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- Faculty Publications (18)
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- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
Keywords: AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- 2011
- Chapter
Prospective Codes Fufilled: A Potential Neural Mechanism of Will
By: Thalia Wheatley and Christine E. Looser
One of my few shortcomings is that I can’t predict the future.
Lars Ulrich, Metallica.
Lars Ulrich was right and wrong. He was right in the way we most often think about the future—as a long stretch of time during which multiply... View Details
Lars Ulrich, Metallica.
Lars Ulrich was right and wrong. He was right in the way we most often think about the future—as a long stretch of time during which multiply... View Details
Keywords: Free Will; Neuroscience; Responsibility; Prospection; Forecasting and Prediction; Science; Cognition and Thinking
Wheatley, Thalia, and Christine E. Looser. "Prospective Codes Fufilled: A Potential Neural Mechanism of Will." Chap. 13 in Conscious Will and Responsibility: A Tribute to Benjamin Libet, edited by Walter Sinnott-Armstrong and Lynn Nadel, 146–158. New York: Oxford University Press, 2011.
- 2009
- Case
The Prediction Lover's Handbook
By: Thomas H. Davenport and Jeanne G. Harris
When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options--some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction... View Details
Davenport, Thomas H., and Jeanne G. Harris. "The Prediction Lover's Handbook." 2009.
- Article
Behavioral and Neural Representations en route to Intuitive Action Understanding
By: Leyla Tarhan, Julian De Freitas and Talia Konkle
When we observe another person’s actions, we process many kinds of information—from how their body moves to the intention behind their movements. What kinds of information underlie our intuitive understanding about how similar actions are to each other? To address this... View Details
Keywords: Action Perception; Intuitive Similarity; Multi-arrangement; fMRI; Representational Similarity Analysis; Behavior; Perception
Tarhan, Leyla, Julian De Freitas, and Talia Konkle. "Behavioral and Neural Representations en route to Intuitive Action Understanding." Neuropsychologia 163 (December 2021).
- Article
Consumer Neuroscience: Advances in Understanding Consumer Psychology
By: Uma R. Karmarkar and Carolyn Yoon
While the study of consumer behavior has been enriched by improved abilities to generate new insights, many of the mechanisms underlying judgments and decision making remain difficult to investigate. In this review, we highlight some of the ways in which our... View Details
Keywords: Consumer Neuroscience; Neuroscience; Neuroeconomics; Consumer Psychology; Customer Behavior; Predictive Analytics; Neural Prediction; Neuroimaging; fMRI; Eye-tracking; Consumer Behavior; Marketing
Karmarkar, Uma R., and Carolyn Yoon. "Consumer Neuroscience: Advances in Understanding Consumer Psychology." Current Opinion in Psychology 10 (August 2016): 160–165.
- July 2019
- Article
'Forward Flow': A New Measure to Quantify Free Thought and Predict Creativity
By: Kurt Gray, Stephen Anderson, Eric Evan Chen, John Michael Kelly, Michael S. Christian, John Patrick, Laura Huang, Yoed N. Kenett and Kevin Lewis
When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people’s streams of free thought—free association—its utility for scientific research is undermined... View Details
Gray, Kurt, Stephen Anderson, Eric Evan Chen, John Michael Kelly, Michael S. Christian, John Patrick, Laura Huang, Yoed N. Kenett, and Kevin Lewis. "'Forward Flow': A New Measure to Quantify Free Thought and Predict Creativity." American Psychologist 74, no. 5 (July 2019): 539–554.
- June 2017
- Article
When Novel Rituals Lead to Intergroup Bias: Evidence from Economic Games and Neurophysiology
By: Nicholas M. Hobson, Francesca Gino, Michael I. Norton and Michael Inzlicht
Long-established rituals in pre-existing cultural groups have been linked to the cultural evolution of large-scale group cooperation. Here we test the prediction that novel rituals—arbitrary hand and body gestures enacted in a stereotypical and repeated fashion—can... View Details
Keywords: Ritual; Intergroup Dynamics; Intergroup Bias; Neural Reward Processing; Open Data; Open Materials; Preregistered; Groups and Teams; Behavior; Prejudice and Bias; Cooperation
Hobson, Nicholas M., Francesca Gino, Michael I. Norton, and Michael Inzlicht. "When Novel Rituals Lead to Intergroup Bias: Evidence from Economic Games and Neurophysiology." Psychological Science 28, no. 6 (June 2017): 733–750.
