A dopamine mechanism for reward maximization.

Individual survival and evolutionary selection require biological organisms to maximize reward. Economic choice theories define the necessary and sufficient conditions, and neuronal signals of decision variables provide mechanistic explanations. Reinforcement learning (RL) formalisms use predictions, actions, and policies to maximize reward. Midbrain dopamine neurons code reward prediction errors (RPE) of subjective reward value suitable for RL. Electrical and optogenetic self-stimulation experiments demonstrate that monkeys and rodents repeat behaviors that result in dopamine excitation. Dopamine excitations reflect positive RPEs that increase reward predictions via RL; against increasing predictions, obtaining similar dopamine RPE signals again requires better rewards than before. The positive RPEs drive predictions higher again and thus advance a recursive reward-RPE-prediction iteration toward better and better rewards. Agents also avoid dopamine inhibitions that lower reward prediction via RL, which allows smaller rewards than before to elicit positive dopamine RPE signals and resume the iteration toward better rewards. In this way, dopamine RPE signals serve a causal mechanism that attracts agents via RL to the best rewards. The mechanism improves daily life and benefits evolutionary selection but may also induce restlessness and greed.

Reliable population code for subjective economic value from heterogeneous neuronal signals in primate orbitofrontal cortex.

Behavior-related neuronal signals often vary between neurons, which might reflect the unreliability of individual neurons or a truly heterogeneous code. This notion may also apply to economic ("value-based") choices and the underlying reward signals. Reward value is subjective and can be described by a nonlinearly weighted magnitude (utility) and probability. Defining subjective values relies on the continuity axiom, whose testing involves structured variations of a wide range of reward magnitudes and probabilities. Axiom compliance demonstrates understanding of the stimuli and the meaningful character of choices. Using these tests, we investigated the encoding of subjective economic value by neurons in a key economic-decision structure of the monkey brain, the orbitofrontal cortex (OFC). We found that individual neurons carry heterogeneous neuronal value signals that largely fail to match the animal's choices. However, neuronal population signals matched the animal's choices well, suggesting accurate subjective economic value encoding by a heterogeneous population of unreliable neurons.

A view-based decision mechanism for rewards in the primate amygdala.

Primates make decisions visually by shifting their view from one object to the next, comparing values between objects, and choosing the best reward, even before acting. Here, we show that when monkeys make value-guided choices, amygdala neurons encode their decisions in an abstract, purely internal representation defined by the monkey's current view but not by specific object or reward properties. Across amygdala subdivisions, recorded activity patterns evolved gradually from an object-specific value code to a transient, object-independent code in which currently viewed and last-viewed objects competed to reflect the emerging view-based choice. Using neural-network modeling, we identified a sequence of computations by which amygdala neurons implemented view-based decision making and eventually recovered the chosen object's identity when the monkeys acted on their choice. These findings reveal a neural mechanism in the amygdala that derives object choices from abstract, view-based computations, suggesting an efficient solution for decision problems with many objects.

Worth the Work? Monkeys Discount Rewards by a Subjective Adapting Effort Cost.

All life must solve how to allocate limited energy resources to maximize benefits from scarce opportunities. Economic theory posits decision makers optimize choice by maximizing 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 male nonhuman primates (Macaca mulatta) in a binary choice task in which reward quantity and physical effort to obtain were varied. Applying random utility modeling to independently estimate reward utility and effort disutility, we show the subtractive effort model better explains out-of-sample choice behavior when compared with parabolic and exponential effort discounting. Furthermore, we demonstrate that effort disutility depends on previous experience of effort: in analogy to work from behavioral labor 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 choices 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.SIGNIFICANCE STATEMENT All 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.

Studying neural responses for multi-component economic choices in human and non-human primates using concept-based behavioral choice experiments.

Realistic, everyday rewards contain multiple components, such as taste and size. However, our reward valuations and the associated neural reward signals are single dimensional (vector to scalar transformation). Here, we present a protocol to identify these single-dimensional neural responses for multi-component choice options in humans and monkeys using concept-based behavioral choice experiments. We describe the use of stringent economic concepts to develop and implement behavioral tasks. We detail regional neuroimaging in humans and fine-grained neurophysiology in monkeys and describe approaches for data analysis. For complete details on the use and execution of this protocol, please refer to our work on humans Seak et al.1 and Pastor-Bernier et al.2 and monkeys Pastor-Bernier et al. 3, Pastor-Bernier et al.4, and Pastor-Bernier et al.5.

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.

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 metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

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 related to explicit external stimuli and rewards. However, personal choices are based on private internal values of the rewards at stake. Without indications of an agent's private internal value, we do not know whether dopamine neurons, or any reward neurons, encode the internal value. The well-established Becker-DeGroot-Marschak (BDM) auction-like mechanism allows participants to place bids for freely stating their private internal value for a good. BDM bids are known to reflect the agent's true internal valuation, as inaccurate bidding results in suboptimal reward ('incentive compatibility'). In our experiment rhesus monkeys placed 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. Responses of midbrain dopamine neurons followed the trial-by-trial variation of bids despite constant, explicitly predicted reward amounts; correspondingly, the dopamine responses were similar when the animal placed similar bids for different reward amounts. Support Vector Regression demonstrated accurate prediction of the animal's bids by as few as twenty dopamine neurons, demonstrating the validity of the dopamine code for internal reward value. Thus, dopamine responses reflect the instantaneous internal subjective reward value rather than the value imposed by external stimuli.

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. Significance: All 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.

Reward Value Revealed by Auction in Rhesus Monkeys.

Economic choice is thought to involve the elicitation of the subjective values of the choice options. Thus far, value estimation in animals has relied on 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"). Male 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 (BCs), 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.SIGNIFICANCE STATEMENT The 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.

