title: Does Familiarity breed inattention? Why drivers crash on the roads they know best

authors: Charlton, Samuel G.

subjects: driver attention

abstract: This paper describes our research into the nature of everyday driving, with a particular emphasis on the processes that govern driver behaviour in familiar, well - practiced situations. The research examined the development and maintenance of proceduralised driving habits in a high-fidelity driving simulator by paying 29 participants to drive a simulated road regularly over three months of testing. A range of measures, including detection task performance and driving performance were collected over the course of 20 sessions. Performance from a yoked control group who experienced the same road scenarios in a single session was also measured. The data showed the development of stereotyped driving patterns and changes in what drivers noticed, indicative of in attentional blindness and “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to foveal road features associated with vehicle guidance and performance on an embedded vehicle detection task (detection of a specific vehicle type). The changes in attentional focus and driving performance resulting from extended practice help explain why drivers are at increased risk of crashing on roads they know well. Identifying the features of familiar roads that attract driver attention, even when they are driving without awareness, can inform new interventions and designs for safer roads. The data also provide new light on a range of previous driver behaviour research including a “Tandem Model” that includes both explicit and implicit processes involved in driving performance.

text: Does Familiarity breed inattention? Why drivers crash on the roads they know best This paper describes our research into the nature of everyday driving, with a particular emphasis on the processes that govern driver behaviour in familiar, well - practiced situations. The research examined the development and maintenance of proceduralised driving habits in a high-fidelity driving simulator by paying 29 participants to drive a simulated road regularly over three months of testing. A range of measures, including detection task performance and driving performance were collected over the course of 20 sessions. Performance from a yoked control group who experienced the same road scenarios in a single session was also measured. The data showed the development of stereotyped driving patterns and changes in what drivers noticed, indicative of in attentional blindness and “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to foveal road features associated with vehicle guidance and performance on an embedded vehicle detection task (detection of a specific vehicle type). The changes in attentional focus and driving performance resulting from extended practice help explain why drivers are at increased risk of crashing on roads they know well. Identifying the features of familiar roads that attract driver attention, even when they are driving without awareness, can inform new interventions and designs for safer roads. The data also provide new light on a range of previous driver behaviour research including a “Tandem Model” that includes both explicit and implicit processes involved in driving performance.

year: 2012

title: Naive Bayes for text classification with unbalanced classes

authors: Frank, Eibe

subjects: Science & Technology

abstract: Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper, we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.

text: Naive Bayes for text classification with unbalanced classes Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper, we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.

year: 2006-01-01

title: Lessons for teachers through intergenerational Māori experiences in Aotearoa’s education system

authors: Virtue, Katie

abstract: The intention of this research is to serve as professional development for myself and fellow teachers who work with tamariki Māori (Māori children) within mainstream education contexts in Aotearoa. This research explores historical factors that have contributed to the academic disparities of Māori including colonisation, the doctrine of discovery and contention over the differing versions of the Treaty of Waitangi. This research then shifts from holding the coloniser at the centre of the narrative to positioning Māori at the centre as exemplary examples of relentless Māori resistance. More contemporary examples of educational policies are then presented and analysed as an attempt to better understand these contexts.
What is blatantly clear through the exploration of educational policies throughout Aotearoa’s post-colonial history is the absence of Māori voice. For this reason, the findings of this thesis are centred on the voices of four wāhine Māori (Māori Women), across three generations, and their experiences with different teachers through Aotearoa’s education system.  
These stories provide invaluable lessons for all educators in Aotearoa.

text: Lessons for teachers through intergenerational Māori experiences in Aotearoa’s education system The intention of this research is to serve as professional development for myself and fellow teachers who work with tamariki Māori (Māori children) within mainstream education contexts in Aotearoa. This research explores historical factors that have contributed to the academic disparities of Māori including colonisation, the doctrine of discovery and contention over the differing versions of the Treaty of Waitangi. This research then shifts from holding the coloniser at the centre of the narrative to positioning Māori at the centre as exemplary examples of relentless Māori resistance. More contemporary examples of educational policies are then presented and analysed as an attempt to better understand these contexts.
What is blatantly clear through the exploration of educational policies throughout Aotearoa’s post-colonial history is the absence of Māori voice. For this reason, the findings of this thesis are centred on the voices of four wāhine Māori (Māori Women), across three generations, and their experiences with different teachers through Aotearoa’s education system.  
These stories provide invaluable lessons for all educators in Aotearoa.

year: 2021