Learning efficiency: The influence of cue salience during spatial navigation

Examining individual learning patterns using generalised linear mixed models

Everyone learns differently, but individual performance is often ignored in favour of a group-level analysis. Using data from four different experiments, we show that generalised linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified, clustered and used for comparisons across various experimental conditions or groups. This analysis can handle a range of datasets including discrete, continuous, censored and non-censored, as well as different experimental conditions, sample sizes and trial numbers. Using this approach, we show that learning a face-named paired associative task produced individuals that can learn quickly, with the performance of some remaining high, but with a drop-off in others, whereas other individuals show poor performance throughout the learning period. We see this more clearly in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals who produced a rapid learning and another showing a slow and gradual learning pattern. Using data from another spatial learning task (Sea Hero Quest), we show that individuals' performance generally reflects their age category, but not always. Overall, using this analytical approach may help practitioners in education and medicine to identify those individuals who might need extra help and attention. In addition, identifying learning patterns may enable further investigation of the underlying neural, biological, environmental and other factors associated with these individuals.

Landmark Distance Impacts the Overshadowing Effect in Spatial Learning Using a Virtual Water Maze Task with Healthy Adults

Cue competition is a key element of many associative theories of learning. Overshadowing, an important aspect of cue competition, is a phenomenon in which learning about a cue is reduced when it is accompanied by a second cue. Overshadowing has been observed across many domains, but there has been limited investigation of overshadowing in human spatial learning. This experiment explored overshadowing using two landmarks/cues (at different distances to the goal) in a virtual water maze task with young, healthy adult participants. Experiment 1 initially examined whether the cues used were equally salient. Results indicated that both gained equal control over performance. In experiment 2, overshadowing was examined using the two cues from experiment 1. Results indicated that overshadowing occurred during spatial learning and that the near cue controlled searching significantly more than the far cue. Furthermore, the far cue appeared to have been completely ignored, suggesting that learning strategies requiring the least amount of effort were employed by participants. Evidence supporting an associative account of human spatial navigation and the influence of proximal cues was discussed.

NavWell: A simplified virtual-reality platform for spatial navigation and memory experiments

Being able to navigate, recall important locations, and find the way home are critical skills, essential for survival for both humans and animals. These skills can be examined in the laboratory using the Morris water maze, often considered the gold standard test of animal navigation. In this task, animals are required to locate and recall the location of an escape platform hidden in a pool filled with water. Because animals can not see the platform directly, they must use various landmarks in the environment to escape. With recent advances in technology and virtual reality (VR), many tasks originally used in the animal literature can now be translated for human studies. The virtual water maze task is no exception. However, a number of issues are associated with these mazes, including cost, lack of flexibility, and lack of standardization in terms of experimental designs and procedures. Here we present a virtual water maze system (NavWell) that is readily downloadable and free to use. The system allows for the easy design of experiments and the testing of participants on a desktop computer or fully immersive VR environment. The data from four independent experiments are presented in order to validate the software. From these experiments, a set of procedures for use with a number of well-known memory tests is suggested. This potentially can help with the standardization of navigational research and with navigational testing in the clinic or in an educational environment. Finally, we discuss the limitations of the software and plans for its development and future use.

Hippocampal and prefrontal contributions to memory retrieval: Examination of immediate early gene, NMDA receptor and environmental interactions

Animals can use a range of strategies to recall important locations. These include simple stimulus-response strategies and more complex spatial (place) strategies, which are thought to have distinct neural substrates. The hippocampus-and NMDA receptor activation therein-is considered to be crucial for spatial, but not response strategies. The medial prefrontal cortex has also been implicated in memory retrieval; however, evidence concerning its specific role is equivocal. Both hippocampal and prefrontal regions have been associated with flexible behavioural responding (e.g. when task demands change). Here, we investigated the use of spatial and non-spatial strategies in the Morris water maze and their associated brain areas in rats using immediate early gene (IEG) imaging of Zif268 and c-Fos. Specifically, we charted the involvement of hippocampal and prefrontal subregions during retrieval of spatial and non-spatial memories. Behavioural flexibility was also examined using intact and partial cue configurations during recall. Results indicated that regions of both the hippocampus (area CA3) and prefrontal cortex (anterior cingulate cortex) were preferentially engaged in spatial memory recall compared to response learning. In addition, both spatial and non-spatial memories were dependent on NMDA receptor activation. MK801 impaired recall performance across all groups and reduced IEG activation across hippocampal and prefrontal regions. Finally, IEG results revealed divergent patterns of Zif268 and c-Fos activity and support the suggestion that Zif268 plays a functional role in the recall of long-term memories.

Understanding the role of distance, direction and cue salience in an associative model of landmark learning

Navigation and spatial memory relies on the ability to use and recall environmental landmarks relative to important locations. Such learning is thought to result from the strengthening of associations between the goal location and environmental cues. Factors that contribute to the strength of this association include cue stability, saliency and cue location. Here we combine an autoregressive random walk model, that describes goal-directed swimming behaviour, with an associative learning model to provide an integrated model of landmark learning, using the water maze task. The model allows for the contribution of each cue, the salience and the vector information provided (both distance and directional) to be separately analysed. The model suggests that direction and distance information are independent components and can influence searching patterns. Importantly, the model can also be used to simulate various experimental scenarios to understand what has been learnt in relation to the cues, thereby offering new insights into how animals navigate.

Efficiency: an underlying principle of learning?

Learning is essential. It allows animals to change circumstances, deal with new situations and adapt to environments. Here, we argue that learning, at behavioral and neural levels, involves efficiency, reflected in metabolic cost reductions. Behaviourally, although multiple solutions to a novel problem may be available, all solutions are not learnt – it is too costly. Furthermore, once a strategy has been selected, it is reinforced producing an efficiency that leads to a maximisation of performance and metabolic cost reductions. Learning can be represented in the brain through many mechanisms; however, if learning is truly efficient, then, all such mechanisms should also be accompanied by a reduction in measurable metabolic costs. By thinking about learning in terms of efficiency, not simply as a descriptive term but rather in terms of metabolic costs, it allows learning to be examined more carefully and provides predictions that can be easily tested (and indeed refuted).