Our Lab
NeuroEconomics and NeuroMarketing Lab
In our lab we use an interdisciplinary approach, which involves quantitative economic theories, combined with advanced behavioral methods, and theoretical models from psychology, marketing, and economics with neuroscience techniques such as functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG).
The main aim in our studies is to better understand how we make decisions and what are the neural mechanisms underlying value-based choices. The projects in the lab range from examining the neural correlates of value computations and the common currency network, deciphering the neural mechanisms of irrational choice behaviors both in humans and in C.elegans, through looking for a common denominator between basic visual perception and value computations, to projects that aim to predict future preferences and population success of marketing stimuli using neural signals.
A few of our current projects:
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Predicting consumers' preferences using EEG-based neural measurements.
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The neural mechanisms of choice inconsistency.
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The neural building blocks of rational choice: examining loss aversion in a simple nervous system.
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The neural representations of the decoy effect.
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The effect of low-level visual and auditory features on valuation.
How Do We Make Decisions?
We are looking for participants in an exciting new experiment: you can come and win up to 100 NIS! Please contact: dinolevy@tauex.tau.ac.il
The People
Dino Levy
Associate Professor
I am an associate professor at the Marketing Department at Coller School of Management, Tel Aviv University and the head of the Neuroeconomics and Neuromarketing lab. I am also a member of the Sagol School of Neuroscience at Tel Aviv University and a visiting scholar at the Institute for the Interdisciplinary Study of Decision Making at NYU. In my lab we are trying to understand consumer decision-making and various aspects of value representation in the brain. Specifically, we are exploring the effects of basic visual attributes on decision-making and on neural representations of value. We also examine the neural mechanisms of irrational behavior. Finally, we are trying to predict consumer’s future preferences using neural measurements above and beyond the accuracy of behavioral measurements.
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Asaf Madar
Ph.D. Student
I'm a 4th year Direct-PhD student at Sagol School of Neuroscience, under the joint supervision of Prof. Dino Levy and Dr. Ido Tavor. I completed my BSc in Biology and Psychology with emphasis on Neuroscience from the Sagol School of Neuroscience, Tel Aviv University in 2020 (summa cum laude). I'm interested in the connection between neural representations of options and their effect on choices. My research focuses on the neural mechanisms of the decoy effect and inconsistent choices, and the prediction of choices from both resting-state and task-fMRI, using machine learning algorithms.
Inbal Gur Arie
Ph.D. Student
I am a third year Ph.D. student in psychology, under the joint supervision of Prof. Dino Levy and Prof. Liad Mudrik. I have a B.mus in piano performance and an M.A. in cognitive psychology, in which I studied the unconscious influences on decision making and examined if or how they may threaten the notion of free will.
My interest is how value is represented in the brain and I examine if and to what extent can we use the neural data, measured using EEG, to predict subjects valuation and the success of marketing stimuli at the population level.
Valentina Parada Nino
MS.c Student
I am an international master student at Sagol School of Neuroscience in Tel Aviv University. I got my Bachelors degree in psychology with a minor in neuroscience at Los Andes University in Colombia. During my undergraduate studies I realized I was interested in behavioral economics. In my master's degree the research I have been working on is focused on the understanding of valuation mechanisms when we are presented with a product commercial. Mainly I'm working on models that will allow us to predict, based on low level features, which commercials would be more successful. Moreover I'm interested in continuing this research using image based advertisements. In my free time I enjoy going to the gym, reading and spending time with my family.
Alumni
Maytal Shabat-Simon, PhD.
Assistant professor at Tel Hai College
Tal Sela, PhD.
Lecturer, School of Social Sciences and Humanities, Kinneret College
Anastasia Shuster, PhD.
Adam Hakim, PhD.
Liz Izakson, PhD.
Vered Kurtz-David, PhD.
Ben Hadad, MSc.
Noa Palmon, MSc.
Shira Klorfeld, M.A.
Dor Shukrun, M.A.
Aviad Barel Ozana, M.A.
Tzili Lewin, M.Sc.
