[图书][B] Modeling psychophysical data in R
K Knoblauch, LT Maloney - 2012 - books.google.com
Many of the commonly used methods for modeling and fitting psychophysical data are
special cases of statistical procedures of great power and generality, notably the …
special cases of statistical procedures of great power and generality, notably the …
Maximum likelihood conjoint measurement
K Knoblauch, LT Maloney, K Knoblauch… - … psychophysical data in …, 2012 - Springer
Conjoint measurement allows the experimenter to estimate psychophysical scales that
capture how two or more physical dimensions contribute to a perceptual judgment. It is most …
capture how two or more physical dimensions contribute to a perceptual judgment. It is most …
Classification images
K Knoblauch, LT Maloney, K Knoblauch… - … psychophysical data in …, 2012 - Springer
Abstract In 1996, Ahumada [2] reported the following experiment. On each trial, the observer
was presented with two small horizontal bars separated by a small, lateral gap (of 1 pixel …
was presented with two small horizontal bars separated by a small, lateral gap (of 1 pixel …
Modeling in R
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
Modeling in R | SpringerLink Skip to main content Advertisement SpringerLink Account Menu
Find a journal Publish with us Track your research Search Cart Book cover Modeling …
Find a journal Publish with us Track your research Search Cart Book cover Modeling …
The Psychometric Function: Introduction
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
The psychometric function is a summary of the relation between performance in a
classification task (such as the ability to detect or discriminate between stimuli) and stimulus …
classification task (such as the ability to detect or discriminate between stimuli) and stimulus …
Signal Detection Theory
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
Signal detection theory (SDT), developed in the 1950s, is a framework of statistical methods
used to model how observers classify sensory events [72, 169]. In this chapter we describe …
used to model how observers classify sensory events [72, 169]. In this chapter we describe …
The Psychometric Function: Continuation
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
In the previous chapter we showed how to use direct optimization methods and the
generalized linear model (GLM) to fit psychometric functions to Yes–No data. In an extended …
generalized linear model (GLM) to fit psychometric functions to Yes–No data. In an extended …
Maximum Likelihood Difference Scaling
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
In previous chapters we focused on psychophysical methods such as discrimination and
models such as signal detection theory which allow us to measure the ability to discriminate …
models such as signal detection theory which allow us to measure the ability to discriminate …
Mixed-Effects Models
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
With one exception the models that we have treated before this chapter contain a single
source of variability. In the linear models with Gaussian error, the variance of the population …
source of variability. In the linear models with Gaussian error, the variance of the population …
A First Tour Through R by Example
K Knoblauch, LT Maloney, K Knoblauch… - … Psychophysical Data in …, 2012 - Springer
A First Tour Through R by Example | SpringerLink Skip to main content Advertisement
SpringerLink Account Menu Find a journal Publish with us Track your research Search Cart …
SpringerLink Account Menu Find a journal Publish with us Track your research Search Cart …