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student.
TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
17. LINEAR MIXED EFFECT MODELING Note. This chapter written by Todd Gureckis who adapted it from Gabriela K Hajduk and the University of Edinburgh coding club tutorial on Mixed Effect Linear Models and the mixed models Kaggle notebook by OJ Watson.The former is release under CC BY-SA 4.0, the latter under the Apache 2.0 license. The text is a mashup of these two resources with various editing to connect to the rest of the 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y ABOUT - TODD GURECKIS In recent years I have branched out from this core theme of active/self-directed learning to consider research on decision making, the cognitive neuroscience of memory, virtual reality, and how we reason and interact with the physical and social world. I am committed to help develop open source software to enable new types ofhigh-quality
VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
COMPUTATION + COGNITION LAB @ NYU: PEOPLE Pam is a fifth year graduate student in the Center for Neural Science studying human learning. Her interests are in combining behavior, neural imaging, and computational modeling to characterize and potentially improve learning strategies. Ethan Ludwin-Peery. Gradstudent.
TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
17. LINEAR MIXED EFFECT MODELING Note. This chapter written by Todd Gureckis who adapted it from Gabriela K Hajduk and the University of Edinburgh coding club tutorial on Mixed Effect Linear Models and the mixed models Kaggle notebook by OJ Watson.The former is release under CC BY-SA 4.0, the latter under the Apache 2.0 license. The text is a mashup of these two resources with various editing to connect to the rest of the 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y ABOUT - TODD GURECKIS In recent years I have branched out from this core theme of active/self-directed learning to consider research on decision making, the cognitive neuroscience of memory, virtual reality, and how we reason and interact with the physical and social world. I am committed to help develop open source software to enable new types ofhigh-quality
VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: RESEARCH computation + cognition lab @ nyu. The goal of our research is to better understand the memory, learning, and decision processes which allow humans to carry out intelligent and adaptive behaviors. The name of our lab is "computation and cognition" which reflects the two main strands that inform our work.VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
MENTAL IMAGERY, MENTAL SIMULATION, AND MENTAL ROTATION Mental simulation. A concept deeply realted to the concept of mental imagery is the idea of mental simulation. If mental imagery focuses on the content and representation of static sitautions, mental simulation focuses on how we run "movies" in our mind about how the world might unfold over the future. CAN LOSSES HELP ATTENUATE LEARNING TRAPS? Can losses help attenuate learning traps? Amy X. Li1 (amy.x.li@outlook.com), Todd Gureckis2 (todd.gureckis@nyu.edu), Brett K. Hayes1 (b.hayes@unsw.edu.au) 1School of Psychology, UNSW Sydney, Sydney, NSW 2052, Australia 2Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA Abstract Recent work has demonstrated robust learning traps duringMODELING FMRI PT2
To compute the appropriate p level using the Bonferroni correction we divide our desired false positive rate (e.g., .05) by the number of separate tests being conducted. In our fMRI data the number of tests is equal to the number of voxels since we are running one correlation for every voxel timeseries. Question 8. THE ATTENTIONAL LEARNING TRAP AND HOW TO AVOID IT The Attentional Learning Trap and How to Avoid It Alexander S. Rich (asr443@nyu.edu) Todd M. Gureckis (todd.gureckis@nyu.edu) New York University, Department of Psychology, 6 Washington Place, New York, NY10003 USA
SELF‐DIRECTED LEARNING FAVORS LOCAL, RATHER THAN GLOBAL canonical example of this type of decision making is a doctor deciding which diagnostic test to perform on a sick patient (e.g., either an MRI or a blood test). MENTAL ROTATION INTERMEDIATE ARTICLE Mental Rotation Intermediate article Yohtaro Takano, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan Matia Okubo, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan ONLINE EXPERIMENTS USING JSPSYCH, PSITURK, AND AMAZON Online Experiments using jsPsych, psiTurk, and Amazon Mechanical Turk Joshua R. de Leeuw (jodeleeu@indiana.edu) Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana Univeristy,TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: PEOPLE Pam is a fifth year graduate student in the Center for Neural Science studying human learning. Her interests are in combining behavior, neural imaging, and computational modeling to characterize and potentially improve learning strategies. Ethan Ludwin-Peery. Gradstudent.
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful ABOUT - TODD GURECKIS In recent years I have branched out from this core theme of active/self-directed learning to consider research on decision making, the cognitive neuroscience of memory, virtual reality, and how we reason and interact with the physical and social world. I am committed to help develop open source software to enable new types ofhigh-quality
VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
SIGNAL DETECTION THEORY (LAB) So, here goes. First let's make a sequence of n = 1000 trials in which 50% randomly are going to have “signal” and 50% are randomly going to have no signal. To do that we want to create an array called signal_present that is True 50% of the time and False 50% of the time. signal_present = np.random.rand(1000) > .5.MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: PEOPLE Pam is a fifth year graduate student in the Center for Neural Science studying human learning. Her interests are in combining behavior, neural imaging, and computational modeling to characterize and potentially improve learning strategies. Ethan Ludwin-Peery. Gradstudent.