Publications
Articles
Glickman, M., Sela, T., Usher, M., & Levy, D. J. (2023). The effect of perceptual organization on numerical and preference-based decisions shows inter-subject correlation. Psychonomic Bulletin and Review.
Or-Borichev, A., Gurevitch, G., Klovatch, I., Greental, A., Lerner, Y., Levy, D. J., & Hendler, T. (2023). Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity. NeuroImage, 266.
Izakson, L., Gal, S., Shahar, M., Tavor, I. & Levy, D. J. Similar functional networks predict performance in both perceptual and value-based decision tasks. Cereb. Cortex (2022).
Shuster, A., Inzelberg, L., Ossmy, O., Izakson, L., Hanein, Y. & Levy, D. J. Lie to my face: An electromyography approach to the study of deceptive behavior. Brain Behav. e2386 (2021). https://doi.org/10.1002/brb3.2386
Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M. & Levy, D. J. Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning. Int. J. Res. Mark. 38, (2021). 10.1016/j.ijresmar.2020.10.005
Izakson, L., Zeevi, Y. & Levy, D. J. Attraction to similar options: The Gestalt law of proximity is related to the attraction effect. PLoS One 15, (2020). https://dx.doi.org/10.1371%2Fjournal.pone.0240937
Shuster, A. & Levy, D. J. Contribution of self- and other-regarding motives to (dis)honesty. Sci. Rep. 10, (2020). https://doi.org/10.1038/s41598-020-72255-5
Oren, S., Sela, T., Levy, D. J. & Schonberg, T. Item Features Interact With Item Category in Their Influence on Preferences. Front. Psychol. 11, (2020).https://doi.org/10.3389/fpsyg.2020.00988
Mudrik, L., Levy, D. J., Gavenas, J. & Maoz, U. Studying volition with actions that matter: Combining the fields of neuroeconomics and the neuroscience of volition. Psychol. Conscious. Theory Res. Pract. 7, 67–86 (2020). https://psycnet.apa.org/doi/10.1037/cns0000260
Glickman M, Sharoni O, Levy DJ, Niebur E, Stuphorn V, Usher M (2019) The formation of preference in risky choice. PLoS Comput Biol 15(8): e1007201. August 29, 2019 https://doi.org/10.1371/journal.pcbi.1007201
Cohen, D., Teichman, G., Volovich, M. et al. Bounded rationality in C. elegans is explained by circuit-specific normalization in chemosensory pathways. Nat Commun 10, 3692 (2019) August 13, 2019 https://doi.org/10.1038/s41467-019-11715-7
Kurtz-David, V., Persitz, D., Webb, R. et al. The neural computation of inconsistent choice behavior. Nat Commun 10, 1583 . April 05, 2019 https://doi.org/10.1038/s41467-019-09343-2
Hakim, A, Levy, DJ. A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing. WIREs Cogn Sci. 2019; 10:e1485. November 29, 2018 . https://doi.org/10.1002/wcs.1485
Book Chapters
Levy DJ and Glimcher PW, Common value representation – a neuroeconomic perspective. In Handbook of Value: Perspectives from Economics, Neuroscience, Philosophy, Psychology and Sociology, 2015. Tobias Brosch and David Sander, editors. Oxford Scholarship Online
Our Projects
Predicting consumers’ preferences using
EEG-based neural measurements.
In many cases, relying only on consumers’ verbal reports or their responses to questionnaires for evaluation of their preferences can result in biased, inaccurate or inconclusive results. Therefore, we employ neural measurements using EEG to acquire information about consumer’s preferences that is unobtainable through conventional methods. Mainly, we obtain neural data while consumers evaluate various related marketing campaigns (such as TV ads, internet pages, pictures, etc.), extract multiple measurements from the recordings, and try to predict consumers’ future choices and behavior using diverse machine & deep learning approaches. We aspire to increase the predictive power currently attainable with standard non-physiological measures and to identify consumers’ true preferences — namely the choice they will make at the time of their purchasing decision.
