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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).
Attention-dependent modulation of cortical taste circuits revealed by Granger causality with signal-dependent noise.
We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention.
Selective attention to affective value alters how the brain processes olfactory stimuli.
How does selective attention to affect influence sensory processing? In a functional magnetic resonance imaging investigation, when subjects were instructed to remember and rate the pleasantness of a jasmine odor, activations were greater in the medial orbito-frontal and pregenual cingulate cortex than when subjects were instructed to remember and rate the intensity of the odor. When the subjects were instructed to remember and rate the intensity, activations were greater in the inferior frontal gyrus. These top-down effects occurred not only during odor delivery but started in a preparation period after the instruction before odor delivery, and continued after termination of the odor in a short-term memory period. Thus, depending on the context in which odors are presented and whether affect is relevant, the brain prepares itself, responds to, and remembers an odor differently. These findings show that when attention is paid to affective value, the brain systems engaged to prepare for, represent, and remember a sensory stimulus are different from those engaged when attention is directed to the physical properties of a stimulus such as its intensity. This differential biasing of brain regions engaged in processing a sensory stimulus depending on whether the cognitive demand is for affect-related versus more sensory-related processing may be an important aspect of cognition and attention. This has many implications for understanding the effects not only of olfactory but also of other sensory stimuli.
Attentional modulation of affective versus sensory processing: functional connectivity and a top-down biased activation theory of selective attention.
Top-down selective attention to the affective properties of taste stimuli increases activation to the taste stimuli in the orbitofrontal cortex (OFC) and pregenual cingulate cortex (PGC), and selective attention to the intensity of the stimuli increases the activation in the insular taste cortex, but the origin of the top-down attentional biases is not known. Using psychophysiological interaction connectivity analyses, we showed that in the anterior lateral prefrontal cortex (LPFC) at Y = 53 mm the correlation with activity in OFC and PGC seed regions was greater when attention was to pleasantness compared with when attention was to intensity. Conversely, we showed that in a more posterior region of the LPFC at Y = 34 the correlation with activity in the anterior insula seed region was greater when attention was to intensity compared with when attention was to pleasantness. We also showed that correlations between areas in these separate processing streams were dependent on selective attention to affective value versus physical intensity of the stimulus. We then propose a biased activation theory of selective attention to account for the findings and contrast this with a biased competition theory of selective attention.
How cognition modulates affective responses to taste and flavor: top-down influences on the orbitofrontal and pregenual cingulate cortices.
How cognition influences the affective brain representations of the taste and flavor of a food is important not only for understanding top-down influences in the brain, but also in relation to the topical issues of appetite control and obesity. We found using functional magnetic resonance imaging that activations related to the affective value of umami taste and flavor (as shown by correlations with pleasantness ratings) in the orbitofrontal cortex were modulated by word-level descriptors. Affect-related activations to taste were modulated in a region that receives from the orbitofrontal cortex, the pregenual cingulate cortex, and to taste and flavor in another region that receives from the orbitofrontal cortex, the ventral striatum. Affect-related cognitive modulations were not found in the insular taste cortex, where the intensity but not the pleasantness of the taste was represented. We conclude that top-down language-level cognitive effects reach far down into the earliest cortical areas that represent the appetitive value of taste and flavor. This is an important way in which cognition influences the neural mechanisms that control appetite.
The representation of oral fat texture in the human somatosensory cortex.
How fat is sensed in the mouth and represented in the brain is important in relation to the pleasantness of food, appetite control, and the design of foods that reproduce the mouthfeel of fat yet have low energy content. We show that the human somatosensory cortex (SSC) is involved in oral fat processing via functional coupling to the orbitofrontal cortex (OFC), where the pleasantness of fat texture is represented. Using functional MRI, we found that activity in SSC was more strongly correlated with the OFC during the consumption of a high fat food with a pleasant (vanilla) flavor compared to a low fat food with the same flavor. This effect was not found in control analyses using high fat foods with a less pleasant flavor or pleasant-flavored low fat foods. SSC activity correlated with subjective ratings of fattiness, but not of texture pleasantness or flavor pleasantness, indicating a representation that is not involved in hedonic processing per se. Across subjects, the magnitude of OFC-SSC coupling explained inter-individual variation in texture pleasantness evaluations. These findings extend known SSC functions to a specific role in the processing of pleasant-flavored oral fat, and identify a neural mechanism potentially important in appetite, overeating, and obesity.
