4.4 Systematic Changes Demonstrated Across Single-Trial
Amplitude and Latency Measures
Previous studies using ST analyses have examined systematic changes in
latency across the duration of the ERP task to identify informative
patterns that may represent learning over time (Jongsma et al., 2006,
Quian Quiroga et al., 2007; Rey et al., 2015). In this current study, an
exploratory curve fitting procedure was used to determine whether
changes in amplitude, latency, or RT across trials in a single EEG/ERP
session of the speeded version of the flanker task were random or
systematic. Curve fitting revealed systematic changes that were similar
across all components, for both amplitude and latency, as well as RT.
These systematic changes suggest that for the speeded flanker task
participants require some time at the start of a session to develop a
strategy for successful goal directed behavior. This is reflected by the
increasing of the components’ amplitudes, increasing latencies, and
longer RTs across the initial trials, peaking around 50 to 100 trials.
Following these initial peaks, there was a systematic trend of
successively smaller component amplitudes, decreasing component
latencies, and shorter RTs. This suggests that once a strategy was
developed, participants were able to sustain their behavioral
performance more efficiently, more quickly, and with fewer allocated
resources. At the end of the block, there were slight increases in
component amplitudes and latencies with concomitant RT increases which
may reflect fatigue or boredom. Similar systematic changes were repeated
in the 2nd block (i.e., after the hashed line in
Figures 2 and 3) although blunted, indicating that the strategy was
already developed during block one and less time was needed to
re-establish and less effort to sustain the behavioral pattern. These
systematic changes were most prominent for peak-to-peak data from the
STP approach and much less so in the data from the STW approach.
The observed non-linear trends in the peak-to-peak amplitude measures
accounted for 12% to 25% of variance across all trials depending on
the component. Curve fitting models for latency revealed non-linear
systematic change similar to component amplitudes and response times.
These findings are consistent with other ST analyses which also have
examined systematic changes in latency across the duration of the ERP
task to identify informative patterns that may represent learning over
time (Jongsma et al., 2006, Quian Quiroga et al., 2007; Rey et al.,
2015). Thus, the consistency of the trends in the trial-to-trial changes
in amplitude and latency across components and RT suggest that the
participants “learn” to implement a strategy for task performance
which improves with the accumulation of experience, i.e., successive
trials. Similarly, trends of increased amplitudes and latencies at the
end of blocks suggests a time-dependent effect such as boredom or
fatigue.