Lee Newman
I am a professor of Behavioral Science at IE University (Segovia) and IE Business School (Madrid). I am also serving as Dean of IE’s School of Social & Behavioral Science
Research. I am interested in the behavioral bases of human judgment and decision making with a focus on understanding biases and errors in decision making under risk and uncertainty. My recent work focused on individual differences in behavioral judgment and learning in a well-known gambling task. At the present time I pursuing work investigating behavioral biases in judgmental forecasting and behavioral methods that reduce confirmation bias through training.
Teaching. I have been actively involved in teaching from the outset of academic career. I have been fortunate to have had the opportunity to teach in a number of areas including management, cognitive psychology, clinical psychology, neuropsychology, and artificial intelligence. The core of my teaching focuses on helping students understand and address behavioral biases in judgement and decision making. I am currently leading an effort to develop Active Learning technologies that shift the learning paradigm from lectures and textbooks to “learning by experiencing”. One result of this initiative is the IExperiments Platform that enables students to test their judgment and decision making, with the data being immediately available to the professor for presentation in the classroom.
Experience. Prior to pursuing an academic career, I was a founder and senior manager in two technology-based startups in New York City (Brainstorm Interactive, and HR One), and I served as a management consultant with McKinsey & Company in Chicago.
Education. I completed interdiscplinary doctoral studies at the University of Michigan under the guidance of Dr. Thad Polk. My doctoral work combined Psychology (Cognition & Cognitive Neuroscience) and Computer Science (Intelligent Systems). I also hold Masters degrees in Management (M.I.T. Sloan) and in Technology Policy (M.I.T. TPP) and a Bachelors degree in Electrical Engineering (Brown University).
Supervisors: Thad Polk
Research. I am interested in the behavioral bases of human judgment and decision making with a focus on understanding biases and errors in decision making under risk and uncertainty. My recent work focused on individual differences in behavioral judgment and learning in a well-known gambling task. At the present time I pursuing work investigating behavioral biases in judgmental forecasting and behavioral methods that reduce confirmation bias through training.
Teaching. I have been actively involved in teaching from the outset of academic career. I have been fortunate to have had the opportunity to teach in a number of areas including management, cognitive psychology, clinical psychology, neuropsychology, and artificial intelligence. The core of my teaching focuses on helping students understand and address behavioral biases in judgement and decision making. I am currently leading an effort to develop Active Learning technologies that shift the learning paradigm from lectures and textbooks to “learning by experiencing”. One result of this initiative is the IExperiments Platform that enables students to test their judgment and decision making, with the data being immediately available to the professor for presentation in the classroom.
Experience. Prior to pursuing an academic career, I was a founder and senior manager in two technology-based startups in New York City (Brainstorm Interactive, and HR One), and I served as a management consultant with McKinsey & Company in Chicago.
Education. I completed interdiscplinary doctoral studies at the University of Michigan under the guidance of Dr. Thad Polk. My doctoral work combined Psychology (Cognition & Cognitive Neuroscience) and Computer Science (Intelligent Systems). I also hold Masters degrees in Management (M.I.T. Sloan) and in Technology Policy (M.I.T. TPP) and a Bachelors degree in Electrical Engineering (Brown University).
Supervisors: Thad Polk
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monozygotic twins than in dizygotic twins, but this is not true for patterns evoked by other visual stimuli such as words and chairs. By what mechanism could genetics come to influence the cortical development of face processing more than the processing of other visual stimuli? We hypothesize that an innate attentional preference to look at faces in early development interacts with initial functional connectivity and principles of cortical self-organization to lead to observed
neural and behavioral effects. We present a neural network model that makes explicit how this hypothesis might work. In particular, we demonstrate that manipulating the similarity of initial patterns of connectivity in simulated cortex has a greater effect on the similarity of activity patterns evoked by faces than by other visual stimuli. The model therefore provides a computationally explicit account of the observed effects of zygosity on neural activation patterns in human twins and, more generally, explains how genetics could influence face processing preferentially.
neural network model that demonstrates the feasibility of this common contexts hypothesis and present two experiments confirming some novel predictions: (a) repeatedly presenting arbitrary visual stimuli in common contexts leads those stimuli to be confusable with each other, and (b) different forms of the same letter are more confusable with each other in word-like contexts than in nonword-like contexts. We then extend the model to use real pictures of letters as input and simulate some of the novel empirical findings from the experiments.
monozygotic twins than in dizygotic twins, but this is not true for patterns evoked by other visual stimuli such as words and chairs. By what mechanism could genetics come to influence the cortical development of face processing more than the processing of other visual stimuli? We hypothesize that an innate attentional preference to look at faces in early development interacts with initial functional connectivity and principles of cortical self-organization to lead to observed
neural and behavioral effects. We present a neural network model that makes explicit how this hypothesis might work. In particular, we demonstrate that manipulating the similarity of initial patterns of connectivity in simulated cortex has a greater effect on the similarity of activity patterns evoked by faces than by other visual stimuli. The model therefore provides a computationally explicit account of the observed effects of zygosity on neural activation patterns in human twins and, more generally, explains how genetics could influence face processing preferentially.
neural network model that demonstrates the feasibility of this common contexts hypothesis and present two experiments confirming some novel predictions: (a) repeatedly presenting arbitrary visual stimuli in common contexts leads those stimuli to be confusable with each other, and (b) different forms of the same letter are more confusable with each other in word-like contexts than in nonword-like contexts. We then extend the model to use real pictures of letters as input and simulate some of the novel empirical findings from the experiments.