Implications for applied inhibitory control training

A particularly interesting application of inhibitory control training is to utilise it to reduce compulsive behaviours by paring compulsion associated stimuli with stopping (for meta-analysis see: Allom et al., 2016; Jones et al., 2016).

For example, Lawrence, O’Sullivan et al. (2015) found that by pairing images of unhealthy foods with the requirement to withhold responses in a go/no-go task resulted in weight-loss, which persisted 6-months post intervention, and reduced

calorific intake.

The present experiments have a number of implications for more applied research, particularly on how to best facilitate the acquisition of stimulus-stop associations. Perhaps the first thing to note is that, in general, the associatively-mediated stopping effect in these experiments is quite strong, suggesting that the procedures used (in particular the hybrid Go/No-go procedure) are very effective.

Secondly, the research demonstrates that task instructions and demands brought about by subtleties within the design can crucially change what is learnt. Thus, on the assumption that excitatory associations are formed more readily than inhibitory ones, learning is likely to be more effective if the effective outcome is clearly stopping. Experiments 6 and 7 demonstrate that multiple and/or salient stop signals are likely to be crucial for this, a conclusion backed up by the research presented in Chapter 2.

Finally, across all studies, the singleton discrimination (i.e. C-, D+) was best learned, or at least as strong as, the feature-negative or feature-positive com-pound discriminations. And at test, there was no evidence for an advantage for either the positive or negative feature relative to stimuli trained on their own. This is rather surprising; one might expect that P+ PQ-, for example, would result in stronger inhibition to Q than simple C+ D- training, but the present data yeilds no evidence that this is the case. The reasons for this result are, at present, unclear (though one possibility is that it may be explained by altering the rep-resentational assumptions used by Rescorla-Wagner as in McLaren et al., 2012 or Wagner, 2003), but one might conclude that single stimulus training must therefore be at least as effective as compound stimulus training, and that there is no virtue in using a more complex design. This may be true, however, there is one theoretical reason why compound stimulus training might prove to be more effective than singleton designs. Research on conditioned inhibitors (i.e. Q, in an P+ PQ- design) in rats suggests that they do not extinguish, or at least extinguish

at a significantly reduced rate (McLaren & Verbruggen, 2016; Williams, 1986;

Zimmer-Hart & Rescorla, 1974). Applying this idea to an intervention designed to reduced food consumption predicts that a design encouraging conditioned inhibi-tion, such as P+ PQ-, should result in longer lasting effects than just presenting unhealthy foods on stop trials (as is often the case). This is of course speculative, but remains an interesting hypothesis to be tested.

3.9 Conclusion

To summarise, it seems that - within the context of the theoretical framework presented in Chapter 1 - associations can form between either the stop or go centre, depending on the demands of the task. Therefore, in tasks that emphasise stopping and use multiple stop signals (as the majority of experiments in this thesis do), associations are formed between the cues and stopping.

So far I have considered automatic inhibition as an implicit reactive process; in the sense that I have assumed the learning in the present paradigm is implicit and is therefore unlikely to be mediated by top-down processes. However, it is possible that this is not achieved by implicitly acquired associations between a stimulus and the stop centre, but learning that a stop signal is likely after certain stimuli, expecting to stop, and therefore engaging proactive control. The following chapter contrasts these two possibilities; comparing the automatic stop effect to explicitly instructed cues (that should engage proactive control) and incidentally trained cues (that follow the same format of the experiments presented in Chapters 2 and 3).

CHAPTER 4

E x p l i c i t I n s t r u c t i o n a n d E x p e r i e n c e d Co n t i n g e n c y

T

his chapter discusses how explicit instruction and experienced contin-gency combine to determine learning and behaviour. There are two main questions of interest: First, is there evidence for dissociable processes supporting these two modes of learning, or are both better explained by appealing to a single learning process? Secondly, how does instruction influence learning about contingency based on experience, and vice-versa? I address this question in the context of inhibition learning, using the cued inhibition paradigm that I reviewed in Chapter 1. The paradigm used here is a modification of that more fully described in Chapter 2 which allows incidentally trained inhibition learning and explicitly cued instruction to be measured simultaneously, giving me the opportunity to study how these two modes of learning interact when deployed together.

Thus far, I have discussed response inhibition primarily as a reactive processes triggered by a stop signal. However, control is more multifaceted than I have al-luded to in previous chapters and even a routine task, such as cycling to work, contains numerous acts of control: More often than not one will have to slow to allow for congestion, unexpectedly stop to avoid colliding with pedestrians

who have unexpectedly stepped into the road, or (after recalling the experience of a near accident) stop and wait at a particularly treacherous junction. Such acts of control can be broadly divided into two classes: Reactive and proactive (Aron, 2011; Braver et al., 2007; Braver, 2012). Reactive control refers to rapid adjustments that are typically initiated by a salient stimulus (e.g. a pedestrian in the road). Whereas proactive control precedes (and possibly prepares for) the use of reactive control and is typically triggered by consistencies within the environment (e.g. congestion) or internal cues (e.g. recalling a dangerous situ-ation). Similarly acts of control can be subdivided into those best described as deliberate, conscious and goal directed, and more bottom-up, automatic, and associatively mediated processes (Verbruggen, Best et al., 2014; Verbruggen, McLaren & Chambers, 2014). For example, whilst slowing at a dangerous junction may, in the first instance, be characterised by deliberate thought, after experi-ence of repeatedly slowing at the same junction this action may become primed or activated automatically by an association between a representation of the junction and stopping.