Emotion recognition: The role of featural and configural face information

Several studies investigated the role of featural and configural information when processing facial identity. A lot less is known about their contribution to emotion recognition. In this study, we addressed this issue by inducing either a featural or a configural processing strategy (Experiment 1) and by investigating the attentional strategies in response to emotional expressions (Experiment 2). In Experiment 1, participants identified emotional expressions in faces that were presented in three different versions (intact, blurred, and scrambled) and in two orientations (upright and inverted). Blurred faces contain mainly configural information, and scrambled faces contain mainly featural information. Inversion is known to selectively hinder configural processing. Analyses of the discriminability measure (A′) and response times (RTs) revealed that configural processing plays a more prominent role in expression recognition than featural processing, but their relative contribution varies depending on the emotion. In Experiment 2, we qualified these differences between emotions by investigating the relative importance of specific features by means of eye movements. Participants had to match intact expressions with the emotional cues that preceded the stimulus. The analysis of eye movements confirmed that the recognition of different emotions rely on different types of information. While the mouth is important for the detection of happiness and fear, the eyes are more relevant for anger, fear, and sadness.

Detection and pattern recognition applied to leaves and chromosomes

The Project you are about to see it is based on the technologies used on object detection and recognition, especially on leaves and chromosomes. To do so, this document contains the typical parts of a scientific paper, as it is what it is. It is composed by an Abstract, an Introduction, points that have to do with the investigation area, future work, conclusions and references used for the elaboration of the document. The Abstract talks about what are we going to find in this paper, which is technologies employed on pattern detection and recognition for leaves and chromosomes and the jobs that are already made for cataloguing these objects. In the introduction detection and recognition meanings are explained. This is necessary as many papers get confused with these terms, specially the ones talking about chromosomes. Detecting an object is gathering the parts of the image that are useful and eliminating the useless parts. Summarizing, detection would be recognizing the objects borders. When talking about recognition, we are talking about the computers or the machines process, which says what kind of object we are handling. Afterwards we face a compilation of the most used technologies in object detection in general. There are two main groups on this category: Based on derivatives of images and based on ASIFT points. The ones that are based on derivatives of images have in common that convolving them with a previously created matrix does the treatment of them. This is done for detecting borders on the images, which are changes on the intensity of the pixels. Within these technologies we face two groups: Gradian based, which search for maximums and minimums on the pixels intensity as they only use the first derivative. The Laplacian based methods search for zeros on the pixels intensity as they use the second derivative. Depending on the level of details that we want to use on the final result, we will choose one option or the other, because, as its logic, if we used Gradian based methods, the computer will consume less resources and less time as there are less operations, but the quality will be worse. On the other hand, if we use the Laplacian based methods we will need more time and resources as they require more operations, but we will have a much better quality result. After explaining all the derivative based methods, we take a look on the different algorithms that are available for both groups. The other big group of technologies for object recognition is the one based on ASIFT points, which are based on 6 image parameters and compare them with another image taking under consideration these parameters. These methods disadvantage, for our future purposes, is that it is only valid for one single object. So if we are going to recognize two different leaves, even though if they refer to the same specie, we are not going to be able to recognize them with this method. It is important to mention these types of technologies as we are talking about recognition methods in general. At the end of the chapter we can see a comparison with pros and cons of all technologies that are employed. Firstly comparing them separately and then comparing them all together, based on our purposes. Recognition techniques, which are the next chapter, are not really vast as, even though there are general steps for doing object recognition, every single object that has to be recognized has its own method as the are different. This is why there is not a general method that we can specify on this chapter. We now move on into leaf detection techniques on computers. Now we will use the technique explained above based on the image derivatives. Next step will be to turn the leaf into several parameters. Depending on the document that you are