Anterograde interference emerges when
two differing tasks
are learned in close temporal proximity, an effect repeatedly attributed to a competition between differing task memories. However, recent development alternatively suggests that initial learning may trigger a refractory period that occludes neuroplasticity and impairs subsequent learning, consequently mediating interference independently of memory competition. Accordingly, this study tested the hypothesis that interference can emerge when
the same motor task is being learned twice,
that is when competition between memories is prevented. In a first experiment, the inter-session interval (ISI) between two identical motor learning sessions was manipulated to be 2 min, 1 h or 24 h. Results revealed that retention of the second session was impaired as compared to the first one when the ISI was 2 min but not when it was 1 h or 24 h, indicating a time-dependent process. Results from a second experiment replicated those of the first one and revealed that adding a third motor learning session with a 2 min ISI further impaired retention, indicating a dose-dependent process. Results from a third experiment revealed that the retention impairments did not take place when a learning session was preceded by simple rehearsal of the motor task without concurrent learning, thus ruling out fatigue and confirming that retention is impaired specifically when preceded by a learning session. Altogether, the present results suggest that competing memories is not the sole mechanism mediating anterograde interference and introduce the possibility that a time- and dose-dependent refractory period—independent of fatigue—also contributes to its emergence. One possibility is that learning transiently perturbs the homeostasis of learning-related neuronal substrates. Introducing additional learning when homeostasis is still perturbed may not only impair performance improvements, but also memory formation.
Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning
