Valuations of target items are drawn towards unavailable decoy items due to prior expectations.

When people make choices, the items they consider are often embedded in a context (of other items). How this context affects the valuation of the specific item is an important question. High-value context might make items appear less attractive because of contrast-the tendency to normalize perception of an object relative to its background-or more attractive because of assimilation-the tendency to group objects together. Alternatively, a high-value context might increase prior expectations about the item's value. Here, we investigated these possibilities. We examined how unavailable context items affect choices between two target items, as well as the willingness-to-pay for single targets. Participants viewed sets of three items for several seconds before the target(s) were highlighted. In both tasks, we found a significant assimilation-like effect where participants were more likely to choose or place a higher value on a target when it was surrounded by higher-value context. However, these context effects were only significant for participants' fastest choices. Using variants of a drift-diffusion model, we established that the unavailable context shifted participants' prior expectations towards the average values of the sets but had an inconclusive effect on their evaluations of the targets during the decision (i.e. drift rates). In summary, we find that people use context to inform their initial valuations. This can improve efficiency by allowing people to get a head start on their decision. However, it also means that the valuation of an item can change depending on the context.

Pre-acquired Functional Connectivity Predicts Choice Inconsistency.

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context-dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here, we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risky choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects' inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pretrained on FC matrices of 37,000 subjects and fine-tuned it on our data and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system and that synchrony between the motor and value networks plays a crucial role in this tendency.

DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing.

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers' subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects' willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product's image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

The effect of perceptual organization on numerical and preference-based decisions shows inter-subject correlation.

Individual differences in cognitive processing have been the subject of intensive research. One important type of such individual differences is the tendency for global versus local processing, which was shown to correlate with a wide range of processing differences in fields such as decision making, social judgments and creativity. Yet, whether these global/local processing tendencies are correlated within a subject across different domains is still an open question. To address this question, we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local and global information instead of instructing subjects to specifically attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we accounted for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.

Similar functional networks predict performance in both perceptual and value-based decision tasks.

There are numerous commonalities between perceptual and preferential decision processes. For instance, previous studies have shown that both of these decision types are influenced by context. Also, the same computational models can explain both. However, the neural processes and functional connections that underlie these similarities between perceptual and value-based decisions are still unclear. Hence, in the current study, we examine whether perceptual and preferential processes can be explained by similar functional networks utilizing data from the Human Connectome Project. We used resting-state functional magnetic resonance imaging data to predict performance of 2 different decision-making tasks: a value-related task (the delay discounting task) and a perceptual task (the flanker task). We then examined the existence of shared predictive-network features across these 2 decision tasks. Interestingly, we found a significant positive correlation between the functional networks, which predicted the value-based and perceptual tasks. In addition, a larger functional connectivity between visual and frontal decision brain areas was a critical feature in the prediction of both tasks. These results demonstrate that functional connections between perceptual and value-related areas in the brain are inherently related to decision-making processes across domains.

Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity.

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.

Lie to my face: An electromyography approach to the study of deceptive behavior.

BACKGROUND: Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real-life scenarios remain elusive. METHODS: By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses. RESULTS: With an average accuracy of 73%, we identified two groups of participants: Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features. CONCLUSIONS: Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.

Attraction to similar options: The Gestalt law of proximity is related to the attraction effect.

Previous studies have suggested that there are common mechanisms between perceptual and value-based processes. For instance, both perceptual and value-based choices are highly influenced by the context in which the choices are made. However, the mechanisms which allow context to influence our choice process as well as the extent of the similarity between the perceptual and preferential processes are still unclear. In this study, we examine a within-subject relation between the attraction effect, which is a well-known effect of context on preferential choice, and the Gestalt law of proximity. Then, we aim to use this link to better understand the mechanisms underlying the attraction effect. We conducted one study followed by an additional pre-registered replication study, where subjects performed a Gestalt-psychophysical task and a decoy task. Comparing the behavioral sensitivity of each subject in both tasks, we found that the more susceptible a subject is to the proximity law, the more she displayed the attraction effect. These results demonstrate a within-subject relation between a perceptual phenomenon (proximity law) and a value-based bias (attraction effect) which further strengthens the notion of common rules between perceptual and value-based processing. Moreover, this suggests that the mechanism underlying the attraction effect is related to grouping by proximity with attention as a mediator.