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful ABOUT - TODD GURECKIS In recent years I have branched out from this core theme of active/self-directed learning to consider research on decision making, the cognitive neuroscience of memory, virtual reality, and how we reason and interact with the physical and social world. I am committed to help develop open source software to enable new types ofhigh-quality
VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
SIGNAL DETECTION THEORY (LAB) So, here goes. First let's make a sequence of n = 1000 trials in which 50% randomly are going to have “signal” and 50% are randomly going to have no signal. To do that we want to create an array called signal_present that is True 50% of the time and False 50% of the time. signal_present = np.random.rand(1000) > .5.MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: RESEARCH computation + cognition lab @ nyu. The goal of our research is to better understand the memory, learning, and decision processes which allow humans to carry out intelligent and adaptive behaviors. The name of our lab is "computation and cognition" which reflects the two main strands that inform our work.VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
OPTIMIZING MEMORY USING NEURAL IMAGING Optimizing Memory using Neural Information (OMNI) The goal of the OMNI project is to develop a computational framework for the prediction and optimization of human memory which is informed by indirect measurements of brain activity. Simply put, we aim to record people's brains while they learn things, predict which things they learned CAN LOSSES HELP ATTENUATE LEARNING TRAPS? Can losses help attenuate learning traps? Amy X. Li1 (amy.x.li@outlook.com), Todd Gureckis2 (todd.gureckis@nyu.edu), Brett K. Hayes1 (b.hayes@unsw.edu.au) 1School of Psychology, UNSW Sydney, Sydney, NSW 2052, Australia 2Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA Abstract Recent work has demonstrated robust learning traps during THE ATTENTIONAL LEARNING TRAP AND HOW TO AVOID IT The Attentional Learning Trap and How to Avoid It Alexander S. Rich (asr443@nyu.edu) Todd M. Gureckis (todd.gureckis@nyu.edu) New York University, Department of Psychology, 6 Washington Place, New York, NY10003 USA
MODELING FMRI PT2
To compute the appropriate p level using the Bonferroni correction we divide our desired false positive rate (e.g., .05) by the number of separate tests being conducted. In our fMRI data the number of tests is equal to the number of voxels since we are running one correlation for every voxel timeseries. Question 8. SELF‐DIRECTED LEARNING FAVORS LOCAL, RATHER THAN GLOBAL canonical example of this type of decision making is a doctor deciding which diagnostic test to perform on a sick patient (e.g., either an MRI or a blood test). MENTAL ROTATION INTERMEDIATE ARTICLE Mental Rotation Intermediate article Yohtaro Takano, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan Matia Okubo, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan ONLINE EXPERIMENTS USING JSPSYCH, PSITURK, AND AMAZON Online Experiments using jsPsych, psiTurk, and Amazon Mechanical Turk Joshua R. de Leeuw (jodeleeu@indiana.edu) Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana Univeristy,TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: PEOPLE Pam is a fifth year graduate student in the Center for Neural Science studying human learning. Her interests are in combining behavior, neural imaging, and computational modeling to characterize and potentially improve learning strategies. Ethan Ludwin-Peery. Gradstudent.
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y ABOUT - TODD GURECKIS I am a devoted and purposeful computational ployglot – research in my lab frequently combines methods such as neural networks, Bayesian methods, causal learning, reinforcement learning, program induction, and symbolic systems. In recent years I have branched out from this core theme to consider research on decision making, the cognitive 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be usefulVISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. SIGNAL DETECTION THEORY (LAB) So, here goes. First let's make a sequence of n = 1000 trials in which 50% randomly are going to have “signal” and 50% are randomly going to have no signal. To do that we want to create an array called signal_present that is True 50% of the time and False 50% of the time. signal_present = np.random.rand(1000) > .5. HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: PEOPLE Pam is a fifth year graduate student in the Center for Neural Science studying human learning. Her interests are in combining behavior, neural imaging, and computational modeling to characterize and potentially improve learning strategies. Ethan Ludwin-Peery. Gradstudent.
