Understanding the relationship between working memory and associative learning.
Measures of working memory and associative learning correlate weakly, but significantly (e.g. DeYoung, Peterson, & Higgins, 2005; Kaufman, DeYoung, Gray, Brown & Mackintosh, 2009) and are also known to make independent contributions to general intelligence (Kaufman et al.). However, the measures of associative learning employed in these studies have been deterministic in nature, that is to say the association between learnt items is perfect, but in the real-world and in many studies of reinforcement learning, this is rarely the case – instead uncertainty is often a feature of the probabilistic relationship between events. Recently, Easdale, Le Pelley & Beesley (2019) have considered a relationship between learning and working memory capacity, proposing that the size of working memory may be dynamic; in particular a function of learning. Easdale et al. suggest that that when learning the relationship between a stimulus and an uncertain outcome, then working memory may adjust to be quite large, however when learning is deterministic, working memory may be relatively small. The logic here is that in the face of uncertainty the learner should integrate more information over time to make behavioural choices; but when certainty prevails then information from (in the extreme case) only the last trial will suffice.
Objective 1. Associative learning and Working memory – Local influences (Year 0 to Year 1.5). A series of 4 experiments will measure working memory capacity to the same stimuli as used in an associative learning task. N-back tasks will be conducted before and after learning trials that have either a probabilistic (p outcome|cue =.7) or deterministic (p outcome|cue=1) outcome. The prediction is that WM capacity will increase after probabilistic associative learning relative to deterministic. Associative learning training will be brief or long to characterise points when prediction errors are either large or small.
Objective 2. Associative learning and Working memory – Global influences (Year 1.5 to Year 3). It is possible that engaging in associative learning under conditions of uncertainty has an impact on WM in tests and stimuli that are unrelated to the stimuli used during the learning task. In a second series of 4 experiments participants will complete a backwards digit span task before and after probabilistic or deterministic associative learning training. If the effects of associative learning have a general (global) effect then WM capacity will again increase after probabilistic associative learning relative to deterministic.
Objective 3. The developmental trajectory of learning-memory correlations. (Years 1, 2 and 3). In each year of the PhD, the student will participate in summer scientist week to collect data from children aged between 5 to 11 years of age. These experiments will employ tasks from objective 1 and 2 and seek to examine the developmental trajectory of the relationship between working memory capacity and associative learning.