Correspondence: Are Cognitive Functions Localizable? Colin Camerer et al. versus Marieke van Rooij and John G. Holden

The Fall 2011 issue of this journal published a two-paper section on “Neuroeconomics.” One paper, by Ernst Fehr and Antonio Rangel, clearly and concisely summarized a small part of the fast-growing literature. The second paper, “It’s about Space, It’s about Time, Neuroeconomics, and the Brain Sublime,” by Marieke van Rooij and Guy Van Orden, is beautifully written and enjoyable to read, but misleading in many critical ways. A number of economists and neuroscientists working at the intersection of the two fields shared our reaction and have signed this letter, as shown below. Some of the paper’s descriptions of empirical findings and methods in neuroeconomics are incomplete, badly out of date, or flatly wrong. In studies the authors describe in detail, their skeptical interpretations have often been refuted by published data, old and new, that they overlook

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

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

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

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

Average effect of state on risk behavior: Within-subject.

<p>A) The average (across subjects) of the proportion to choose the lottery option out of the total number of choices made in both states for all reward types. B) The average (across subjects) of the fitted risk parameters (<i>α</i>) in both states for all reward types. Data represents the mean ± s.e.m. * p<0.05; ** p<0.002; *** p<0.0001.</p

Effect of state on relative value: Representative agent in EUT approach.

<p>The values of the fitted scaling factors (using maximum likelihood estimation) for food and water relative to money as a function of state for the representative agent. Note that the <i>Constant</i> variable represents the value in the deprived state and the <i>State</i> variable represents the addition (subtraction) during the satiated state. A <b>Bold</b><i>Italic</i> font represents a significant effect of state. Scale – the fitted scaling factor. Beta – the slope of the logit function. Coef. – the regression coefficient; Std Err – standard errors; z – z score of the regression; P>z – pvalue of the regression; CI – confidence interval.</p

Trials timeline.

<p>On each trial two options were presented for two seconds. This presentation was followed by the appearance of a yellow cross, which signaled a maximum of 1.5 s for indicating the preferred option by pressing one of two buttons on a computer mouse. Thereafter, a feedback screen indicating the subject's choice was presented for 0.5 s plus the difference between 1.5 s and the reaction time (RT) to make sure that the total time of choice plus feedback was 2 s. This was followed immediately with the next trial.</p