Biology subjects

Levy, D. J.

Publications and source records attributed to Levy, D. J..

7 recordsLinked to original sources

bioRxiv2026

Altering facial preferences with context

Facial features are known to play a key role in how we judge a persons traits. However, in many scenarios people do not evaluate a person in isolation but are required to compare and choose between multiple candidates. Yet, the principles that govern these decisions remain unknown. In economic literature, decisions are known to be influenced by the context of available options, but it is unclear to what extent these principles apply to social decisions about naturalistic stimuli such as human faces. Here, we bridged between these two fields of research - face evaluations and economic decisions - to investigate whether facial preferences can be systematically shifted by context, exhibiting a well-known choice context effect, the "decoy effect". We asked participants to choose the person they think is more trustworthy from either a set of two ("target" and "competitor") or three faces (including a "decoy"). Based on economic theory, we carefully selected face sets using their trustworthiness rankings and perceptual similarity, and tested them across several exploratory samples (n=100-400) and one large, preregistered experiment (n=1099). We found that adding a decoy face to the choice set increased participants choices of the target option by up to 7%, while the decoy face was rarely selected, consistent with a typical decoy effect. These results provide direct evidence that social judgements are affected by the same context-dependent principles known from economic choices, and that the decoy effect can be generalized to complex naturalistic stimuli such as human faces, relevant to various real-world applications. Significance statementPeople often rely on facial judgements when choosing between candidates, like politicians or potential partners. These judgments are typically made in comparison to available alternatives rather than in isolation. In economic literature, decisions are known to depend on the context of alternatives, but it remains unclear whether social evaluations follow the same principles. Here, tested whether the "decoy effect", a well-known choice context effect, applies to social judgments. Participants were asked to choose the more trustworthy individual from either two (decoy-absent) or three faces (decoy-present). Adding a decoy face increased selection of the target face by up to 7%, while the decoy itself was rarely chosen, showing that social judgments are governed by the same context-dependent principles as economic choices.

neuroscience↗

bioRxiv2025

Latent dimensions in neural representations predict choice context effects

Choices are affected by the context of available alternatives, a phenomenon termed choice context effects. Current models of context effects require options to be described by two explicit numerical attributes. However, decision-makers might represent these options by additional latent attributes, which are hard to define a-priori. We propose to use participants neural representations to access the full attribute set they consider and predict context effects without modelling any explicit attributes. We first estimated the context effects elicited by lotteries using a behavioral sample. Then we recruited two fMRI samples with preregistered design to estimate the neural representations of each lottery without the context of choice. We predicted the context effects using only the similarity in neural representations between the individual lotteries, improving both out-of-sample and in-sample predictions compared to traditional methods. These neural representations encoded a mixture of explicit and latent attributes, previously inaccessible to researchers using only behavioral methods.

neuroscience↗

bioRxiv2022

The trembling hand unraveled: the motor dynamics and neural sources of choice inconsistency

Extensive evidence shows that humans are inconsistent with their choices. Yet, the neural mechanism underlying this type of choices remains unknown. Here, we aim to show that inconsistent choice is tied to the valuation process, but can also arise from motor errors during task execution. We report the results from three behavioral and neuroimaging studies. Subjects completed a risky-choice task to test their inconsistency levels, followed by two novel tasks, explicitly designed to examine motor output. We recorded mouse trajectories during task execution and designed 34 features to analyze motor dynamics in an exploratory manner. We show that motor dynamics predict inconsistency levels, even when motor output was absent any valuation elements. In the neuroimaging study, we show that inconsistency is associated with value brain circuits, but at the same time, is also related to activity in motor circuits. These findings suggest that (at least) two neural sources of noise contribute to inconsistent choice behavior.

neuroscience↗

bioRxiv2019

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 independently 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.

neuroscience↗

bioRxiv2018

Visual features contribute differently to preferences for different item categories

Low-level visual features have been known to play a role in value-based decision-making. However, thus far, mainly single features were tested on one type of item using one method of measurement. Here, we test the contribution of low-level visual features on three items types: fractal-art images, faces, and snack food items. We test the role of visual features on preferences using both subjective ratings and choices. We show that low-level visual features contribute to value-based decision-making even after controlling for higher level configural features of faces like eye-distance and market features of snacks like calories. Importantly, we show that while low-level visual features consistently contribute to value-based decision-making, different features contribute to different types of items when using different measurement methods. Our study highlights the necessity of using multiple item types and multiple measurement methods to construct a unifying framework regarding the contribution of low-level features to value-based decision-making.

neuroscience↗

bioRxiv2018

More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects individual future preferences over the products and the commercials population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects preferences and marketing campaigns success.

neuroscience↗

bioRxiv2018

Asymmetric Overlap in Neuronal Sensation Constraints Rational Choice in C. elegans

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. We developed tests for IIA in the nematode Caenorhabditis elegans, an animal with only 302 neurons, using olfactory chemotaxis assays. We found that in most cases C. elegans make rational decisions. However, by probing multiple neuronal architectures using various choice sets, we show that asymmetric sensation of odor options by the AWCON neuron can lead to violations of rationality. We further show that genetic manipulations of the asymmetry between the AWC neurons can make the worm rational or irrational. Last, a 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.

neuroscience↗