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 option's 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 two macaque monkeys in risky and riskless 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 risky and riskless 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.

Risky choice: Probability weighting explains independence axiom violations 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 serves as basis for investigating neuronal decision mechanisms. Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09388-7.

Reward-specific satiety affects subjective value signals in orbitofrontal cortex during multicomponent economic choice.

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural ongoing 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, and 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 the 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. The 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.

Adaptation of utility functions to reward distribution in rhesus monkeys.

This study investigated how the experience of different reward distributions would shape the utility functions that can be inferred from economic choice. Despite the generally accepted notion that utility functions are not insensitive to external references, the exact way in which such changes take place remains largely unknown. Here we benefitted from the capacity to engage in thorough and prolonged empirical tests of economic choice by one of our evolutionary cousins, the rhesus macaque. We analyzed data from thousands of binary choices and found that the animals' preferences changed depending on the statistics of rewards experienced in the past (up to weeks) and that these changes could reflect monkeys' adapting their expectations of reward. The utility functions we elicited from their choices stretched and shifted over several months of sequential changes in the mean and range of rewards that the macaques experienced. However, this adaptation was usually incomplete, suggesting that - even after months - past experiences held weight when monkeys' assigned value to future rewards. Rather than having stable and fixed preferences assumed by normative economic models, our results demonstrate that rhesus macaques flexibly shape their preferences around the past and present statistics of their environment. That is, rather than relying on a singular reference-point, reference-dependent preferences are likely to capture a monkey's range of expectations.

Functions of primate amygdala neurons in economic decisions and social decision simulation.

Long implicated in aversive processing, the amygdala is now recognized as a key component of the brain systems that process rewards. Beyond reward valuation, recent findings from single-neuron recordings in monkeys indicate that primate amygdala neurons also play an important role in decision-making. The reward value signals encoded by amygdala neurons constitute suitable inputs to economic decision processes by being sensitive to reward contingency, relative reward quantity and temporal reward structure. During reward-based decisions, individual amygdala neurons encode both the value inputs and corresponding choice outputs of economic decision processes. The presence of such value-to-choice transitions in single amygdala neurons, together with other well-defined signatures of decision computation, indicate that a decision mechanism may be implemented locally within the primate amygdala. During social observation, specific amygdala neurons spontaneously encode these decision signatures to predict the choices of social partners, suggesting neural simulation of the partner's decision-making. The activity of these 'simulation neurons' could arise naturally from convergence between value neurons and social, self-other discriminating neurons. These findings identify single-neuron building blocks and computational architectures for decision-making and social behavior in the primate amygdala. An emerging understanding of the decision function of primate amygdala neurons can help identify potential vulnerabilities for amygdala dysfunction in human conditions afflicting social cognition and mental health.

Nonhuman Primates Satisfy Utility Maximization in Compliance with the Continuity Axiom of Expected Utility Theory.

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 Expected Utility Theory 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 utility by animals is missing. We experimentally tested the continuity axiom for a broad class of gamble types in 4 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 STATEMENT A 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 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.

Single-Dimensional Human Brain Signals for Two-Dimensional Economic Choice Options.

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 (ICs) 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 (mid-OFC) 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 STATEMENT Rewards 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 (ICs) of economic theory provide behavioral 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 (OFC) that follow choice ICs representing behavioral preferences over and above variations of individual reward components. In addition, the concept-driven approach encourages future studies on natural, multicomponent rewards that are prone to irrational choice of normal and brain-damaged individuals.

Retrospective Valuation of Experienced Outcome Encoded in Distinct Reward Representations in the Anterior Insula and Amygdala.

Our ability to evaluate an experience retrospectively is important because it allows us to summarize its total value, and this summary value can then later be used as a guide in deciding whether the experience merits repeating, or whether instead it should rather be avoided. However, when an experience unfolds over time, humans tend to assign disproportionate weight to the later part of the experience, and this can lead to poor choice in repeating, or avoiding experience. Using model-based computational analyses of fMRI recordings in 27 male volunteers, we show that the human brain encodes the summary value of an extended sequence of outcomes in two distinct reward representations. We find that the overall experienced value is encoded accurately in the amygdala, but its merit is excessively marked down by disincentive anterior insula activity if the sequence of experienced outcomes declines temporarily. Moreover, the statistical strength of this neural code can separate efficient decision-makers from suboptimal decision-makers. Optimal decision-makers encode overall value more strongly, and suboptimal decision-makers encode the disincentive markdown (DM) more strongly. The separate neural implementation of the two distinct reward representations confirms that suboptimal choice for temporally extended outcomes can be the result of robust neural representation of a displeasing aspect of the experience such as temporary decline.SIGNIFICANCE STATEMENT One of the numerous foibles that prompt us to make poor decisions is known as the "Banker's fallacy," the tendency to focus on short-term growth at the expense of long-term value. This effect leads to unwarranted preference for happy endings. Here, we show that the anterior insula in the human brain marks down the overall value of an experience as it unfolds over time if the experience entails a sequence of predominantly negative temporal contrasts. By contrast, the amygdala encodes overall value accurately. These results provide neural indices for the dichotomy of decision utility and experienced utility popularized as Thinking fast and slow by Daniel Kahneman.

Experimentally revealed stochastic preferences for multicomponent choice options.

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 multicomponent choice options? Here, we measured how stochastic choices revealed preferences for 2-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the 2 components as trade-off: parts of 1 component were given up for obtaining 1 additional unit of the other component without a change in preference. The well-ordered, nonoverlapping 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 2-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In interspecies 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 multicomponent choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and nonhuman primates. The results encourage formal behavioral and neural investigations of normal, irrational, and pathological economic choices. (PsycInfo Database Record (c) 2020 APA, all rights reserved).