referring to, there will be more or less parameters. Some papers recommend to divide the leaf into 3 main features (shape, dent and vein] and doing mathematical operations with them we can get up to 16 secondary features. Next proposition is dividing the leaf into 5 main features (Diameter, physiological length, physiological width, area and perimeter] and from those, extract 12 secondary features. This second alternative is the most used so it is the one that is going to be the reference. Following in to leaf recognition, we are based on a paper that provides a source code that, clicking on both leaf ends, it automatically tells to which specie belongs the leaf that we are trying to recognize. To do so, it only requires having a database. On the tests that have been made by the document, they assure us a 90.312% of accuracy over 320 total tests (32 plants on the database and 10 tests per specie]. Next chapter talks about chromosome detection, where we shall pass the metaphasis plate, where the chromosomes are disorganized, into the karyotype plate, which is the usual view of the 23 chromosomes ordered by number. There are two types of techniques to do this step: the skeletonization process and swiping angles. Skeletonization progress consists on suppressing the inside pixels of the chromosome to just stay with the silhouette. This method is really similar to the ones based on the derivatives of the image but the difference is that it doesnt detect the borders but the interior of the chromosome. Second technique consists of swiping angles from the beginning of the chromosome and, taking under consideration, that on a single chromosome we cannot have more than an X angle, it detects the various regions of the chromosomes. Once the karyotype plate is defined, we continue with chromosome recognition. To do so, there is a technique based on the banding that chromosomes have (grey scale bands] that make them unique. The program then detects the longitudinal axis of the chromosome and reconstructs the band profiles. Then the computer is able to recognize this chromosome. Concerning the future work, we generally have to independent techniques that dont reunite detection and recognition, so our main focus would be to prepare a program that gathers both techniques. On the leaf matter we have seen that, detection and recognition, have a link as both share the option of dividing the leaf into 5 main features. The work that would have to be done is to create an algorithm that linked both methods, as in the program, which recognizes leaves, it has to be clicked both leaf ends so it is not an automatic algorithm. On the chromosome side, we should create an algorithm that searches for the beginning of the chromosome and then start to swipe angles, to later give the parameters to the program that searches for the band profiles. Finally, on the summary, we explain why this type of investigation is needed, and that is because with global warming, lots of species (animals and plants] are beginning to extinguish. That is the reason why a big database, which gathers all the possible species, is needed. For recognizing animal species, we just only have to have the 23 chromosomes. While recognizing a plant, there are several ways of doing it, but the easiest way to input a computer is to scan the leaf of the plant. RESUMEN. El proyecto que se puede ver a continuación trata sobre las tecnologías empleadas en la detección y reconocimiento de objetos, especialmente de hojas y cromosomas. Para ello, este documento contiene las partes típicas de un paper de investigación, puesto que es de lo que se trata. Así, estará compuesto de Abstract, Introducción, diversos puntos que tengan que ver con el área a investigar, trabajo futuro, conclusiones y biografía utilizada para la realización del documento. Así, el Abstract nos cuenta qué vamos a poder encontrar en este paper, que no es ni más ni menos que las tecnologías empleadas en el reconocimiento y detección de patrones en hojas y cromosomas y qué trabajos hay existentes para catalogar a estos objetos. En la introducción se explican los conceptos de qué es la detección y qué es el reconocimiento. Esto es necesario ya que muchos papers científicos, especialmente los que hablan de cromosomas, confunden estos dos términos que no podían ser más sencillos. Por un lado tendríamos la detección del objeto, que sería simplemente coger las partes que nos interesasen de la imagen y eliminar aquellas partes que no nos fueran útiles para un futuro. Resumiendo, sería reconocer los bordes del objeto de estudio. Cuando hablamos de reconocimiento, estamos refiriéndonos al proceso que tiene el ordenador, o la máquina, para decir qué clase de objeto estamos tratando. Seguidamente nos encontramos con un recopilatorio de las tecnologías más utilizadas para la detección de objetos, en general. Aquí nos encontraríamos con dos grandes grupos de tecnologías: Las basadas en las derivadas de imágenes y las basadas en los puntos ASIFT. El grupo de tecnologías basadas en derivadas de imágenes tienen en común que hay que tratar a las imágenes mediante una convolución con una matriz creada previamente. Esto se hace para detectar bordes en las imágenes que son básicamente cambios en la intensidad de los píxeles. Dentro de estas tecnologías nos encontramos con dos grupos: Los basados en gradientes, los cuales buscan máximos y mínimos de intensidad en la imagen puesto