The present research sought to describe and explain age related changes to associative learning processes. Eleven experiments were conducted using a human conditional learning paradigm. Background data on health, lifestyle, and cognitive ability were collected and used as predictor variables in multiple regression analyses. Experiments 1 to 8 were formative, and found that older participants showed an overall age related decline in learning ability exacerbated by the number of stimuli and outcomes used, and the concurrent presentation of different problem types. Configural models of learning (e.g. Pearce, 1994, 2002) best predicted young participants’ learning whereas older people’s learning was more consistent with elemental models (e.g. Rescorla-Wagner, 1972), suggesting an age related change in generalisation processes. Those who learned problems better were also more likely to be able to articulate a rule that had helped them learn the problem. Age itself was the most predominant predictor of accuracy in these experiments. Experiments 9, 10, and 11 were multiple stage experiments that looked at the extent of pro- and retro-active interference in learning. Experiments 9 and 10 used easy and hard HCL problems to examine the role of rule induction in learning. Older participants who had learned initial discriminations better were more prone to pro-active interference in both experiments, the extent of which was predicted most reliably by fluid intelligence. Rule learning had a profound effect on participants’ predictions during the unreinforced test stage. In Experiment 9 (Easy-Hard) younger participants suffered from more retroactive interference than older people. This pattern was far less pronounced in Experiment 10, (Hard-Easy) suggesting that problem order affected the way participants generalised from rule-based knowledge. This observation is inexplicable by associative learning theories, and explanation may require a problem solving approach. Experiment 11 examined feature-based generalisation. Again older participants suffered more proactive and retroactive interference and elemental theories predicted their responses best, whereas younger participants responses were consistent with configural models of learning. In this instance, resistance to pro- and retro-active interference was predicted by fluid intelligence. Overall the research concluded that there is a demonstrable, complexity dependent change in associative learning processes in later life. It appears that humans have an increasing tendency to rely on elemental, rather than configural processes of generalisation in later life, and this leads to overgeneralisation between stimuli and an inability to resist pro- and retroactive interference in learning. This may be as a result of an inhibitory or source monitoring failure as a consequence of atrophy in the frontal lobes of the brain, although some of the learning deficits are explicable through mnemonic decline.

Deep learning models have been used widely in various areas and applications of our everyday lives. They could also change the way non-profit organizations work and help optimize fundraising results. In this thesis, sequential models are applied in fundraising to compare their performance against the traditional machine learning model. Sequential model is a type of neural network that is specialized for processing sequential data. Although some research utilizing machine learning algorithms in fundraising context exists, it is based on the data extracted from the specific time window, which does not take time-dependency of features into account; therefore, time-series features are independent at each data point relative to others. This approach results in loss of time notion. In this thesis, we experiment with the application of time-dependent sequential models including Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and their variants in the fundraising domain to predict the alumni monetary contribution to the university. We also expand our study by including the architecture that treats time-invariant demographic data as a condition to the sequential layers. In this model, the time-dependent data is concatenated after running the sequential model. Sequential deep learning is empirically evaluated and compared against the traditional machine learning models. The results demonstrate the potential use of both traditional machine learning and sequential deep learning in the prediction of fundraising outcomes and offer non-profit organizations solutions to achieve their mission. / Graduate