Contribution of self- and other-regarding motives to (dis)honesty.

Why would people tell the truth when there is an obvious gain in lying and no risk of being caught? Previous work suggests the involvement of two motives, self-interest and regard for others. However, it remains unknown if these motives are related or distinctly contribute to (dis)honesty, and what are the neural instantiations of these motives. Using a modified Message Game task, in which a Sender sends a dishonest (yet profitable) or honest (less profitable) message to a Receiver, we found that these two motives contributed to dishonesty independently. Furthermore, the two motives involve distinct brain networks: the LPFC tracked potential value to self, whereas the rTPJ tracked potential losses to other, and individual differences in motives modulated these neural responses. Finally, activity in the vmPFC represented a balance of the two motives unique to each participant. Taken together, our results suggest that (dis)honest decisions incorporate at least two separate cognitive and neural processes-valuation of potential profits to self and valuation of potential harm to others.

Item Features Interact With Item Category in Their Influence on Preferences.

Low-level visual features are known to play a role in value-based decision-making. However, most previous studies focused on the role of only a single low-level feature or only for one type of item. These studies also used only one method of measurement and provided a theory accounting for those specific findings. We aimed to utilize a different more robust approach. We tested the contribution of low-level visual features to value-based decision-making of three item types: fractal-art images, faces, and snack food items. We used two techniques to estimate values: subjective ratings and actual choices. We found that low-level visual features contribute to value-based decision-making even after controlling for higher level features relevant for each item category (for faces, features like eye distance and for food snacks, features like price and calories). Importantly, we show that, overall, while low-level visual features consistently contribute to value-based decision-making as was previously shown, different features distinctively contribute to preferences of specific item types, as was evident when we estimated values using both techniques. We claim that theories relying on the role of single features for individual item types do not capture the complexity of the contribution of low-level visual features to value-based decision-making. Our conclusions call for future studies using multiple item types and various measurement methods for estimating value in order to modify current theories and construct a unifying framework regarding the relationship between low-level visual features and choice.

The formation of preference in risky choice.

A key question in decision-making is how people integrate amounts and probabilities to form preferences between risky alternatives. Here we rely on the general principle of integration-to-boundary to develop several biologically plausible process models of risky-choice, which account for both choices and response-times. These models allowed us to contrast two influential competing theories: i) within-alternative evaluations, based on multiplicative interaction between amounts and probabilities, ii) within-attribute comparisons across alternatives. To constrain the preference formation process, we monitored eye-fixations during decisions between pairs of simple lotteries, designed to systematically span the decision-space. The behavioral results indicate that the participants' eye-scanning patterns were associated with risk-preferences and expected-value maximization. Crucially, model comparisons showed that within-alternative process models decisively outperformed within-attribute ones, in accounting for choices and response-times. These findings elucidate the psychological processes underlying preference formation when making risky-choices, and suggest that compensatory, within-alternative integration is an adaptive mechanism employed in human decision-making.

Bounded rationality in C. elegans is explained by circuit-specific normalization in chemosensory pathways.

Rational choice theory assumes optimality in decision-making. Violations of a basic axiom of economic rationality known as "Independence of Irrelevant Alternatives" (IIA) have been demonstrated in both humans and animals and could stem from common neuronal constraints. Here we develop tests for IIA in the nematode Caenorhabditis elegans, an animal with only 302 neurons, using olfactory chemotaxis assays. We find that in most cases C. elegans make rational decisions. However, by probing multiple neuronal architectures using various choice sets, we show that violations of rationality arise when the circuit of olfactory sensory neurons is asymmetric. We further show that genetic manipulations of the asymmetry between the AWC neurons can make the worm irrational. Last, a context-dependent normalization-based model of value coding and gain control explains how particular neuronal constraints on information coding give rise to irrationality. Thus, we demonstrate that bounded rationality could arise due to basic neuronal constraints.

The neural computation of inconsistent choice behavior.

