Spectral content as a cue to perceived auditory distance.

Changes in the spectral content of wide-band auditory stimuli have been repeatedly implicated as a possible cue to the distance of a sound source. Few of the previous studies of this factor, however, have considered whether the cue provided by spectral content serves as an absolute or a relative cue. That is, can differences in spectral content indicate systematic differences in distance even on their first presentation to a listener, or must the listener be able to compare sounds with one another in order to perceive some change in their distances? An attempt to answer this question and simultaneously to evaluate the possibly confounding influence of changes in the sound level and/or the loudness of the stimuli are described in this paper. The results indicate that a decrease in high-frequency content (as might physically be produced by passage through a greater amount of air) can lead to increases in perceived auditory distance, but only when compared with similar sounds having a somewhat different high-frequency content, ie spectral information can serve as a relative cue for auditory distance, independent of changes in overall sound level.     

Preventing Suicide at Suicide Hotspots: A Case Study from Australia

The Gap Park Self‐Harm Minimisation Masterplan project is a collaborative attempt to address jumping suicides at Sydney's Gap Park through means restriction, encouraging help‐seeking, and increasing the likelihood of third‐party intervention. We used various data sources to describe the Masterplan project's processes, impacts, and outcomes. There have been reductions in reported jumps and confirmed suicides, although the trends are not statistically significant. There has been a significant increase in police call‐outs to intervene with suicidal people who have not yet reached the cliff's edge. The collaborative nature of the Masterplan project and its multifaceted approach appear to be reaping benefits.     

Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real-World Problem-Solving Task

This article presents a computational model of the learning of diagnostic knowledge, based on observations of human operators engaged in real-world troubleshooting tasks. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem-solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model not only addresses issues in human learning, but, in addition, is computationally justified as a uniform, extensible framework for multistrategy learning.