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Conduct, disseminate and translate research on how new technologies can help us expand our cognitive abilities and augment – rather than replace – human performance and intelligence.

Mission

About

In the last decade alone, humans have developed countless technologies that helped us overcome our physical limitations. We have built vehicles that allow us to travel at unprecedented speeds, ventured into the depths of the oceans or the helm of space, and overcame numerous biological limitations (extending life, replacing organs, recovering from debilitating conditions, overturning conditions like deafness, blindness, or paralyses). Further, we are making progress to penetrate margins of our psyche that were inaccessible before (i.e., decoding dreams and thoughts or communicating with individuals under vegetative states).

All of those advances were enabled by new technologies.

At the Center for Advanced Technology and Human Performance, we strive to uncover new, sustainable ways by which technology can help us overcome our cognitive limitations and elevate our species to previously unthinkable levels. We do this investigation while exploring the guardrails that these advances would necessitate. In pursuing this mission, the center functions as a hub that brings together world-leading experts from academia, industry, think tanks, governments, and the media.

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News

Prof. Cerf's article in the "Bulletin of the Atomic Scientists", "If you worry about humanity, your should be more scared of humans than of AI", is out

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Prof. Matz' book, "The Psychology of Technology", is published.

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"Bioadapted", a play inspired by Prof. Cerf's work, receives NY Times reviews.

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Projects

Research Questions:

Individuals

How can we use technology (e.g. AI, virtual reality, or wearable devices) to understand and improve an individual’s performance across a variety of domains (e.g., decision-making, creativity, productivity and persistence, well-being or sleep).

Teams

How can we use technology (e.g., virtual reality, neurofeedback, or meta-data from conference calls) to facilitate coordination and shared reality among increasingly diverse teams and hybrid work environments?

Businesses

How can we use technology (e.g., AI and Big Data or neuroimaging) to help businesses design more personalized, sustainable, and equitable products/services and processes for their customers and employees?

Society

What is the impact of technology on society at large? How do we leverage some of the tremendous opportunities is provides for solving some of the world’s most pressing challenges (e.g., climate change, nuclear threat, political polarization) and mitigate the unprecedented risks it poses for our freedom and collective sense of shared reality?

*** - Can we use predictive technologies and personalized interventions to help individuals accomplish goals that are difficult for the human brain to pursue (e.g., saving, exercising, eating more healthily)? - Can we use technology to help individuals monitor and enhance self-control and persist longer?  - Can we use Big Data to better understand the drivers of psychological well-being? - Can we use Virtual or Augmented Reality (VR and AR) technologies to help individuals optimize their capabilities and develop new skills? - Can we enhance the quality and speed of human decision-making by presenting information in more intuitive sensory modalities (e.g. tactile vibrations on the skin)? - Can we create non-invasive brain-machine interfaces that allow individuals to monitor and adjust their performance in real time?  - Can we use new technology to tap into an underexplored layer of human performance: Sleep? - Can we use wearable biometric devices (e.g., Whoop, Fitbit or other smart watches/bracelets) to predict and modulate people’s cognitive and mental states (e.g., their level of alertness, propensity to focus, stress levels)? Can we use large scale behavioral data in combination with machine learning to predict performance challenges and churn?

*** - Can we use Virtual or Augmented Reality (VR and AR) technologies to facilitate coordination and shared reality in remote/hybrid work environments? - Can we use neuroscience and dynamic neurofeedback to understand and foster cooperation among groups of people? - Can we use ubiquitous metadata from video calls to predict the quality of work meetings and help leaders become more effective in managing them? - Can we use the world of online gaming – with access to highly granular microlevel process data – to teach us about coordination and the drivers of collective performance?

*** - Can we leverage the vast amounts of digital footprints generated in our interactions with technology (e.g., browsing histories, social media, email, sensor data from wearable devices) to better understand and cater to the needs and preferences of customers and employees? - Can we leverage the advances in generative AI to optimize the performance of their products and services (both the quality and scale)? - Can we use neuroimaging techniques to replace traditional focus groups with more reliable and trustworthy metrics of consumer engagement and preferences? - Can we use Big Data and AI to better understand how the decision to diversify the workforce and hire people from underrepresented groups into leadership positions impacts an organization’s culture and performance?