Neural systems underlying decisions about affective odors.
Decision-making about affective value may occur after the reward value of a stimulus is represented and may involve different brain areas to those involved in decision-making about the physical properties of stimuli, such as intensity. In an fMRI study, we delivered two odors separated by a delay, with instructions on different trials to decide which odor was more pleasant or more intense or to rate the pleasantness and intensity of the second odor without making a decision. The fMRI signals in the medial prefrontal cortex area 10 (medial PFC) and in regions to which it projects, including the anterior cingulate cortex (ACC) and insula, were higher when decisions were being made compared with ratings, implicating these regions in decision-making. Decision-making about affective value was related to larger signals in the dorsal part of medial area 10 and the agranular insula, whereas decisions about intensity were related to larger activations in the dorsolateral prefrontal cortex (dorsolateral PFC), ventral premotor cortex, and anterior insula. For comparison, the mid orbitofrontal cortex (OFC) had activations related not to decision-making but to subjective pleasantness ratings, providing a continuous representation of affective value. In contrast, areas such as medial area 10 and the ACC are implicated in reaching a decision in which a binary outcome is produced.
How the brain represents the reward value of fat in the mouth.
The palatability and pleasantness of the sensory properties of foods drive food selection and intake and may contribute to overeating and obesity. Oral fat texture can make food palatable and pleasant. To analyze its neural basis, we correlated humans' subjective reports of the pleasantness of the texture and flavor of a high- and low-fat food with a vanilla or strawberry flavor, with neural activations measured with functional magnetic resonance imaging. Activity in the midorbitofrontal and anterior cingulate cortex was correlated with the pleasantness of oral fat texture and in nearby locations with the pleasantness of flavor. The pregenual cingulate cortex showed a supralinear response to the combination of high fat and pleasant, sweet flavor, implicating it in the convergence of fat texture and flavor to produce a representation of highly pleasant stimuli. The subjective reports of oral fattiness were correlated with activations in the midorbitofrontal cortex and ventral striatum. The lateral hypothalamus and amygdala were more strongly activated by high- versus low-fat stimuli. This discovery of which brain regions track the subjective hedonic experience of fat texture will help to unravel possible differences in the neural responses in obese versus lean people to oral fat, a driver of food intake.
Planning activity for internally generated reward goals in monkey amygdala neurons.
The best rewards are often distant and can only be achieved by planning and decision-making over several steps. We designed a multi-step choice task in which monkeys followed internal plans to save rewards toward self-defined goals. During this self-controlled behavior, amygdala neurons showed future-oriented activity that reflected the animal's plan to obtain specific rewards several trials ahead. This prospective activity encoded crucial components of the animal's plan, including value and length of the planned choice sequence. It began on initial trials when a plan would be formed, reappeared step by step until reward receipt, and readily updated with a new sequence. It predicted performance, including errors, and typically disappeared during instructed behavior. Such prospective activity could underlie the formation and pursuit of internal plans characteristic of goal-directed behavior. The existence of neuronal planning activity in the amygdala suggests that this structure is important in guiding behavior toward internally generated, distant goals.
Prediction of subjective affective state from brain activations.
