The role of perirhinal cortex in visual discrimination learning for visual secondary reinforcement in rats

Perirhinal cortex in monkeys has been thought to be involved in visual associative learning. The authors examined rats' ability to make associations between visual stimuli in a visual secondary reinforcement task. Rats learned 2-choice visual discriminations for secondary visual reinforcement. They showed significant learning of discriminations before any primary reinforcement. Following bilateral perirhinal cortex lesions, rats continued to learn visual discriminations for visual secondary reinforcement at the same rate as before surgery. Thus, this study does not support a critical role of perirhinal cortex in learning for visual secondary reinforcement. Contrasting this result with other positive results, the authors suggest that the role of perirhinal cortex is in "within-object" associations and that it plays a much lesser role in stimulus-stimulus associations between objects.

Improvement of classification accuracy for stochastic discrimination - Multi-class classification

In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.

Optimal discrimination and classification of THz spectra in the wavelet domain

In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain. An optimization procedure results in a further improvement of the wavelet classifier. The procedure is suitable for enhancing the contrast or classifying spectra acquired by either continuous wave or THz transient spectrometers as well as for increasing the dynamic range of THz imaging systems. (C) 2003 Optical Society of America.

On improvement of classification accuracy for stochastic discrimination

Stochastic discrimination (SD) depends on a discriminant function for classification. In this paper, an improved SD is introduced to reduce the error rate of the standard SD in the context of a two-class classification problem. The learning procedure of the improved SD consists of two stages. Initially a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. Then the standard SD is modified by 1) restricting sampling in the important space, and 2) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but with a smaller variance than that of the standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples are provided to demonstrate the effectiveness of the proposed improved SD.

‘That’s definite discrimination’: practice under the umbrella of inclusion

Inclusive practice is well embedded across society and has developed over time. However, although policy and public view have moved forward, the way organisations address the agenda for inclusion often represents a superficial interpretation of this concept. Qualitative data were gathered using new ethnography to explore the experiences of a library-based reading group for visually impaired readers. The voices of the individuals shed light on the individual and collective experience of reading. These insights challenge the traditional views of distinct provision that are designed to address targets for inclusion of individuals with disabilities. We argue for a clearer focus on the unintentional consequences of practice in the name of inclusion that leave individuals feeling marginalised. This paper suggests the alternative focus on social justice as offering a discourse that focuses on society and away from the individual.