*** - Can we use technology to safeguard society in light of existential threats such as nuclear power? - Can we use technology to speed up innovation in domains of collective interest such as climate change or disease control? - How should governments think about regulating new technologies such as AI or neural implants to strike a balance between innovation and individual/collective freedom? - How can new technologies (such as federated learning) be used to help consumers reap the personalization and convenience benefits that come with the use of personal data while maintaining sufficiently high levels of privacy and self-determination?

Publications

Peer-reviewed:

  • Cerf, M. (2023). “Dream Marketing: A Method for Marketing Communication During Sleep and Dreams” SSRN

  • Cerf, M. (2023). “How many times do you need to view content before it is registered in your memory” Advances in Consumer Research
     

  • Matz, S.C., Teeny, J., Vaid, S. S., Harari, G. M., & Cerf, M. (2023). The Potential of Generative AI for Personalized Persuasion at Scale. Psycharxiv

  • Cerf, M. and Moutinho, L. (2023). “Biometrics and Neuroscience Research in Business and Management” De Gruyter

  • Wang, G. & Cerf, M. (2022). “Brain-Computer Interface using neural network and temporal-spectral features” Frontiers in Neuroinformatics
     

  • Wang, G., & Cerf, M. (2022). Brain-Computer Interface using neural network and temporal-spectral features. Frontiers in Neuroinformatics, 16, 952474

  • ​​Cerf, M., Thiruvengadam, N., Mormann, F., Kraskov, A., Quiroga, R. Q., Koch, C., & Fried, I. (2010). On-line, voluntary control of human temporal lobe neurons. Nature, 467(7319), 1104-1108

  • Matz, S. C., Beck, E., Atherton, O., White, M., Kim, M., Rauthmann, J., Mrozcek, D. & Bogg, T. (2023). The Promise of Personality Science in the Digital Age: How Psychological Targeting Can Be Used to Personalize Behavior Change Interventions at Scale. Perspectives on Psychological Science

  • Freiberg, B., & Matz, S. C. (2023). Founder personality and entrepreneurial success: A large-scale field study of technology startups. Proceedings of the National Academy of Science

  • Peters, H., Matz, S. C. & Cerf, M. (2023). Sensory Substitution Can Improve Decision-Making. Computers in Human Behavior

  • Cerf, M., Matz, S. C. & MacIver, M. A. (2023) Participating in a climate futures market increases support for costly climate policies. Nature Climate Change, 13, 511–512

  • Cerf, M., Matz, S. C. & MacIver M. A. (2023). Participating in a climate prediction market can increase concern about global warming. Nature Climate Change, 13, 523-531

  • Matz, S. C., Bukow, C. S., Peter, H., Dinu, A., Deacons, C. & Stachl, C. (2023). Throwing the cap or throwing in the towel? Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports

  • Matz, S. C., Gladstone, J. J. & Farrokhnia, R. A. (2023). Leveraging Psychological Fit to Encourage Saving Behavior. American Psychologist

  • Matz, S.C., Hyon, R., Baek, E.C., Parkinson, C., & Cerf. M. (2022). Personality similarity predicts synchronous neural responses in fMRI and EEG data. Scientific Reports, 12, 14325

  • Lawson, M. A. Martin, A. E., Huda, I. & Matz, S. C. (2022). Hiring women into senior leadership positions is associated with a reduction in gender stereotypes in organizational language. Proceedings of the National Academy of Science

  • Ramon, Y., Matz, S. C., Farrokhnia, R. A. & Martens, D. (2022). Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data. Information

  • Stachl, C., Boyd, R. L., Horstman, K.T, Khambatta, P., Matz, S. C., & Harari, G. M. (2021). Computational personality assessment. Personality Science, 2, 1-22

  • Freiberg, B. & Cerf, M. (2021). “Single neuron evidence of inattentional blindness in humans” Neuropsychologia

  • Müller, S. R, Chen, X, Peters, H, Chaintreau, A., & Matz, S. C. (2021). Depression predictions from GPS based mobility do not generalize well to large, demographically heterogeneous samples. Scientific Reports

  • Müller, S., Peters, H., Matz, S. C., Wang, W. & Harari, G. (2020). Everyday Mobility Behaviors Predict Psychological WellBeing Among Young Adults. European Journal of Personality

  • Massaro, S., Drover, W., Hmieleski, K. & Cerf, M. (2020). “Using functional neuroimaging to advance entrepreneurial cognitive research” Journal of Small Business Management

  • Massaro, S., Drover, W., & Cerf, M. (2020). “Founder gender and investor pitch assessments: an fMRI multivariate pattern analysis investigation” Academy of Management (AOM)

