Investigating a Dual-Process Account of Learning, Memory and Cognition.

Lead Research Organisation: University of Exeter
Department Name: Psychology


his project seeks to answer the question of whether there is compelling evidence for an associative system in people, and in doing so resolve the larger ongoing theoretical debate of whether single process (entirely propositional, see Mitchell, De Houwer, & Lovibond, 2009) or dual-process accounts of learning and memory (see McLaren et al., 2014) are the better model for human cognition. This debate is not limited to learning, as dual-processes accounts have been used to explain many cognitive functions such as reasoning and decision making (Evans & Stanovich, 2013). An answer to this question would have real consequences. Lovibond (see 2018 reference for an example) has argued that clinical therapies should de-emphasise the behavioural component and focus instead on the more cognitive, "talking" part based on evidence casting doubt on the contribution made by associative processes to human mental life. Given this, the project has not only the potential to significantly contribute to the scientific literature, but to also help us apply our understanding of learning to mental health issues such as anxiety and phobias, as well as informing educational practice.

My approach will be to use both an empirical and a computational modelling perspective to study phenomena such as peak shift in humans. Empirically, I will employ both behavioural and neuroscientific methods, such as electroencephalography (EEG) and transcranial direct current stimulation (tDCS). EEG can be used to examine the neural signatures that accompany any effect but is essentially correlational in nature. Neurostimulation using tDCS, however, will allow me to test whether each process (associative and propositional) can be independently modulated and enable me to infer the causal mechanisms responsible for any effect. These empirical techniques will be combined with computational modelling that will allow analysis of the specific processes and equations that govern learning, as well as how stimuli and objects are represented in the mind. This can be used to build upon existing associative (e.g., McLaren & Mackintosh, 2002) and cognitive (e.g., Lee, Hayes and Lovibond, 2018) models.

An example of a study that would help distinguish between dual and single-process theories is one in which two dimensions are tested. In this study, both brightness (ranging from dark to bright, denoted by X) and colour (ranging from blue to green, denoted by Y) would constitute the two dimensions. Henceforth, the stimulus values will be represented as (X,Y) and range from dark blue (1,1) to light green (11,11), with a midpoint of medium bright blueish-green (6,6). Participants would initially be trained with two stimuli - 3,5 and 9,7 - that are associated with two categories, left and right, respectively. As these stimuli are obviously brighter and darker than one another, this will lead to induction of a rule governing the brightness dimension (3 and 9). They will then be trained with two more stimuli - 5,5 and 7,7 - which are the same colour as the initial training stimuli, but not as extreme in their brightness and darkness. This serves to further establish a rule on the brightness dimension and will also ensure sufficient associative strength has accumulated to the colour dimension. If participants are attending to the brightness dimension, as this was most clearly relevant during training, a dual-process account would predict rule based responding when tested on this dimension (where the stimulus colour remains constant). Furthermore, this account would also predict peak shift when participants are tested on the colour dimension. Participants would be interviewed after to ascertain if they were using any rules or noticed any relationships between the stimuli and categories. This experiment would also be computationally simulated, and the results would be compared with that of the model.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000630/1 01/10/2017 30/09/2027
2399616 Studentship ES/P000630/1 01/10/2020 30/09/2023 Toby Johnson