Targeted-component analysis
As a complementary, ERP-focused
approach, we examined the responses for all comparisons of interest in
the N1, P2 and P3 time windows at the Fz, Cz and Pz electrodes. The
windows were defined after visual inspection of the data by locating the
highest negative or positive (depending on the component of interest)
peak in the usual latencies and electrodes for each component, and
defining a window centered on the peak and adjusted to the width of the
component, as reported by previous works (SanMiguel et al., 2013). We
observed morphological differences between responses to acquisition
sounds and test sounds, thus windows were defined separately for the two
types of sounds, based on the peaks observed in either the average of
all acquisition sounds or all test sounds. The N1 was measured at the Fz
electrode in the window 80-120 ms in acquisition sounds and 110-140 ms
in test sounds. The P2 was measured at Cz in the window 180-240 ms in
acquisition sounds and 210-270 ms in test sounds. Both components showed
reversed polarity at the mastoid electrodes in these windows. The P3
component was measured at the Fz electrode (P3a) and the Pz electrode
(P3b), respectively, in the 310-390 ms time-window in acquisition sounds
and the 340-400 ms time-window in test sounds.
We ran repeated-measures ANOVAs in order to test for differences on the
mean amplitude of each component at the selected electrodes between
conditions of interest. Specifically, for the acquisition sounds, we ran
a two-way ANOVA with the factors agency (two levels: agent and
observer) and learning stage (two levels: early and late) for
each ERP component of interest.
We ran two separate ANOVAs for each component for the test sounds. A
one-way ANOVA with the factor movement-sound congruency (levels:
congruent and incongruent) and a two-way ANOVA with the factors agency
and learning stage. We also analysed whether the effects of congruency
were modulated by agency and learning stage by using the differences
between amplitudes in congruent and incongruent trials as the dependent
variable in a two-way ANOVA with the factors agency and learning stage.
Correlation
analysis
We aimed to identify electrophysiological markers related to the
performance benefits associated with active learning amongst a small set
of pre-defined candidate ERP components. Thus, we tested for significant
correlations, using Pearson’s coefficient, between each participant’s
effect of agency on the percentage of correct responses and the effect
of agency on the N1, P2 and P3 in acquisition sounds (amplitude of
acquisition sound ERPs in agent condition – observer condition). Given
that agency effects on the behavioural data were restricted to the early
learning stage (see results), we used only the performance data from the
early learning stage (%Correct in agent–%Correct in passive) for this
correlation analysis.
Behavioural results
Behavioural results show that active exploration led to faster learning
and better memory performance. However, given enough training, passive
viewing led to similarly good performance (fig. 2).
A two-way ANOVA was run with the factors agency (agent versus observer)
and learning block (1 to 7) and the dependent variable %Correct.
We found a significant main effect of agency [F(1, 22)= 11.865, p < .001, ηp2 =
0.32] and learning block [F(6,132) = 16.534, p
< .001, ηp2 = 0.39], and a
significant interaction between the two factors
[F(6,132) = 6.635, p < .001,
ηp2 = 0.20]. Post-hoc t-tests
(Bonferroni corrected) showed that memory performance in the agent
condition was significantly higher than in the observer condition in the
first, second and fourth learning block. The effect of agency on
%Correct was inversely correlated with learning block
(r(5) = -3.922, p = .01). Post-hoc t-tests showed that
the difference between agent and observer was significantly larger in
the first learning block than in the last learning block
[t(22) = 4.291, p < 0.001, d =
1.105]. The effect of learning was significantly smaller in the agent
compared to the observer condition [t(22) = -4.291, p
< 0.001, d = -0.656] (subtracting learning block 1
from learning block 7 in agent versus observer condition). This shows
that agency accelerates memory encoding for arbitrary audiovisual
associations.