  • Cerf, M., Matz, S. C., & Berg, A. (2020). Using Blockchain to Improve Decision Making that Benefits the Public Good. Frontiers in Blockchain

  • Cowgill, B., Dell’Acqua, F., & Matz, S. C. (2020). Algorithmic Fairness Rethoric. American Economic Association Papers and Proceedings

  • Matz, S. C., Appel, R., & Kosinski, M. (2019). Privacy in the Age of Psychological Targeting. Current Opinion in Psychology

  • Hammer, R., & Cerf, M. (2019). “Risk Assessment Under Perceptual Ambiguity and its Impact on Category Learning and Decision-Making” PsyArXiv

  • Levy, J., Markell, D., Cerf, M. (2019). “Polar Similars: using massive mobile dating data to predict synchronization and alignment in dating preferences” Frontiers in Psychology: Personality and Social Psychology

  • Shane, S., Drover, W., Clingingsmith, D., & Cerf, M. (2019). “Founder passion, neural engagement and informal investor interest in startup pitches: an fMRI study” Journal of Business Venturing

  • Sokol, L., Young, M., Paparian, J., Kluger, B., Lum, H., Besbris, J., Kramer, N., Lang, A., Espay, A., Dubaz, O., Miyasaki, J., Matlock, D., Simuni, T., Cerf, M. (2019). “Advance Care Planning in Parkinson’s disease: Ethical Challenges and Future Directions” Nature Parkinson’s Disease

  • Herrero, J., Khuvis, S., Yeagle, E., Cerf, M., & Mehta, A. (2018). “Breathing above the Brainstem: Volitional Control and Attentional Modulation in humans” Journal of Neurophysiology

  • Barnett, S., Rose, C., Robinson, A., Campero, A., Zilberman, R., & Cerf, M. (2018). “Trust the polls? Neural and recall responses provide alternative predictors of political outcomes” Advances in Consumer Research

  • Barnett, S., & Cerf, M. (2017). “A Ticket for Your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers” Journal of Consumer Research

  • Drover, W., Massaro, S., Cerf, M., & Busenitz, L. (2017). “Neuro-Entrepreneurship” Academy of Management

  • Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. (2017). Psychological Targeting as an Effective Approach to Digital Mass Communication. Proceedings of the National Academy of Science

  • Matz, S. C., & Netzer, O. (2017). Using Big Data as a Window Into Consumer Psychology. Current Opinion in Behavioral Science, 18, 7-12

  • Behrendt, P., Matz, S. C., & Goeritz, A. (2017). An integrative model of leadership behaviour. Leadership Quarterly, 28(1), 229-244

  • Mentovich, A., Huq, A., & Cerf, M. (2015). “The psychology of corporate rights” Journal of Law and Human Behavior

  • Hoffman, G., Cerf, M. (2015). “The dark sides of robot social awareness” IEEE CIS Newsletter of the Autonomous Mental Development Technical Committee

  • Barnett, S., & Cerf, M. (2015). “Connecting on movie night? neural measures of engagement differ by gender” Advances in Consumer Research

  • Cerf, M., Greenleaf, E., Meyvis, T., & Morwitz, V. (2014). “Using Single-Neuron Recording in Marketing: Opportunities, Challenges, and an Application to Fear Enhancement in Communication” Journal of Marketing Research

  • Cerf, M., MacKay, M., & Koch, C (2012). “Evidence for two distinct mechanisms directing gaze in natural scenes” Journal of Vision

  • Cerf, M., Frady, P, & Koch, C (2009). “Faces and text attract gaze independent of the task: Experimental data and computer model” Journal of Vision

  • Einhäuser, W., Schumann, F., Vockeroth, J., Bartl, K., Cerf, M., Harel, J., Schneider E., & König, P. (2008). “Distinct roles for eye and head movements in selecting salient image parts during natural exploration” Annals. of the New York Academy of Sciences

  • Cerf, M., Harel, J., Huth, A., & Koch, C. (2008). “Decoding what people see from where they look: Predicting visual stimuli from scanpaths” Lecture Notes in Artificial Intelligence (LNAI)

  • Cerf, M., Harel, J., Einhäuser, W., & Koch, C. (2007). “Predicting human gaze using low-level saliency combined with face detection” Advances in Neural Information Processing Systems (NIPS)

Books:

  • Matz, S. C. (2022). The Psychology of Technology: Social Science Research in the Age of Big Data. APA