- 2014
- Chapter
Appetite, Consumption, and Choice in the Human Brain
By: Brian Knutson and Uma R. Karmarkar
Although linked, researchers have long distinguished appetitive from consummatory phases of reward processing. Recent improvements in the spatial and temporal resolution of neuroimaging techniques have allowed researchers to separately visualize different stages of... View Details
Knutson, Brian, and Uma R. Karmarkar. "Appetite, Consumption, and Choice in the Human Brain." Chap. 9 in The Interdisciplinary Science of Consumption, edited by Stephanie D. Preston, Morten L. Kringelbach, and Brian Knutson, 163–184. Cambridge, MA: MIT Press, 2014.
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- July 2017
- Article
The Impact of 'Display-Set' Options on Decision-Making
By: Uma R. Karmarkar
The way a choice set is constructed can have a significant influence on how individuals perceive and evaluate their options and make decisions between them. Here, I examine whether a “display set” of visible but unavailable options can exert these same types of... View Details
Keywords: Decision Making Process; Heuristics; Similarity; Categorization; Marketing Insight; Marketing; Choice; Choice Architecture; Choice Sets; Display; Retail; Consumer Behavior; Decision Choices and Conditions; Decisions; Decision Making; Retail Industry; Consumer Products Industry
Karmarkar, Uma R. "The Impact of 'Display-Set' Options on Decision-Making." Journal of Behavioral Decision Making 30, no. 3 (July 2017): 744–753.
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- Research Summary
Overview
Over the last decade, technology companies like Amazon, Google, and Netflix have pioneered data-driven research and development processes centered on massive experimentation. However, as companies increase the breadth and scale of their experiments to millions of... View Details
- Research Summary
Overview
By: Iavor I. Bojinov
Over the last decade, technology companies like Amazon, Google, and Netflix have pioneered data-driven research and development processes centered on massive experimentation. However, as companies increase the breadth and scale of their experiments to millions of... View Details
- 24 May 2018
- Research & Ideas
Distance Still Matters in Business, Despite the Internet
industries, then translate that into locations. A few technologies on the horizon appear to have the capacity to restructure specific industries. Among them are the deployment of new uses for AI (artificial intelligence), such as neural... View Details
Keywords: by Sean Silverthorne; Transportation; Telecommunications; Shipping; Publishing; Technology
- 23 Oct 2012
- First Look
First Look: October 23
model, poor people give little because they expect donations to come mainly from richer individuals. In others, donations by poor individuals constitute a large fraction of donations, and this raises the incentive for poor people to donate. The model View Details
Keywords: Sean Silverthorne
- 26 Mar 2012
- Research & Ideas
What Neuroscience Tells Us About Consumer Desire
those responses were not able to predict sales," Karmarker's note states, illustrating the marketing value of subconscious cerebral data. Neuromarketing can provide important but complex data to companies that target a global... View Details
- 05 Nov 2014
- What Do You Think?
Are We Entering an Era of Neuromanagement?
businesses." The predominant view was that the practice at present has many limitations. Ann Romaine-Adelstein commented, "I doubt we can predict with validity from a scan yet who will work hard, be innovative or exercise great... View Details
Keywords: by James Heskett
- 28 Aug 2018
- First Look
New Research and Ideas, August 28, 2018
although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large... View Details
Keywords: Dina Gerdeman
- 02 Sep 2014
- First Look
First Look: September 2
consumption. These findings imply that distinguishing appetite from consumption may improve predictions of future choice and illuminate neural components that support the process of decision making.... View Details
Keywords: Sean Silverthorne