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y ABOUT - TODD GURECKIS I am a devoted and purposeful computational ployglot – research in my lab frequently combines methods such as neural networks, Bayesian methods, causal learning, reinforcement learning, program induction, and symbolic systems. In recent years I have branched out from this core theme to consider research on decision making, the cognitive 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be usefulVISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. SIGNAL DETECTION THEORY (LAB) So, here goes. First let's make a sequence of n = 1000 trials in which 50% randomly are going to have “signal” and 50% are randomly going to have no signal. To do that we want to create an array called signal_present that is True 50% of the time and False 50% of the time. signal_present = np.random.rand(1000) > .5. HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
COMPUTATION + COGNITION LAB @ NYU: RESEARCH computation + cognition lab @ nyu. The goal of our research is to better understand the memory, learning, and decision processes which allow humans to carry out intelligent and adaptive behaviors. The name of our lab is "computation and cognition" which reflects the two main strands that inform our work.VISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
COMPUTATIONAL COGNITIVE SCIENCE AT NEW YORK UNIVERSITY Computational cognitive science is the interdisciplinary study of intelligence that recognizes that intelligence is a property of many sufficiently complex systems. The key insight that unites this field of study is the idea that intelligence is best described in terms of computational systems (i.e., computer algorithms or programs). OPTIMIZING MEMORY USING NEURAL IMAGING Optimizing Memory using Neural Information (OMNI) The goal of the OMNI project is to develop a computational framework for the prediction and optimization of human memory which is informed by indirect measurements of brain activity. Simply put, we aim to record people's brains while they learn things, predict which things they learned CAN LOSSES HELP ATTENUATE LEARNING TRAPS? Can losses help attenuate learning traps? Amy X. Li1 (amy.x.li@outlook.com), Todd Gureckis2 (todd.gureckis@nyu.edu), Brett K. Hayes1 (b.hayes@unsw.edu.au) 1School of Psychology, UNSW Sydney, Sydney, NSW 2052, Australia 2Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA Abstract Recent work has demonstrated robust learning traps duringMODELING FMRI PT2
To compute the appropriate p level using the Bonferroni correction we divide our desired false positive rate (e.g., .05) by the number of separate tests being conducted. In our fMRI data the number of tests is equal to the number of voxels since we are running one correlation for every voxel timeseries. Question 8. THE ATTENTIONAL LEARNING TRAP AND HOW TO AVOID IT The Attentional Learning Trap and How to Avoid It Alexander S. Rich (asr443@nyu.edu) Todd M. Gureckis (todd.gureckis@nyu.edu) New York University, Department of Psychology, 6 Washington Place, New York, NY10003 USA
MENTAL ROTATION INTERMEDIATE ARTICLE Mental Rotation Intermediate article Yohtaro Takano, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan Matia Okubo, University of Tokyo, Hongo, Bunkyo-ku, Tokyo, Japan ONLINE EXPERIMENTS USING JSPSYCH, PSITURK, AND AMAZON Online Experiments using jsPsych, psiTurk, and Amazon Mechanical Turk Joshua R. de Leeuw (jodeleeu@indiana.edu) Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana Univeristy, COMPUTATION + COGNITION LAB @ NYU research overview teaching nyuconcats resources/code github lab blog psiturk onmi cogsci@nyu *new* how to cogsci*new*: publications by year by category: people todd gureckis (pi) - everyone - OPTIMIZING MEMORY USING NEURAL IMAGING There are seven categories of project releases: data, publications, posters, abstracts, pre-prints, modeling code, and blog posts/news.The following list shows all current and planned data releases (white buttons are planned and upcoming releases): 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful COMPUTATIONAL COGNITIVE SCIENCE AT NEW YORK UNIVERSITY Computational cognitive science is the interdisciplinary study of intelligence that recognizes that intelligence is a property of many sufficiently complex systems. The key insight that unites this field of study is the idea that intelligence is best described in terms of computational systems (i.e., computer algorithms or programs). ABOUT - TODD GURECKIS I am a devoted and purposeful computational ployglot – research in my lab frequently combines methods such as neural networks, Bayesian methods, causal learning, reinforcement learning, program induction, and symbolic systems. In recent years I have branched out from this core theme to consider research on decision making, the cognitiveVISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
