Incidental Sequence Learning in Humans: Predictions of an Associative Account

Datasets relating to this thesis can also be found in ORE: http://hdl.handle.net/10871/15714; http://hdl.handle.net/10871/15713; http://hdl.handle.net/10871/15712; http://hdl.handle.net/10871/15711; http://hdl.handle.net/10871/15710; http://hdl.handle.net/10871/15709; http://hdl.handle.net/10871/15701This thesis aims to investigate how well associative learning can account for human sequence learning under incidental conditions. It seems that we can learn complex sequential information about events in our environment, for example language or music, incidentally, without being aware of it. Awareness is, however, a complex issue with arguments for (Dienes, 2012) and against (Shanks, 2005) the existence of implicit learning processes. A dual process account proposes that there exist two different learning systems, one based on conscious, controlled reasoning and rules, and the other based on automatic association formation, which can take place outside of awareness (McLaren, Green, & Mackintosh, 1994). This thesis attempts to use the predictions of an associative account in conjunction with a suitable method for investigating implicit learning: sequence learning (Destrebecqz & Cleeremans, 2003). The research involves a collection of serial reaction time (SRT) tasks whereby participants respond to on-screen stimuli that follow a sequence that they were (intentional learning) or were not (incidental learning) informed of. Following on from the experimental design of Jones and McLaren (2009) this thesis provides evidence that humans differ in their ability to learn different sequential contingencies. After training sequences of trials where the current trial location was twice as likely to be either: the same as (Same rule); or different to (Different rule) the location two trials before this, participants were far better at learning the latter rule. I found that this result was not adequately simulated by the benchmark associative model of sequence learning, the Augmented SRN (Cleeremans & McClelland, 1991), and present a revised model. This model, amongst other attributes, represents all the stimuli experienced by participants and can therefore learn stimulus-response contingencies. These seem to block learning (to some extent) about the Same rule thus providing an associative explanation of the advantage for acquisition of the Different rule. Further predictions regarding the role of additional stimuli alongside sequence learning were then derived from this associative account and tested on human participants. The first of these was that additional stimuli within the task will interact with sequence learning. I found that human participants show increased Same rule learning when additional, concurrently presented stimuli follow the previous element in the sequence. I demonstrate that when participants perform an SRT task where responses are predicted by the colour of a cue, they are able to learn about this relationship in the absence of awareness. Using this cue-response learning I further investigate cue-competition between sequences and colours under incidental conditions and find evidence that suggests between cue associations may alter the influence of cue competition. These results altogether suggest that stimuli – both simple and sequential – can be learned under incidental conditions. This thesis further proposes that learning about simple and more complex relationships between stimuli interacts according to the predictions of an associative account and provides evidence that contributes to a dual process understanding of human learning.University of Exete