  • Cerf, M. (2023). “Brain Imaging: An Illustrated Guide to the Future of Neuroscience” Lulu Press

  • Moutinho, L., & Cerf, M. (2023). “Biometrics and Neuroscience Research in Business and Management” de Gruyter

  • Cerf, M., & Garcia M. (2017). “Consumer Neuroscience” MIT Press

  • Cerf, M., & Wolcott, R. (2017). “Foresight” Northwestern University Press

  • Fried, I., Cerf, M., & Kreiman, G. (2014). “Single neuron studies of the human brain” MIT Press

  • Cerf, M. (2009). “Competition and Attention in the human brain” Lambert Press

Book chapters:

  • Matz., S.C., Appel, R. E., & Croll, B. (2022). Privacy and Ethics in the Age of Big Data. In S. C. Matz, The Psychology of Technology. APA

  • Cerf, M., & Matz., S.C. (2022). The Psychology of Technology: Where the Future Might Take Us. In S. C. Matz, The Psychology of Technology. APA

  • Cerf, M. (2023). “Using Neuroscience and Biometrics in Individuals and Organizations” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Cerf, M., & Brendl, M. (2023). “Using Sensory Substitutions to Make Better Business Decisions (or How Sensory Devices Connected to Our Body Can Help Us Outperform AI and Common Data Analytics)” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Granoviter, O., Cerf, M., & Hanein, Y. (2023). “Leaked Expressions Captured with Wearable High-Resolution Facial Electromyography” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Cerf, M. (2023). “The Human Affair with Data, the Challenges It Creates, Ways to Solve These Challenges, and Future Outlook” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Cerf, M. (2023). “Using Biometrics in Healthcare Management and Diagnostics” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Moutinho, L., & Cerf, M. (2023). “The Future of Neuroscience and Biometrics in Business” Biometrics and Neuroscience Research in Business and Management, Editors: Luis Moutinho, Moran Cerf, Publisher: de Gruyter

  • Cerf, M. (2019). “Using neuroscience to assess brands” Branding in a Hyper-connected world, Editors: Alice Tybout, Tim Calkins, Publisher: Wiley

  • Mentovich, A., & Cerf, M. (2014). “A psychological perspective on punishing corporate entities” Regulating Corporate Criminal Liability, Editors: Dominik Brodowski, Manuel Espinoza, Publisher: Elsevier

  • Cerf, M., Gelbard-Sagiv, H., & Fried, I. (2013). “Studying thoughts and deliberations using single-neuron recordings in humans” Single neuron studies of the human brain, Editors: Itzhak Fried, Moran Cerf, Ueli Rutishauser, Gabriel Kreiman, Publisher: MIT Press

  • Rutishauser, U., Cerf, M., & Kreiman, G. (2013). “Data analysis techniques for human microwire recordings: spike detection and sorting, decoding, relation between units and local field potentials” Single neuron studies of the human brain, Editors: Itzhak Fried, Moran Cerf, Ueli Rutishauser, Gabriel Kreiman, Publisher: MIT Press

  • Cerf, M., & Mackay, M. (2011). “Studying consciousness using direct recording from single neurons in the human brain” Research and Perspective in Neuroscience, Editors Stanislas Dehaene and Yves Christien. Publisher: Springer

  • Cerf, M. (2011). “Projecting thoughts to an external display using single-neuron recordings in the human brain” Seeing with Eyes closed, Editors: Ivana Franke and Ida Momennejad. Association of Neuroesthetics

Patents:

  • Barnett, S., & Cerf, M. (2015). “Method for measuring engagement” U.S. Patent no. US20150206174A1

  • Shachar, J., Chen, T., Farkas, L., Wu, W., Zimmerman, K., Cerf, M., Marx, B., Johnson, D., & Farkas, L. (2012) “Brain retractor apparatus for measuring and predicting electrophysiological parameters” United States Patent

  • Shachar, J., Chen, T., Farkas, L., Wu, W., Zimmerman, K., Cerf, M., Marx, B., Johnson, D., & Farkas, L. (2010). “Magnetic breather pump for delivery of Chemotherapeutic agents into the brain” U.S. Patent no. US20100160737A1

  • Cerf, M., & Koch, C. (2008). “Automatic prediction of human gaze in visuals by localizing high-level elements” U.S. Provisional Patent application no. CIT-5033-P

  • Hall, A. L., Kenton, S., Van Harken, J., & Cerf , M., “Applying biometrics to the development of digital content” United States Patent

Cases: 