Humans are often inconsistent (irrational) when choosing among simple bundles of goods, even without any particular changes to framing or context. However, the neural computations that give rise to such inconsistencies are still unknown. Similar to sensory perception and motor output, we propose that a substantial component of inconsistent behavior is due to variability in the neural computation of value. Here, we develop a novel index that measures the severity of inconsistency of each choice, enabling us to directly trace its neural correlates. We find that the BOLD signal in the vmPFC, ACC, and PCC is correlated with the severity of inconsistency on each trial and with the subjective value of the chosen alternative. This suggests that deviations from rational choice arise in the regions responsible for value computation. We offer a computational model of how variability in value computation is a source of inconsistent choices.

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography-based prediction in neuromarketing.

In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)-based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event-related potential design, inter-subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: Economics > Interactive Decision-Making Economics > Individual Decision-Making Psychology > Prediction Neuroscience > Cognition.

Objective Physiological Measurements but Not Subjective Reports Moderate the Effect of Hunger on Choice Behavior.

Hunger is a powerful driver of human behavior, and is therefore of great interest to the study of psychology, economics, and consumer behavior. Assessing hunger levels in experiments is often biased, when using self-report methods, or complex, when using blood tests. We propose a novel way of objectively measuring subjects' levels of hunger by identifying levels of alpha-amylase (AA) enzyme in their saliva samples. We used this measure to uncover the effect of hunger on different types of choice behaviors. We found that hunger increases risk-seeking behavior in a lottery-choice task, modifies levels of vindictiveness in a social decision-making task, but does not have a detectible effect on economic inconsistency in a budget-set choice task. Importantly, these findings were moderated by AA levels and not by self-report measures. We demonstrate the effects hunger has on choice behavior and the problematic nature of subjective measures of physiological states, and propose to use reliable and valid biologically based methods to overcome these problems.

Common Sense in Choice: The Effect of Sensory Modality on Neural Value Representations.

Although it is well established that the ventromedial prefrontal cortex (vmPFC) represents value using a common currency across categories of rewards, it is unknown whether the vmPFC represents value irrespective of the sensory modality in which alternatives are presented. In the current study, male and female human subjects completed a decision-making task while their neural activity was recorded using functional magnetic resonance imaging. On each trial, subjects chose between a safe alternative and a lottery, which was presented visually or aurally. A univariate conjunction analysis revealed that the anterior portion of the vmPFC tracks subjective value (SV) irrespective of the sensory modality. Using a novel cross-modality multivariate classifier, we were able to decode auditory value based on visual trials and vice versa. In addition, we found that the visual and auditory sensory cortices, which were identified using functional localizers, are also sensitive to the value of stimuli, albeit in a modality-specific manner. Whereas both primary and higher-order auditory cortices represented auditory SV (aSV), only a higher-order visual area represented visual SV (vSV). These findings expand our understanding of the common currency network of the brain and shed a new light on the interplay between sensory and value information processing.

Impairment of decision-making in multiple sclerosis: A neuroeconomic approach.

OBJECTIVE: To assess the decision-making impairment in patients with multiple sclerosis (MS) and how they relate to other cognitive domains. METHODS: We performed a cross-sectional analysis in 84 patients with MS, and 21 matched healthy controls using four tasks taken from behavioral economics: (1) risk preferences, (2) choice consistency, (3) delay of gratification, and (4) rate of learning. All tasks were conducted using real-world reward outcomes (food or money) in different real-life conditions. Participants underwent cognitive examination using the Brief Repeatable Battery-Neuropsychology. RESULTS: Patients showed higher risk aversion (general propensity to choose the lottery was 0.51 vs 0.64, p = 0.009), a trend to choose more immediate rewards over larger but delayed rewards ( p = 0.108), and had longer reactions times ( p = 0.033). Choice consistency and learning rates were not different between groups. Progressive patients chose slower than relapsing patients. In relation to general cognitive impairments, we found correlations between impaired decision-making and impaired verbal memory ( r = 0.29, p = 0.009), visual memory ( r = -0.37, p = 0.001), and reduced processing speed ( r = -0.32, p = 0.001). Normalized gray matter volume correlated with deliberation time ( r = -0.32, p = 0.005). CONCLUSION: Patients with MS suffer significant decision-making impairments, even at the early stages of the disease, and may affect patients' quality and social life.