Decoding and information theoretic techniques were used to analyze the predictions that can be made from functional magnetic resonance neuroimaging data on individual trials. The subjective pleasantness produced by warm and cold applied to the hand could be predicted on single trials with typically in the range 60-80% correct from the activations of groups of voxels in the orbitofrontal and medial prefrontal cortex and pregenual cingulate cortex, and the information available was typically in the range 0.1-0.2 (with a maximum of 0.6) bits. The prediction was typically a little better with multiple voxels than with one voxel, and the information increased sublinearly with the number of voxels up to typically seven voxels. Thus the information from different voxels was not independent, and there was considerable redundancy across voxels. This redundancy was present even when the voxels were from different brain areas. The pairwise stimulus-dependent correlations between voxels, reflecting higher-order interactions, did not encode significant information. For comparison, the activity of a single neuron in the orbitofrontal cortex can predict with 90% correct and encode 0.5 bits of information about whether an affectively positive or negative visual stimulus has been shown, and the information encoded by small numbers of neurons is typically independent. In contrast, the activation of a 3 x 3 x 3-mm voxel reflects the activity of approximately 0.8 million neurons or their synaptic inputs and is not part of the information encoding used by the brain, thus providing a relatively poor readout of information compared with that available from small populations of neurons.
Elevated emotional reactivity in affective but not cognitive components of theory of mind: a psychophysiological study.
Recent research proposes that theory of mind (ToM), that is the ability to infer other people's mental state, is a multidimensional construct and that a distinction may be made between affective and cognitive ToM. We examined whether these two subcomponents of ToM correspond to different levels in skin conductance responses (SCRs). Seventeen healthy adults listened to ten affective (faux pas) ToM stories, ten cognitive ToM stories and ten non-ToM stories. Results demonstrated significantly elevated SCR for affective ToM as compared with cognitive ToM and control stories, with no differences in SCR levels in the latter two story types. We discuss the possible underlying mechanisms for these differential psychophysiological correlates of affective and cognitive ToM processing, and suggest further investigations especially in clinical populations.
Value, pleasure and choice in the ventral prefrontal cortex.
Rapid advances have recently been made in understanding how value-based decision-making processes are implemented in the brain. We integrate neuroeconomic and computational approaches with evidence on the neural correlates of value and experienced pleasure to describe how systems for valuation and decision-making are organized in the prefrontal cortex of humans and other primates. We show that the orbitofrontal and ventromedial prefrontal (VMPFC) cortices compute expected value, reward outcome and experienced pleasure for different stimuli on a common value scale. Attractor networks in VMPFC area 10 then implement categorical decision processes that transform value signals into a choice between the values, thereby guiding action. This synthesis of findings across fields provides a unifying perspective for the study of decision-making processes in the brain.
Selective attention to affective value alters how the brain processes taste stimuli.
How does selective attention to affect influence sensory processing? In an fMRI investigation, when subjects were instructed to remember and rate the pleasantness of a taste stimulus, 0.1 M monosodium glutamate, activations were greater in the medial orbitofrontal and pregenual cingulate cortex than when subjects were instructed to remember and rate the intensity of the taste. When the subjects were instructed to remember and rate the intensity, activations were greater in the insular taste cortex. An interaction analysis showed that this dissociation of taste processing, depending on whether attention to pleasantness or intensity was relevant, was highly significant (P < 0.0002). Thus, depending on the context in which tastes are presented and whether affect is relevant, the brain responds to a taste differently. These findings show that, when attention is paid to affective value, the brain systems engaged to represent the sensory stimulus of taste are different from those engaged when attention is directed to the physical properties of a stimulus such as its intensity. This differential biasing of brain regions engaged in processing a sensory stimulus, depending on whether the cognitive demand is for affect-related vs. more sensory-related processing, may be an important aspect of cognition and attention. This has many implications for understanding the effects not only of taste but also of other sensory stimuli.
Decisions under ambiguity and decisions under risk: correlations with executive functions and comparisons of two different gambling tasks with implicit and explicit rules.
We conducted two experiments with healthy subjects to investigate the possible relationships between Iowa Gambling Task (IGT) performance and executive functions as well as IGT performance and decision-making in a task with explicit rules, the Game of Dice Task (GDT). Results indicated that only the last trials of the IGT were correlated with executive functions and GDT performance. We suggest that the IGT taps into two mechanisms of decision-making: decisions under ambiguity in the first trials and decisions under risk in the latter trials. Results have impact on the interpretation of deficient IGT performance in patients with frontal lobe dysfunctions.
