trials. However, subjective reward valuations are made moment-to-moment and do not always require alternative options; their consequences are usually felt immediately. Here we describe a Becker-DeGroot-Marschak (BDM) auction-like mechanism that provides more direct and simple valuations with immediate consequences. The BDM encourages agents to truthfully reveal their true subjective value in individual choices (incentive compatibility). Monkeys reliably placed well-ranked BDM bids for up to five juice volumes while paying from a water budget. The bids closely approximated the average subjective values estimated with conventional binary choices, thus demonstrating procedural invariance and aligning with the wealth of knowledge acquired with these less direct estimation methods. The feasibility of BDM bidding in monkeys paves the way for an analysis of subjective neuronal value signals in single trials rather than from averages; the feasibility also bridges the gap to the increasingly used BDM method in human neuroeconomics. SignificanceThe subjective economic value of rewards cannot be measured directly but must be inferred from observable behavior. Until now, the estimation method in animals was rather complex and required comparison between several choice options during repeated choices; thus, such methods did not respect the imminence of the outcome from individual choices. However, human economic research has developed a simple auction-like procedure that can reveal in a direct and immediate manner the true subjective value in individual choices (Becker-DeGroot-Marschak, BDM, mechanism). The current study implemented this mechanism in rhesus monkeys and demonstrates its usefulness for eliciting meaningful value estimates of liquid rewards. The mechanism allows future neurophysiological assessment of subjective reward value signals in single trials of controlled animal tasks.

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↗