Brain Responses Track Patterns in Sound

This thesis uses specifically structured sound sequences, with electroencephalography (EEG) recording and behavioural tasks, to understand how the brain forms and updates a model of the auditory world. Experimental chapters 3-7 address different effects arising from statistical predictability, stimulus repetition and surprise. Stimuli comprised tone sequences, with frequencies varying in regular or random patterns. In Chapter 3, EEG data demonstrate fast recognition of predictable patterns, shown by an increase in responses to regular relative to random sequences. Behavioural experiments investigate attentional capture by stimulus structure, suggesting that regular sequences are easier to ignore. Responses to repetitive stimulation generally exhibit suppression, thought to form a building block of regularity learning. However, the patterns used in this thesis show the opposite effect, where predictable patterns show a strongly enhanced brain response, compared to frequency-matched random sequences. Chapter 4 presents a study which reconciles auditory sequence predictability and repetition in a single paradigm. Results indicate a system for automatic predictability monitoring which is distinct from, but concurrent with, repetition suppression. The brain’s internal model can be investigated via the response to rule violations. Chapters 5 and 6 present behavioural and EEG experiments where violations are inserted in the sequences. Outlier tones within regular sequences evoked a larger response than matched outliers in random sequences. However, this effect was not present when the violation comprised a silent gap. Chapter 7 concerns the ability of the brain to update an existing model. Regular patterns transitioned to a different rule, keeping the frequency content constant. Responses show a period of adjustment to the rule change, followed by a return to tracking the predictability of the sequence. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information

Animal population decline and recovery after severe fire: Relating ecological and life history traits with expert estimates of population impacts from the Australian 2019-20 megafires

Catastrophic megafires can increase extinction risks identifying species priorities for management and policy support is critical for preparing and responding to future fires. However, empirical data on population loss and recovery post-fire, especially megafire, are limited and taxonomically biased. These gaps could be bridged if species' morphological, behavioural, ecological and life history traits indicated their fire responses. Using expert elicitation that estimated population changes following the 2019–20 Australian megafires for 142 terrestrial and aquatic animal species (from every vertebrate class, one invertebrate group), we examined whether expert estimates of fire-related mortality, mortality in the year post-fire, and recovery trajectories over 10 years/three generations post-fire, were related to species traits. Expert estimates for fire-related mortality were lower for species that could potentially flee or shelter from fire, and that associated with fire-prone habitats. Post-fire mortality estimates were linked to diet, diet specialisation, home range size, and susceptibility to introduced herbivores that damage or compete for resources. Longer-term population recovery estimates were linked to diet/habitat specialisation, susceptibility to introduced species species with slower life histories and shorter subadult dispersal distances also had lower recovery estimates. Across animal groups, experts estimated that recovery was poorest for species with pre-fire population decline and more threatened conservation status. Sustained management is likely needed to recover species with habitat and diet specialisations, slower life histories, pre-existing declines and threatened conservation statuses. This study shows that traits could help inform management priorities before and after future megafires, but further empirical data on animal fire response is essential