Multivariate pattern analysis
MVPA was applied to decode patterns of neural activity associated with
different numbers of choices. A backward decoding classification
algorithm (linear discriminant analysis) was used, with all electrodes
as features. To ensure reliability and interpretability of the results,
the choice set sizes of 4 and 8 were grouped together to create the
small choice set condition, and 12 and 16 were grouped together to
create the large choice set condition11Decoding was also
conducted with four distinct choice set sizes, with results presented
in the Supplementary Materials.. Before performing MVPA, the epochs
were down sampled to 50 Hz to minimize computation time. A 10-fold
cross-validation procedure was the applied using within-class and
between-class balancing with the Amsterdam Decoding and Modeling toolbox
(Fahrenfort et al., 2018). In this
procedure, the trials were randomized and divided into 10 equal-sized
folds. Nine folds were used for training, while the remaining fold was
used for testing. This process was repeated 10 times, ensuring that each
fold served as the test set once. To ensure the impartiality of the
classifier training, we implemented within-class balancing by
undersampling. This procedure involved the random selection of trials
from conditions with a surplus of trials to harmonize the conditions
with fewer trials, thereby equalizing the count of trials within each
class. Additionally, between-class balancing using undersampling was
employed to mitigate the potential of the classifier from developing a
bias towards the overrepresented class during training, as unbalanced
designs can often result in asymmetrical trial counts. The performance
of the classifier was assessed using the area under the curve (AUC)
(Hand & Till, 2001).
Temporal generalization analysis was conducted to assess the stability
of a representation across different time points. A classifier trained
on a specific time point was tested on all other time points (King &
Dehaene, 2014). The resulting
temporal generalization matrix was used to identify periods of
stability. Additionally, the product of the classifier weights and the
data covariance matrix was calculated and spatially normalized for each
participant to obtain the activation patterns (Haufe et al., 2014).
Cluster-based nonparametric
statistical tests (two-tailed cluster-permutation, alpha p< 0.05, cluster alpha p < 0.05, N
permutations = 5000) were used to evaluate the MVPA results using
FieldTrip (Oostenveld et al., 2011).
Results