Manipulating attentional load in sequence learning through random number generation.
Bottom Line: This discrepancy probably results from the specific type of secondary task that is used.In a third experiment, we compared the effects of RNG and TC.Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation.
Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and therefore may bias performance on the primary task in different ways. Here, we used a random number generation (RNG) task, which may allow for a closer monitoring of a participant's engagement in a secondary task than the popular secondary task in sequence learning studies: tone counting (TC). In the first two experiments, we investigated the interference associated with performing RNG concurrently with a serial reaction time (SRT) task. In a third experiment, we compared the effects of RNG and TC. In all three experiments, we directly evaluated participants' knowledge of the sequence with a subsequent sequence generation task. Sequence learning was consistently observed in all experiments, but was impaired under dual-task conditions. Most importantly, our data suggest that RNG is more demanding and impairs learning to a greater extent than TC. Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation. Our studies indicate that RNG is a promising task to explore the involvement of attention in the SRT task.
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Mentions: As in previous experiments, the ANOVA with Block (the first 13 trainingblocks) as a within-subjects variable and Condition as a between-subjectsvariable was performed; however, in this experiment, three differentconditions were used: Control, RNG, and TC. The analysis showed asignificant main effect of block: The differences between RTs in the firstand 13th blocks was 63 ms, F(12, 684) = 29.2,MSE = 318,605.740, p < .001,η2 .34. The main effect of condition was alsosignificant, F(2, 57) = 7.8, MSE =487,482.33, p = .001, η2 .21.RTs in thecontrol condition (414 ms) were significantly faster than RTs in the RNG(488 ms), F(1, 34) = 12.8, MSE =650,313.07, p = .001, η2 .27, and in theTC (493 ms), F(1, 40) = 14.6, MSE =832,844.79, p < .001, η2 .26, conditions, but there wasno significant difference in RTs between the latter two conditions (t <1). The Block × Condition interaction was also significant,F(24, 684) = 4.7, MSE = 4,262.73,p < .001, η2 .14. As shown in Figure 3, the RTs of participants in theRNG condition increased until Block 5 and then began to decrease accordingto the same pattern as the RTs of participants in the control and TCconditions. This result suggests that the non-monotonic effect of learningthat was previously observed in Experiment 1b was not accidental. Thus, weran an additional ANOVA (Block × Condition) with only Blocks 5 to 13.This ANOVA yielded significant effects of block, F(8, 456)= 34.6, MSE = 23,517.17, p < .001,η2 .37, and Condition, F(2, 57) = 7.8,MSE = 321,834.41, p = .001,η2 .21. However, these two factors did not interact asthey did in Experiment 1b, F(16, 456) = 1.3,MSE = 885.89, p = .19,η2 .04.
No MeSH data available.