COMPUTATION + COGNITION LAB @ NYU research overview teaching nyuconcats resources/code github lab blog psiturk onmi cogsci@nyu *new* how to cogsci*new*: publications by year by category: people todd gureckis (pi) - everyone - OPTIMIZING MEMORY USING NEURAL IMAGING There are seven categories of project releases: data, publications, posters, abstracts, pre-prints, modeling code, and blog posts/news.The following list shows all current and planned data releases (white buttons are planned and upcoming releases): 4. INTRO TO PYTHON FOR PSYCHOLOGY UNDERGRADS Note. This chapter authored by Todd M. Gureckis is released under the license for the book. The section on for loops was developed by Lisa Tagliaferri for digitalocean.com released under the Creative Commons Attribution-NonCommercial-ShakeAlike 4.0 International Licence. This document is targetted toward psych undergrads in our Lab in Cognition and Perception course at NYU but could be useful COMPUTATIONAL COGNITIVE SCIENCE AT NEW YORK UNIVERSITY Computational cognitive science is the interdisciplinary study of intelligence that recognizes that intelligence is a property of many sufficiently complex systems. The key insight that unites this field of study is the idea that intelligence is best described in terms of computational systems (i.e., computer algorithms or programs). ABOUT - TODD GURECKIS I am a devoted and purposeful computational ployglot – research in my lab frequently combines methods such as neural networks, Bayesian methods, causal learning, reinforcement learning, program induction, and symbolic systems. In recent years I have branched out from this core theme to consider research on decision making, the cognitiveVISUALIZING DATA
Visualizing Data. This chapter by Todd M. Gureckis. Some of the material is adapted from Danielle Navarro 's excellent Learning Statistics with R book, however there is a focus on plotting using python libraries including matplotlib and seaborn. Must of the seaborn example code is drawn from the outstanding official seaborn tutorialby Michael
A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y HOW YOU NAMED YOUR CHILD: UNDERSTANDING THE RELATIONSHIP How You Named Your Child: Understanding the Relationship Between Individual Decision Making and Collective Outcomes Todd M. Gureckis,a Robert L. Goldstoneb aDepartment of Psychology, New York University bPsychological and Brain Sciences, Indiana University Received 7 July 2008; received in revised form 12 August 2009; accepted 16 August 2009MODELING FMRI
A structural MRI image is a relatively high spatial resolution (compared to fMRI) 3D image that can be used to assess the internal anatomy of individual brains. Functional MRI scans produce a set of 3D images recorded over time. These images are lower spatial resolution than structual images. ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN … Psychological Review 1973, Vol. 80, No. 5, 352-373 ENCODING SPECIFICITY AND RETRIEVAL PROCESSES IN EPISODIC MEMORY1 ENDEL TULVING2
COMPUTATION + COGNITION LAB @ NYU research overview teaching nyuconcats resources/code github lab blog psiturk onmi cogsci@nyu *new* how to cogsci*new*: publications by year by category: people todd gureckis (pi) - everyone - COMPUTATION + COGNITION LAB @ NYU: RESEARCH computation + cognition lab @ nyu. The goal of our research is to better understand the memory, learning, and decision processes which allow humans to carry out intelligent and adaptive behaviors. The name of our lab is "computation and cognition" which reflects the two main strands that inform our work.TODD M. GURECKIS
Coenen, A., Ruggeri, A., Bramley, N.R., and Gureckis, T.M. (in press). Testing one or multiple: How beliefs about sparsity affect causalexperimentation.
ABOUT - TODD GURECKIS I am a devoted and purposeful computational ployglot – research in my lab frequently combines methods such as neural networks, Bayesian methods, causal learning, reinforcement learning, program induction, and symbolic systems. In recent years I have branched out from this core theme to consider research on decision making, the cognitive COMPUTATIONAL COGNITIVE SCIENCE AT NEW YORK UNIVERSITY Computational cognitive science is the interdisciplinary study of intelligence that recognizes that intelligence is a property of many sufficiently complex systems. The key insight that unites this field of study is the idea that intelligence is best described in terms of computational systems (i.e., computer algorithms or programs). A NOTE ABOUT NEGATIVE INFORMATION Act 1: Information theory on measurements. The entropy of a random variable X X is defined as. H (X) = −∑ xp(x)logp(x) H ( X) = − ∑ x p ( x) log. . p ( x) The conditional entropy is the uncertainty left in a random variable after knowing the random variable Y Y. First, we need to know the entropy from knowing a specific value: H (X|Y THE ATTENTIONAL LEARNING TRAP AND HOW TO AVOID IT The Attentional Learning Trap and How to Avoid It Alexander S. Rich (asr443@nyu.edu) Todd M. Gureckis (todd.gureckis@nyu.edu) New York University, Department of Psychology, 6 Washington Place, New York, NY10003 USA
ACTIVE LEARNING STRATEGIES IN A SPATIAL CONCEPT LEARNING GAME Active Learning Strategies in a Spatial Concept Learning Game Todd M. Gureckis (todd.gureckis@nyu.edu) Doug Markant (doug.markant@nyu.edu) New York University, Department of EXPLORATORY CHOICE REFLECTS THE FUTURE VALUE OF INFORMATION Exploratory Choice Reflects the Future Value of Information Alexander S. Rich and Todd M. Gureckis New York University The tension between exploration and exploitation is a primary challenge in decision ARE BIASES WHEN MAKING CAUSAL INTERVENTIONS RELATED TO Are Biases When Making Causal Interventions Related to Biases in Belief Updating? Anna Coenen and Todd M. Gureckis Department of Psychology, NYU, 6 Washington Place, New York, NY 10003 USARESEARCH
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