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.
Preferences for nutrients and sensory food qualities identify biological sources of economic values in monkeys.
Value is a foundational concept in reinforcement learning and economic choice theory. In these frameworks, individuals choose by assigning values to objects and learn by updating values with experience. These theories have been instrumental for revealing influences of probability, risk, and delay on choices. However, they do not explain how values are shaped by intrinsic properties of the choice objects themselves. Here, we investigated how economic value derives from the biologically critical components of foods: their nutrients and sensory qualities. When monkeys chose nutrient-defined liquids, they consistently preferred fat and sugar to low-nutrient alternatives. Rather than maximizing energy indiscriminately, they seemed to assign subjective values to specific nutrients, flexibly trading them against offered reward amounts. Nutrient-value functions accurately modeled these preferences, predicted choices across contexts, and accounted for individual differences. The monkeys' preferences shifted their daily nutrient balance away from dietary reference points, contrary to ecological foraging models but resembling human suboptimal eating in free-choice situations. To identify the sensory basis of nutrient values, we developed engineering tools that measured food textures on biological surfaces, mimicking oral conditions. Subjective valuations of two key texture parameters-viscosity and sliding friction-explained the monkeys' fat preferences, suggesting a texture-sensing mechanism for nutrient values. Extended reinforcement learning and choice models identified candidate neuronal mechanisms for nutrient-sensitive decision-making. These findings indicate that nutrients and food textures constitute critical reward components that shape economic values. Our nutrient-choice paradigm represents a promising tool for studying food-reward mechanisms in primates to better understand human-like eating behavior and obesity.
Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience.
Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects ('object risk') or actions ('action risk'). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.
Neural Basis for Economic Saving Strategies in Human Amygdala-Prefrontal Reward Circuits.
Economic saving is an elaborate behavior in which the goal of a reward in the future directs planning and decision-making in the present. Here, we measured neural activity while subjects formed simple economic saving strategies to accumulate rewards and then executed their strategies through choice sequences of self-defined lengths. Before the initiation of a choice sequence, prospective activations in the amygdala predicted subjects' internal saving plans and their value up to two minutes before a saving goal was achieved. The valuation component of this planning activity persisted during execution of the saving strategy and predicted subjects' economic behavior across different tasks and testing days. Functionally coupled amygdala and prefrontal cortex activities encoded distinct planning components that signaled the transition from saving strategy formation to execution and reflected individual differences in saving behavior. Our findings identify candidate neural mechanisms for economic saving in amygdala and prefrontal cortex and suggest a novel planning function for the human amygdala in directing strategic behavior toward self-determined future rewards.
Componential Granger causality, and its application to identifying the source and mechanisms of the top-down biased activation that controls attention to affective vs sensory processing.
We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger causality sum to 1, allowing causal effects to be directly compared between systems. We show using componential Granger causality analysis applied to an fMRI investigation that there is a top-down attentional effect from the anterior dorsolateral prefrontal cortex to the orbitofrontal cortex when attention is paid to the pleasantness of a taste, and that this effect depends on the activity in the orbitofrontal cortex as shown by the interaction term. Correspondingly there is a top-down attentional effect from the posterior dorsolateral prefrontal cortex to the insular primary taste cortex when attention is paid to the intensity of a taste, and this effect depends on the activity of the insular primary taste cortex as shown by the interaction term. Componential Granger causality thus not only can reveal the directionality of effects between areas (and these can be bidirectional), but also allows the mechanisms to be understood in terms of whether the causal influence of one system on another depends on the state of the system being causally influenced. Componential Granger causality measures the full effects of second order statistics by including variance and covariance effects between each time series, thus allowing interaction effects to be measured, and also provides a systematic framework within which to measure the effects of cross, self, and noise contributions to causality. The findings reveal some of the mechanisms involved in a biased activation theory of selective attention.