Your English writing platform
Discover LudwigExact(1)
As demonstrated by the time courses from this region (Fig. 5), adolescents compared with adults had greater evoked activity for both incentive trial types.
Similar(59)
This shift from reactive control during no-incentive trials to proactive control during incentive trials was observed in conjunction with behavioral improvement in incentive trials.
Additionally, faster RTs in high incentive compared to low incentive trials were observed (t 30) = 4.22, p<0.001).
On incentive trials, incentives were delivered only when performance was accurate, and reaction times were faster than a cutoff value.
A 2-way (2×3) ANOVA including the factors category (Money vs. Liquid) and magnitude (high vs. low vs. no-incentive trials) revealed a significant main effect of incentive magnitude (F 2,29) = 20.56, p<0.001, Figure 2) demonstrating faster RT for incentive trials compared to no-incentive trials.
Specifically in this task, the observed shift from reactive control during no-incentive trials to proactive control during incentive trials likely reflects a change in mnemonic strategies to enhance performance.
In contrast, on incentive trials, activity peaked much earlier, presumably during the encoding or delay period (see Figure 5C).
Likewise looking only at error rates among incentive trials, the effects of magnitude, category, and their interaction were all insignificant (p's >.06).
Moreover, inspection of the other CCN regions showing Liquid-selective effects revealed the same cross-over pattern in activation dynamics was observed during the liquid condition, with activation shifting from a late peak on no-incentive trials to an early peak on incentive trials.
In addition, they were told that two of their win trials would be randomly selected at the end of the experiment as "incentive trials," for which they would receive the actual amount won on those trials.
This interaction was due to the fact that the magnitude effect for low vs. high incentive trials was observed in the Money condition, but not in the Liquid condition (Money: t 30) = 4.59, p<0.001; Liquid: t 30) = 1.25, p = 0.221).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com