  • Cerf, M. (2015). “Tivo: Segmentation Analytics” Kellogg School of Management, Northwestern University

Clinical Trials:

  • Cerf, M. (2023). “Evaluation of Accuracy and Consistency of the X-Trodes System”, Identifier: NCT05722639

Cerf, M. (2023). “Dream Marketing: A Method for Marketing Communication During Sleep and Dreams” SSRN ​ Cerf, M. (2023). “How many times do you need to view content before it is registered in your memory” Advances in Consumer Research Matz, S.C., Teeny, J., Vaid, S. S., Harari, G. M., & Cerf, M. (2023). The Potential of Generative AI for Personalized Persuasion at Scale. Psycharxiv ​​ Cerf, M. and Moutinho, L. (2023). “Biometrics and Neuroscience Research in Business and Management” De Gruyter ​ Wang, G. & Cerf, M. (2022). “Brain-Computer Interface using neural network and temporal-spectral features” Frontiers in Neuroinformatics Wang, G., & Cerf, M. (2022). Brain-Computer Interface using neural network and temporal-spectral features. Frontiers in Neuroinformatics, 16, 952474 ​​ ​​Cerf, M., Thiruvengadam, N., Mormann, F., Kraskov, A., Quiroga, R. Q., Koch, C., & Fried, I. (2010). On-line, voluntary control of human temporal lobe neurons. Nature, 467(7319), 1104-1108 ​ Matz, S. C., Beck, E., Atherton, O., White, M., Kim, M., Rauthmann, J., Mrozcek, D. & Bogg, T. (2023). The Promise of Personality Science in the Digital Age: How Psychological Targeting Can Be Used to Personalize Behavior Change Interventions at Scale. Perspectives on Psychological Science ​ Freiberg, B., & Matz, S. C. (2023). Founder personality and entrepreneurial success: A large-scale field study of technology startups. Proceedings of the National Academy of Science ​​ Peters, H., Matz, S. C. & Cerf, M. (2023). Sensory Substitution Can Improve Decision-Making. Computers in Human Behavior ​ Cerf, M., Matz, S. C. & MacIver, M. A. (2023) Participating in a climate futures market increases support for costly climate policies. Nature Climate Change, 13, 511–512 ​ Cerf, M., Matz, S. C. & MacIver M. A. (2023). Participating in a climate prediction market can increase concern about global warming. Nature Climate Change, 13, 523-531 ​ Matz, S. C., Bukow, C. S., Peter, H., Dinu, A., Deacons, C. & Stachl, C. (2023). Throwing the cap or throwing in the towel? Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports ​ Matz, S. C., Gladstone, J. J. & Farrokhnia, R. A. (2023). Leveraging Psychological Fit to Encourage Saving Behavior. American Psychologist ​ Matz, S.C., Hyon, R., Baek, E.C., Parkinson, C., & Cerf. M. (2022). Personality similarity predicts synchronous neural responses in fMRI and EEG data. Scientific Reports, 12, 14325 ​ Lawson, M. A. Martin, A. E., Huda, I. & Matz, S. C. (2022). Hiring women into senior leadership positions is associated with a reduction in gender stereotypes in organizational language. Proceedings of the National Academy of Science ​ Ramon, Y., Matz, S. C., Farrokhnia, R. A. & Martens, D. (2022). Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data. Information ​ Stachl, C., Boyd, R. L., Horstman, K.T, Khambatta, P., Matz, S. C., & Harari, G. M. (2021). Computational personality assessment. Personality Science, 2, 1-22 ​ Freiberg, B. & Cerf, M. (2021). “Single neuron evidence of inattentional blindness in humans” Neuropsychologia ​ Müller, S. R, Chen, X, Peters, H, Chaintreau, A., & Matz, S. C. (2021). Depression predictions from GPS based mobility do not generalize well to large, demographically heterogeneous samples. Scientific Reports ​ Müller, S., Peters, H., Matz, S. C., Wang, W. & Harari, G. (2020). Everyday Mobility Behaviors Predict Psychological WellBeing Among Young Adults. European Journal of Personality ​ Massaro, S., Drover, W., Hmieleski, K. & Cerf, M. (2020). “Using functional neuroimaging to advance entrepreneurial cognitive research” Journal of Small Business Management ​ Massaro, S., Drover, W., & Cerf, M. (2020). “Founder gender and investor pitch assessments: an fMRI multivariate pattern analysis investigation” Academy of Management (AOM) ​​ Cerf, M., Matz, S. C., & Berg, A. (2020). Using Blockchain to Improve Decision Making that Benefits