State dependent valuation: the effect of deprivation on risk preferences.

The internal state of an organism affects its choices. Previous studies in various non-human animals have demonstrated a complex, and in some cases non-monotonic, interaction between internal state and risk preferences. Our aim was to examine the systematic effects of deprivation on human decision-making across various reward types. Using both a non-parametric approach and a classical economic analysis, we asked whether the risk attitudes of human subjects towards money, food and water rewards would change as a function of their internal metabolic state. Our findings replicate some previous work suggesting that, on average, humans become more risk tolerant in their monetary decisions, as they get hungry. However, our specific approach allowed us to make two novel observations about the complex interaction between internal state and risk preferences. First, we found that the change in risk attitude induced by food deprivation is a general phenomenon, affecting attitudes towards both monetary and consumable rewards. But much more importantly, our data indicate that rather than each subject becoming more risk tolerant as previously hypothesized based on averaging across subjects, we found that as a population of human subjects becomes food deprived the heterogeneity of their risk attitudes collapses towards a fixed point. Thus subjects who show high-risk aversion while satiated shift towards moderate risk aversion when deprived but subjects who are risk tolerant become more risk averse. These findings demonstrate a more complicated interaction between internal state and risk preferences and raise some interesting implications for both day-to-day decisions and financial market structures.

A computational hypothesis for allostasis: delineation of substance dependence, conventional therapies, and alternative treatments.

The allostatic theory of drug abuse describes the brain's reward system alterations as substance misuse progresses. Neural adaptations arising from the reward system itself and from the antireward system provide the subject with functional stability, while affecting the person's mood. We propose a computational hypothesis describing how a virtual subject's drug consumption, cognitive substrate, and mood interface with reward and antireward systems. Reward system adaptations are assumed interrelated with the ongoing neural activity defining behavior toward drug intake, including activity in the nucleus accumbens, ventral tegmental area, and prefrontal cortex (PFC). Antireward system adaptations are assumed to mutually connect with higher-order cognitive processes occurring within PFC, orbitofrontal cortex, and anterior cingulate cortex. The subject's mood estimation is a provisional function of reward components. The presented knowledge repository model incorporates pharmacokinetic, pharmacodynamic, neuropsychological, cognitive, and behavioral components. Patterns of tobacco smoking exemplify the framework's predictive properties: escalation of cigarette consumption, conventional treatments similar to nicotine patches, and alternative medical practices comparable to meditation. The primary outcomes include an estimate of the virtual subject's mood and the daily account of drug intakes. The main limitation of this study resides in the 21 time-dependent processes which partially describe the complex phenomena of drug addiction and involve a large number of parameters which may underconstrain the framework. Our model predicts that reward system adaptations account for mood stabilization, whereas antireward system adaptations delineate mood improvement and reduction in drug consumption. This investigation provides formal arguments encouraging current rehabilitation therapies to include meditation-like practices along with pharmaceutical drugs and behavioral counseling.

The root of all value: a neural common currency for choice.

How do humans make choices between different types of rewards? Economists have long argued on theoretical grounds that humans typically make these choices as if the values of the options they consider have been mapped to a single common scale for comparison. Neuroimaging studies in humans have recently begun to suggest the existence of a small group of specific brain sites that appear to encode the subjective values of different types of rewards on a neural common scale, almost exactly as predicted by theory. We have conducted a meta analysis using data from thirteen different functional magnetic resonance imaging studies published in recent years and we show that the principle brain area associated with this common representation is a subregion of the ventromedial prefrontal cortex (vmPFC)/orbitofrontal cortex (OFC). The data available today suggest that this common valuation path is a core system that participates in day-to-day decision making suggesting both a neurobiological foundation for standard economic theory and a tool for measuring preferences neurobiologically. Perhaps even more exciting is the possibility that our emerging understanding of the neural mechanisms for valuation and choice may provide fundamental insights into pathological choice behaviors like addiction, obesity and gambling.