We computed cueing effect as the difference in accuracy (measured as proportion of correct responses) between the orientation cue condition and neutral cue condition. We conducted two types of analyses to assess the shape of attentional modulation. In the first analysis, we averaged the cueing effects across the positive and negative offsets and used standard
t tests to evaluate enhancement (defined as a positive cueing effect) and suppression (defined as a negative cueing effect) relative to the baseline. In the second analysis, we fit both a monotonic model and a nonmonotonic model to the cueing effect using nonlinear regression to further quantify the surround suppression effect. We included data points up to one point outside the suppressive surround as the data range for fitting the models, based on our previous study showing that farther features are modulated by feature-similarity gain instead of surround suppression (i.e., hybrid profile of FBA; Fang, Becker, & Liu,
2019). Model fitting was performed both on individual participant data and group averaged data. The monotonic model was implemented as a Gaussian function, which had three free parameters:
\begin{equation}y = {A \over w}{e^{ - {{{x^2}} \over {2{w^2}}}}} + b,\end{equation}
where
y is the cueing effect;
x is the cue-target offset; and
w,
A, and
b are the free parameters controlling the shape of the function. The nonmonotonic model was implemented as a polynomial function.
1 \begin{equation}y = a{x^4} + b{x^2} + c,\end{equation}
where
y is the cueing effect,
x is the cue-target offset,
a,
b, and
c are the three free parameters controlling the function's shape. To quantify the evidence supporting each model, we computed the Bayesian information criterion (BIC; Schwarz,
1978), with the assumption of a normal error distribution:
\begin{equation}BIC = n\ln \left( {{{RSS} \over n}} \right) + k\ln \left( n \right),\end{equation}
where
n is the number of observations,
k is the number of free parameters, and
RSS is residual sum of squares (Raftery,
1999). We calculated the Bayes factor (
BF) of the nonmonotonic model over the Gaussian model based on BIC approximation (Wagenmakers,
2007):
\begin{equation}BF = {e^{\left( {{{\left( {BI{C_G} - BI{C_P}} \right)} \over 2}} \right)}},\end{equation}
where
BICG is for the Gaussian model,
BICP is for the polynomial model.