the Public Good. Frontiers in Blockchain ​ Cowgill, B., Dell’Acqua, F., & Matz, S. C. (2020). Algorithmic Fairness Rethoric. American Economic Association Papers and Proceedings ​ Matz, S. C., Appel, R., & Kosinski, M. (2019). Privacy in the Age of Psychological Targeting. Current Opinion in Psychology ​ Hammer, R., & Cerf, M. (2019). “Risk Assessment Under Perceptual Ambiguity and its Impact on Category Learning and Decision-Making” PsyArXiv ​ Levy, J., Markell, D., Cerf, M. (2019). “Polar Similars: using massive mobile dating data to predict synchronization and alignment in dating preferences” Frontiers in Psychology: Personality and Social Psychology ​ Shane, S., Drover, W., Clingingsmith, D., & Cerf, M. (2019). “Founder passion, neural engagement and informal investor interest in startup pitches: an fMRI study” Journal of Business Venturing ​ Sokol, L., Young, M., Paparian, J., Kluger, B., Lum, H., Besbris, J., Kramer, N., Lang, A., Espay, A., Dubaz, O., Miyasaki, J., Matlock, D., Simuni, T., Cerf, M. (2019). “Advance Care Planning in Parkinson’s disease: Ethical Challenges and Future Directions” Nature Parkinson’s Disease ​ Herrero, J., Khuvis, S., Yeagle, E., Cerf, M., & Mehta, A. (2018). “Breathing above the Brainstem: Volitional Control and Attentional Modulation in humans” Journal of Neurophysiology ​ Barnett, S., Rose, C., Robinson, A., Campero, A., Zilberman, R., & Cerf, M. (2018). “Trust the polls? Neural and recall responses provide alternative predictors of political outcomes” Advances in Consumer Research ​ Barnett, S., & Cerf, M. (2017). “A Ticket for Your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers” Journal of Consumer Research ​ Drover, W., Massaro, S., Cerf, M., & Busenitz, L. (2017). “Neuro-Entrepreneurship” Academy of Management ​ Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. (2017). Psychological Targeting as an Effective Approach to Digital Mass Communication. Proceedings of the National Academy of Science ​ Matz, S. C., & Netzer, O. (2017). Using Big Data as a Window Into Consumer Psychology. Current Opinion in Behavioral Science, 18, 7-12 ​ Behrendt, P., Matz, S. C., & Goeritz, A. (2017). An integrative model of leadership behaviour. Leadership Quarterly, 28(1), 229-244 ​ Mentovich, A., Huq, A., & Cerf, M. (2015). “The psychology of corporate rights” Journal of Law and Human Behavior ​ Hoffman, G., Cerf, M. (2015). “The dark sides of robot social awareness” IEEE CIS Newsletter of the Autonomous Mental Development Technical Committee ​ Barnett, S., & Cerf, M. (2015). “Connecting on movie night? neural measures of engagement differ by gender” Advances in Consumer Research ​ Cerf, M., Greenleaf, E., Meyvis, T., & Morwitz, V. (2014). “Using Single-Neuron Recording in Marketing: Opportunities, Challenges, and an Application to Fear Enhancement in Communication” Journal of Marketing Research ​ Cerf, M., MacKay, M., & Koch, C (2012). “Evidence for two distinct mechanisms directing gaze in natural scenes” Journal of Vision ​ Cerf, M., Frady, P, & Koch, C (2009). “Faces and text attract gaze independent of the task: Experimental data and computer model” Journal of Vision ​ Einhäuser, W., Schumann, F., Vockeroth, J., Bartl, K., Cerf, M., Harel, J., Schneider E., & König, P. (2008). “Distinct roles for eye and head movements in selecting salient image parts during natural exploration” Annals. of the New York Academy of Sciences ​ Cerf, M., Harel, J., Huth, A., & Koch, C. (2008). “Decoding what people see from where they look: Predicting visual stimuli from scanpaths” Lecture Notes in Artificial Intelligence (LNAI) ​ Cerf, M., Harel, J., Einhäuser, W., & Koch, C. (2007). “Predicting human gaze using low-level saliency combined with face detection” Advances in Neural Information Processing Systems (NIPS)

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Curriculum

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Introduction to AI

Prof. Cerf teaches key principles of Artifical Intelligence (Machine Learning, Deep Learning, Natural Language Processing, and Transforms for Generative AI).

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