search for: psychophysics

...to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers. Apart from advertising this toolbox I want to ask for possibilities in R to fit psychometric functions, as I would rather use R than matlab. Is there a comparable package specific for psychophysics in R? otherwise: which function would be a good choice? Can I have functionality equal to 'psignifit' with glm() from MASS. I would be grateful for some suggestions of people who have experience in sigmoid-fitting in R. Best, Mario. ________________________________________________________...

Dear Friends, I not sure whether this is an Sweave or a beamer problem. The Rnw file: \documentclass[compress,smaller]{beamer} %\documentclass{article} %\usepackage{beamerarticle} \usepackage{Sweave} \title{Psychophysics II} \date{September 9, 2008} \begin{document} \frame{ \begin{Schunk} \begin{Sinput} > ro <- 0.2 > c <- seq(from = -3, to = 4, by = 0.1) > fn <- 1 - pnorm(c) > fo <- 1 - pnorm(c, mean = 1) > h <- fo + ro - ro * fo > f <- fn > plot(h ~ f, type = "l...

Dear list, I hope the topic is of sufficient interest, because it is not R-related. I have N=100 yes/no-responses from a psychophysics paradigm (say Y Yes and 100-Y No-Responses). I want to see whether these yes-no-responses are in line with a model predicting a certain amount p of yes-responses. Standard procedure would be a one-sample binomial test for the observed proportion, chi?(1 df) = (Y-Np)?/(Np) + [(100-Y)-N(1-p)]?/[N(1-...

We announce the release of package MPDiR version 0.1-11 which contains data sets and functions to support the forthcoming book "Modeling Psychophysical Data in R" by K. Knoblauch and L. T. Maloney, Use R! Vol 32, Springer (expected publication date: September 2012). The package includes several data sets from published psychophysical experiments using detection and rating scale measures

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Dear R users,I am looking for an easy (i.e., direct) way in obtaining the F and p values from the intercept in anovas with within-subject designs. Data are from a psychophysics expeirment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject.I would like to know not only whether there is an effect of modality, but also wether the main effect is signiticant (meaning that d' is differnt from 0). I know that a t.test again th...

Dear R users, I am looking for an easy (i.e., direct) way of obtaining the F and p values from the intercept in anovas with within-subject designs. My data are from a psychophysics experiment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject. I would like to know not only whether there is an effect of modality, but also whether the main effect is significant (meaning that d' > 0). I know that a t.test again the null me...

This is to announce a new package MLCM on CRAN. The package provides functions for estimating perceptual scales by maximum likelihood from data collected in a conjoint measurement experiment. Data for conjoint measurement are typically collected using a psychophysical procedure. The stimuli vary along 2 or more dimensions. The observer views pairs of stimuli and judges which stimulus of each pair

Hello everyone, A little background first: I have collected psychophysical data from 12 participants, and each participant's data is represented as a scatter plot (Percieved roughness versus Physical roughness). I would like to know whether, on average, this data is best fit by a linear function or by a quadratic function. (we have a priori reasons to expect a quadratic) Some of my

This is to announce a new package MLCM on CRAN. The package provides functions for estimating perceptual scales by maximum likelihood from data collected in a conjoint measurement experiment. Data for conjoint measurement are typically collected using a psychophysical procedure. The stimuli vary along 2 or more dimensions. The observer views pairs of stimuli and judges which stimulus of each pair

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

hi, thanks for answers to my previous qns. have some more of them. hope it's ok to ask questions on the basics of CELP here. let me know if it's not. 1) synthesized filter stability after interpolation of LSFs I read from some resources that if LSF representation of LPCs is used for interpolation, stability of the synthesis filter is guaranteed. from another source, i read that the

Dear R users, I'm struggling with applying a user-defined link function in lmer. For analyzing data of a 2AFC psychophysical experiment, I would like to model my binary data with a logistic function with a lower limit at 0.5 instead of 0. In a previous question this has been described as a halflogit function. To do so I wrote my own link function and would like to submit it to lmer, however

Hi all, I've noticed that many computational neuroscience research groups use MATLAB. While it's possible that MATLAB may have some features unavailable in R, I suspect that this may instead simply be a case of costly tradition, where researchers were taught MATLAB as students and pay for it as researchers because it's all they know. I'd like to attempt to break the cycle by

...rection factors are commonly used in the journals for experimental and other psychology reports. I have been switching from SPSS to R for over a year now, but I realize now that I don't have the non-sphericity test and correction factors. The backgroud to this question is that I'm doing a psychophysics experiment and the data analysis uses an aov model that looks like this: roiAOV <- aov( roi ~ (Cue*Hemisphere) + Error(Subject/ (Cue*Hemisphere)), data=roiDataframe) where Cue is 2 levels (an arrow on a screen that points left or right) and Hemisphere is 2 levels (brain activity in the left or...

Hi, I am performing experiments in the field of visual perception where we often apply balanced designs. Within a group of normal subjects, we vary different stimulus conditions (like contrast, luminance, temporal frequency of stimulation) and derive some psychophysical or electrophysiological results from our subjects. Often, the main question is to test the effect of these parameters

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