General Discussion

Model 1 results suggest that different causes may underlie mixing effects and switch effects. With regards to mixing effects, it appears that the differences in structural setting between the pure and mixed networks (such as connection weights) may account for the effect, rather than competition at the task-set level per se. Since neither Top signals nor duration of preparation window had strong effects on the overall RT and accuracy on pure and repetition trials, mixing costs are unlikely to be caused by factors relating to endogenous control.

Instead, differences in the speed of translating representations into responses, through connection weights, may underlie mixing effects. This highlights the possibility that mixing effects may be to do with how the task environment is constructed in the first place, rather than the direct consequence of the interaction among the task-associated elements, such as selection among multiple representations.

In the mixed networks, a greater Top signal and a greater preparation window were both effective at reducing RT on the switch trials. This is done through increasing the activation of the task-relevant TA unit, thereby reducing the competition at the TA level and generating a stronger activation of the appropriate response set. In contrast, Top signal and preparation window had more limited effects on the RTs on pure and repetition trials. This is because, on those trials, the task-relevant TA unit was already excited at the beginning of the trial as activation was carried over from the previous trial.

In sum, RT switch costs were found to be directly associated with factors related to endogenous controls, such as the duration of preparation window and the strength of Top signal. The effect of preparation is consistent with the behavioural finding where a longer window and a transparent task cue reduces

switch costs (Koch, 2003; Logan & Bundesen, 2003; Rogers & Monsell, 1995).

An increased Top signal was also effective in reducing RT while improving accuracy. Similarly, a participant with more effective top-down control may therefore need a smaller preparation window to switch task.

The second phase of simulation focused on the effect of free parameters at different network ages. With weaker feedforward connections, younger networks were overall slower on all trial types, consistent with the slower RT(ms) observed in children as compared to adults. However, younger networks also exhibited a greater accuracy than the older networks. This is in stark contrast to the behavioural finding that children were overall less accurate as well as slower. Furthermore, younger networks generally exhibited a larger between-condition RT cost (mixing and switch costs) than older networks. Such developmental differences were not found in the behavioural experiments.

While it is possible to reduce the age effect on between-condition costs by eliminating the overall switch cost and mixing cost through a very high Top signal, to do so would also go against the common belief that children were overall less efficient in top-down control than adults. Furthermore, a high Top signal would also result in higher accuracy on switch trials, since it would ensure the correct response set was chosen. Although children in our experiments did not exhibit greater between-condition processing costs (mixing and switch costs), they nonetheless exhibited lower overall accuracy on all trial types. A high Top signal would not be consistent with our behavioural findings.

It is likely that other mechanisms are at play in reducing the effect of age on RT switch cost.

Although the general network behaviours were similar in both young and older networks, younger networks were more susceptible to priming costs due

to PS2RO primes on switch trials. The greater RT priming costs are likely to be due to the slower baseline speed in Young and Middle networks. This is because a slower network also means that the primes would go through a greater number of cycles during response setting and selection. In comparison, the simulations did not uncover any priming facilitation on pure and repetition trials, indicating that goal-mediated stimulus-task attribute primes did not further promote response setting when the task goal was already highly activated.

There was also a floor effect in Model, as all networks were resistant to errors, despite the occasional errors under extreme conditions on switch trials (i.e. very low Top signal or very low preparation window). Counterintuitively, younger networks were more accurate than older networks in Model 1. In the behavioural experiment, only adults were at ceiling performance across all trial types, younger children, particularly 4-year-olds, made far greater number of errors than adult participants, including the ‘easy’ repetition trials. Model 1 was unable to capture the developmental differences in accuracy.

In summary, Model 1 was able to capture aspects of between-condition RT costs: (1) RT mixing costs may reflect different underlying assumptions in information processes. The associative strengths between different representations may be stronger in pure condition than in mixed condition, despite the stimuli and responses share perceptual and motoric similarities in the two conditions, and (2) RT switch costs are directly associated with the parameters that relate to endogenous control, such as preparation window and Top signal. Therefore, RT switch costs may be a valid measure of cognitive control in switching tasks, at least when the accuracy is high (e.g. >90%). Model 1 was also able to capture some developmental differences: (3) Younger networks were slower than older networks due to weaker feedforward

connection weights, which determine the baseline speed of a network. (4) Younger networks with weaker connections experienced greater RT priming costs from PS2TA primes, which is consistent with the past study demonstrating a greater automatic priming effects in children than adults (Ridderinkhof et al., 1997; Smulders et al., 2005).

However, Model 1 was unable to capture other notable behavioural findings in bimodal CMTS—namely: (1) the lack of developmental differences in between-condition RT costs, and (2) priming facilitation in pure and repetition trials. Furthermore, (3) Model 1 shows a strong floor effect on accuracy in pure and repetition trials, and a smaller floor effect on switch trials. Thus, Model 1, with its relatively simple architecture, cannot capture the developmental differences in accuracy observed in children and adults.

In Chapter 6, I will introduce additional connections to the existing Model 1 and investigate how performance changes when TA units can be activated reactively, and how RT priming facilitation may be due to reactive primes that are not associated to task attributes. Past literature suggests that young children are more likely to respond reactively to perceptual information, and it is possible that these reactive processes specific to our experiment masked the developmental effect on between-condition costs (e.g. mixing or switch costs) in the bimodal CMTS study.

Table 5.1 Parameter setting in Model 1 Between-set lateral connections between RODog and

RO_Bird

1. The symbol ‘-’ indicates that the setting is the same as in Old network

2. The number is the standard deviation of a normal distribution with a mean of 0, which is used to produce a random noise added to each unit activation update.