The Neural mechanisms of decoy effects
Studies on value-based decision-making have shown that changing the context could affect choices. One of the famous examples of such context-dependent changes is the family of decoy effects, and specifically the attraction effect. Previous models in the literature rely on explicit numerical properties of stimuli (attributes) to explain and account for the occurrence of decoy effects. We aim to examine a novel method for predicting which stimuli will create the attraction effect, based on similarity of neural representations. Subjects make binary and trinary choices inside the fMRI scanner, and we examine the intensity of the attraction effect in behavior. Using the neural activity, we then build a predictive model from representational maps of the binary and trinary options. This liberates the model from the need for structured numerical representations of options and help expand the scope of the decoy attraction effect to novel, unstructured, and real-world stimuli.
The Neural Mechanism of Choice Inconsistency
Revealed preference theory provides analytic tools to measure the inconsistency embedded in a decision-maker’s choices, thus their deviation from a rational benchmark. We apply this theory together with neuroimaging techniques to trace the neural mechanisms that generate inconsistent behavior. We developed a novel index that measures the severity of inconsistency for each choice, and directly trace its neural correlates using fMRI. We also aim to examine the extent that motor noise contributes to inconsistency. In addition, we attempt to develop a computational model of choice suggesting that variability in the computation of value and the motor output can explain our findings.
The Neural Building
Blocks of Rational Choice:
Examining Loss aversion in a
Simple Nervous System
A collaboration with Prof. Oded Rechavi. In order to examine the basic mechanisms if and when organisms obey economic axioms and rational choice, we use the nematode Caenorhabditis elegans (C.elegans), an animal with only 300 neurons and a fully mapped connectome. After finding a basic biological rule for IIA violations at the receptor level, we aim to further our research to identify the boundaries and neural mechanisms of loss aversion. Towards this goal, we developed behavioral assays that test if and when do the worms exhibit loss aversion. In each assay, we grow the worms in different food environments and examine to what extent to they demonstrate loss aversion when moving them to a new environment. We then try to eliminate or increase loss aversion by manipulating the function of specific receptors.
The effect of low-level visual and auditory features on valuation
In recent years the use of video context for marketing, education, and other domains has become more prominent. Hence, the budget for online advertising has increased the need to investigate what attributes and features makes a video successful. In this study, we examine if low level visual and auditory features, extracted from video commercials for consumer products, are related to the overall ranking of those commercials. Subjects watch various video commercials, and they state their willingness to pay for each of the products appearing in the commercial and indicate how much they like each of the commercials overall. We use various algorithms to extract the low-level visual and auditory features of the video commercials. We then use machine learning methods to predict the success or liking of a commercial using its low visual features. This study could help us identify the most influential visual features in a commercial that make it successful, which can later be used to guide marketers and creative teams during the development process of novel commercials.
Links
Coller: https://en-coller.tau.ac.il/profile/dinolevy
Sagol: https://www.sagol.tau.ac.il/faculty/levy-dino/
IISDM: https://isdm.nyu.edu/people/dino-levy/
Press:
https://www.geektime.co.il/private_channel/xtra-mind-joy-ventures/
http://www.haaretz.co.il/news/health/.premium-1.2281884
http://www.globes.co.il/news/article.aspx?did=1000889506
http://recanati-bs.tau.ac.il/_Uploads/dbsAttachedFiles/M-2_13.pdf
https://medicalxpress.com/news/2019-05-inconsistent-choice-making-brain-options.html
https://www.themarker.com/magazine/.premium-1.7622332
TV & Video:
https://www.youtube.com/watch?v=rTFT0gYQduA
https://www.youtube.com/watch?v=-vQ52yFpMKk
https://www.youtube.com/watch?v=h5Cr36qC3QQ
https://www.youtube.com/watch?v=HGctOsy2f1U
Radio and pod cast:
https://podtail.se/podcast/--1416/--03-2019/
http://www.upnextpodcast.com/episode-184/
https://www.youtube.com/watch?v=8MoXcrBkLyo
Contact Us
Where
Coller School of Management
Tel Aviv University, Haim Levanon 55, Ramat Aviv, Tel Aviv, 69978, Israel
neuroeconlab@tauex.tau.ac.il (lab's e-mail)
Phone: 972-3-6409565
Room: 2067, Lorey Lokey building
Phone: 972-73-2425094
Room: 31 (Behavioral Lab)