The spatial-numerical association of response codes (SNARC) is an example of the spatial organisation of magnitude information. Put simply, when presented with smaller numbers (0 to 4), people tend to respond faster if those stimuli are associated with the left extrapersonal hemiside of their perceived surroundings; when presented with larger numbers (6 to 9), people respond faster if those stimuli are instead associated with the right extrapersonal hemiside of their perceived surroundings. The SNARC effect is this automatic association that occurs between the location of the response hand and the semantic magnitude of a modality-independent number. Even for tasks in which magnitude is irrelevant, like parity judgement or phoneme detection, larger numbers are faster responded to with the right response key while smaller numbers are faster responded to with the left. This also occurs when the hands are crossed, with the right hand activating the left response key and vice versa. The explanation given by Dehaene and colleagues is that the magnitude of a number on an oriented mental number line is automatically activated. The mental number line is assumed to be oriented from left to right in populations with a left-to-right writing system (e.g. English), and oriented from right to left in populations with a right-to-left writing system (e.g. Iranian) (Wikipedia).
Frequency Response Descriptions for LTI Systems
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. An introduction to the description of the input output characteristics of linear time-invariant systems b
From playlist Introduction and Background
Frequency Response Magnitude and Poles and Zeros
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Graphical interpretation of the magnitude response of a system described by a linear constant-coefficient difference equation in terms of the locati
From playlist The z-Transform
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
The tool that engineers use to design buildings in earthquake zones | The response spectrum
Earthquakes are one of the most destructive forces of nature. They could induce substantial movement in the ground, which results in the development of excessive forces in structural components, resulting in their failure. The intent of the analysis is to somehow predict the **maximum resp
From playlist Summer of Math Exposition Youtube Videos
Transfer Functions, Resonance, and Frequency Response. My Patreon page is at: https://www.patreon.com/EugeneK
From playlist Physics
Recommender Systems -Memory Based Collaborative Filtering - Session 4
Memory based collaborative filtering Matrix representation of user-item interactions User vector similarity measures Computational cost and Approximate nearest neighbours Evaluation for collaborative filtering
From playlist Recommenders Systems (Hands-on)
Estimate the Correlation Coefficient Given a Scatter Plot
This video explains how to estimate the correlation coefficient given a scatter plot.
From playlist Performing Linear Regression and Correlation
10c Data Analytics: Variogram Introduction
Lecture on the variogram as a measure to quantify spatial continuity.
From playlist Data Analytics and Geostatistics
How to find the magnitude and unit vector from a given vector
http://www.freemathvideos.com In this video series you will learn multiple math operations. I teach in front of a live classroom showing my students how to solve math problems step by step. My math tutorials should be used to review previous lessons, complete your homework, or study for
From playlist Vectors
Climate Models and Climate Sensitivity
Dan Lunt, University of Bristol, UK, delivers a talk entitled, "Climate Models and Climate Sensitivity", at the YCEI conference, "Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists".
From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists
Remembering and the Brain: Can Brain Scans Detect Memories?
(October 23, 2009) Stanford Professor of psychology and neuroscience, Anthony Wagner PhD, discusses how the brain supports memory for everyday events, and will evaluate whether "mind reading" with brain imaging can detect when a person remembers the past and how this might be used as evide
From playlist Reunion Homecoming
Numerator dynamics can be predicted by understanding how to expand the transfer function and reason about derivatives
From playlist Laplace
Numerical relativity: Mathematical formulation by Harald Pfeiffer
PROGRAM: GRAVITATIONAL WAVE ASTROPHYSICS (ONLINE) ORGANIZERS : Parameswaran Ajith, K. G. Arun, Sukanta Bose, Bala R. Iyer, Resmi Lekshmi and B Sathyaprakash DATE: 18 May 2020 to 22 May 2020 VENUE: Online Due to the ongoing COVID-19 pandemic, the original program has been cancelled. Howe
From playlist Gravitational Wave Astrophysics (Online) 2020
Lattice Supersymmetric Field Theories (Lecture 3) by David Schaich
PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II
From playlist NUMSTRING 2022
EMinar 3,4: Michael Pyrcz - Machine Learning-based Geoscience
The subsurface resource industry has a long history of working with large, complicated geoscience and engineering datasets. The subsurface industry been working with ‘big data’ for decades! There is a growing toolbox of legacy and new emerging data-driven methods available that may offer i
From playlist Random Talks
18 Machine Learning: Conclusion
Final lecture with the take-aways from the Subsurface Machine Learning course to help you succeed with machine learning for spatial, subsurface applications.
From playlist Machine Learning
GED for spatial filtering and dimensionality reduction
Generalized eigendecomposition is a powerful method of spatial filtering in order to extract components from the data. You'll learn the theory, motivations, and see a few examples. Also discussed is the dangers of overfitting noise and few ways to avoid it. The video uses files you can do
From playlist OLD ANTS #9) Matrix analysis
MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan This session dedicated to a review of all different numerical methods students learned from this course. License: Creative Commons BY-NC-SA
From playlist MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015
Anthony Patera: Parametrized model order reduction for component-to-system synthesis
Abstract: Parametrized PDE (Partial Differential Equation) Apps are PDE solvers which satisfy stringent per-query performance requirements: less-than or approximate 5-second problem specification time; less-than or approximate 5-second problem solution time, field and outputs; less-than or
From playlist Numerical Analysis and Scientific Computing
Ex: Find the Magnitude of a Vector in 3D
This video explains how to determine the magnitude of a vector in 3D. Site: http://mathispower4u.com
From playlist Vectors in Space (3D)