2.6 Specification curve analysis
To further explore whether the predictability between language control
and executive control influenced behavioral performance, we conducted
the specification curve analysis (SCA, Simonsohn et al., 2020). The SCA
is a framework of modeling all possible specifications (consisting of
independent, dependent and control variables) to provide an unbiased
brain-behavior relationship to test all reasonable specifications.
Recent studies demonstrated its benefits lie in reducing the overall
false-positive rate and strengthening the robustness of a given finding
(Cosme & Lopez 2020; Flournoy et al. 2020; Yuan et al., 2023). In the
current study, we aimed to reveal the relationship between brain
activity and corresponding behavioral performance in the flanker task.
Furthermore, we tentatively examined the probability of the predicted
values derived from the RR model would interact with behavioral
performance.
This analysis was performed with the specr package (Masur & Scharkow,
2020) in R. First, the mean RTs of congruent and incongruent trials
across three switching contexts were separately specified in the flanker
task as the outcome variables. Then, the mean EEG amplitudes of each
flanker condition in three switching contexts were extracted within a
0-800ms time window (the real values). Additionally, using the RR
prediction model, the weights of the prediction model were multiplied
with the independent variable X (i.e., amplitudes during the
picture-naming trials). This calculation yielded the predicted values
(the predicted amplitude of the flanker task by the model) in each
switching context. Both the real and predicted values from three
contexts (each model for congruent and incongruent conditions contained
6 values.) were then input into the SCA as the neural predictors to
examine the corresponding brain-behavior relationships and how
brain-to-brain predictions interact with behavioral performance (i.e.,
significant interactions between the predicted values and RTs). In
addition, participants’ demographics (i.e., age and gender), language
proficiency (L1 self-rating scores, L2 self-rating scores, OPT scores
and CET-4 scores), and the L2 age of acquisition were added as control
variables. In each specification, a single indicator (i.e., each value)
was set as the predict of interest, and its association with the outcome
variable was examined with the changes of specifications and covariables
included in the model. The standardized regression coefficients for each
predictor were subsequently calculated and ordered by the effect size to
plot the specification curve. Finally, the median standardized
regression coefficient was tested with the proportion of the
statistically significant positive and negative coefficients. The
regression coefficients were statistically inferred through a
bootstrapping process (1000 times), which generated confidence intervals
around the median of the curve and assessed the discrepancy between the
observed curve parameters and the null distribution which assumed no
statistically significant relationship between each predictor-outcome
pair.