This study was based on 340 valid responses and a general linear model for univariate method.
We mapped neural responses using a general linear model in SPM5.
We analyzed the amount eaten (log-transformed) as a response variable in a general linear model [50] with feeding treatment (PA or sucrose), parasitism (yes or no) and diet×parasitism as factors.
We modeled response times using a general linear model validated in previous studies using the harm aversion task [ 12].
The mean blood oxygen level-dependent signal time series was extracted from each seed region and used as the model response function in a general linear model analysis.
First, an analysis of the effect of treatment on mass change was conducted by fitting mass change between day 0 and 30 as the response term in a general linear model with normal errors.
For this, we used the number of mites carried by each individual at the end of the experiment as a response variable in a general linear mixed effects model with the lmer function (lme4 package; Bates et al., 2011), and included as explanatory variables the sex of the parent, the size of each individual, and the mass of the carcass.
All filter settings were referenced against the winning method from step 1, which estimates SN amplitude from a reconstructed response function, using a general linear convolution model with a canonical SCRF and time derivative, at standard high pass filter settings – a bidirectional first order Butterworth filter with cut-off frequency of 0.0159 Hz.
A description of behavioural measures is given in Table 3.> To test whether bark origin would have an effect on the strength of response, we used a general linear mixed model (GLMM) with Gaussian error structure to assess differences in the duration of subjects' first orientation towards the playback speaker, and a GLMM with Poisson error structure to assess differences in initial displacement.
We modeled response times using a general linear model containing regressors indicating whether the outcome was for self or other (other); the unsigned difference in subjective value between the two choice options, which was constructed individually for each subject using their model parameters (| ΔV|); and a constant term.