que sólo utilizan la primera derivada; y los Laplacianos, los cuales buscan ceros en la intensidad de los píxeles puesto que estos utilizan la segunda derivada de la imagen. Dependiendo del nivel de detalles que queramos utilizar en el resultado final nos decantaremos por un método u otro puesto que, como es lógico, si utilizamos los basados en el gradiente habrá menos operaciones por lo que consumirá más tiempo y recursos pero por la contra tendremos menos calidad de imagen. Y al revés pasa con los Laplacianos, puesto que necesitan más operaciones y recursos pero tendrán un resultado final con mejor calidad. Después de explicar los tipos de operadores que hay, se hace un recorrido explicando los distintos tipos de algoritmos que hay en cada uno de los grupos. El otro gran grupo de tecnologías para el reconocimiento de objetos son los basados en puntos ASIFT, los cuales se basan en 6 parámetros de la imagen y la comparan con otra imagen teniendo en cuenta dichos parámetros. La desventaja de este método, para nuestros propósitos futuros, es que sólo es valido para un objeto en concreto. Por lo que si vamos a reconocer dos hojas diferentes, aunque sean de la misma especie, no vamos a poder reconocerlas mediante este método. Aún así es importante explicar este tipo de tecnologías puesto que estamos hablando de técnicas de reconocimiento en general. Al final del capítulo podremos ver una comparación con los pros y las contras de todas las tecnologías empleadas. Primeramente comparándolas de forma separada y, finalmente, compararemos todos los métodos existentes en base a nuestros propósitos. Las técnicas de reconocimiento, el siguiente apartado, no es muy extenso puesto que, aunque haya pasos generales para el reconocimiento de objetos, cada objeto a reconocer es distinto por lo que no hay un método específico que se pueda generalizar. Pasamos ahora a las técnicas de detección de hojas mediante ordenador. Aquí usaremos la técnica explicada previamente explicada basada en las derivadas de las imágenes. La continuación de este paso sería diseccionar la hoja en diversos parámetros. Dependiendo de la fuente a la que se consulte pueden haber más o menos parámetros. Unos documentos aconsejan dividir la morfología de la hoja en 3 parámetros principales (Forma, Dentina y ramificación] y derivando de dichos parámetros convertirlos a 16 parámetros secundarios. La otra propuesta es dividir la morfología de la hoja en 5 parámetros principales (Diámetro, longitud fisiológica, anchura fisiológica, área y perímetro] y de ahí extraer 12 parámetros secundarios. Esta segunda propuesta es la más utilizada de todas por lo que es la que se utilizará. Pasamos al reconocimiento de hojas, en la cual nos hemos basado en un documento que provee un código fuente que cucando en los dos extremos de la hoja automáticamente nos dice a qué especie pertenece la hoja que estamos intentando reconocer. Para ello sólo hay que formar una base de datos. En los test realizados por el citado documento, nos aseguran que tiene un índice de acierto del 90.312% en 320 test en total (32 plantas insertadas en la base de datos por 10 test que se han realizado por cada una de las especies]. El siguiente apartado trata de la detección de cromosomas, en el cual se debe de pasar de la célula metafásica, donde los cromosomas están desorganizados, al cariotipo, que es como solemos ver los 23 cromosomas de forma ordenada. Hay dos tipos de técnicas para realizar este paso: Por el proceso de esquelotonización y barriendo ángulos. El proceso de esqueletonización consiste en eliminar los píxeles del interior del cromosoma para quedarse con su silueta; Este proceso es similar a los métodos de derivación de los píxeles pero se diferencia en que no detecta bordes si no que detecta el interior de los cromosomas. La segunda técnica consiste en ir barriendo ángulos desde el principio del cromosoma y teniendo en cuenta que un cromosoma no puede doblarse más de X grados detecta las diversas regiones de los cromosomas. Una vez tengamos el cariotipo, se continua con el reconocimiento de cromosomas. Para ello existe una técnica basada en las bandas de blancos y negros que tienen los cromosomas y que son las que los hacen únicos. Para ello el programa detecta los ejes longitudinales del cromosoma y reconstruye los perfiles de las bandas que posee el cromosoma y que lo identifican como único. En cuanto al trabajo que se podría desempeñar en el futuro, tenemos por lo general dos técnicas independientes que no unen la detección con el reconocimiento por lo que se habría de preparar un programa que uniese estas dos técnicas. Respecto a las hojas hemos visto que ambos métodos, detección y reconocimiento, están vinculados debido a que ambos comparten la opinión de dividir las hojas en 5 parámetros principales. El trabajo que habría que realizar sería el de crear un algoritmo que conectase a ambos ya que en el programa de reconocimiento se debe clicar a los dos extremos de la hoja por lo que no es una tarea automática. En cuanto a los cromosomas, se debería de crear un algoritmo que busque el inicio del cromosoma y entonces empiece a barrer ángulos para después poder dárselo al programa que busca los perfiles de bandas de los cromosomas. Finalmente, en el resumen se explica el por qué hace falta este tipo de investigación, esto es que con el calentamiento global, muchas de las especies (tanto animales como plantas] se están empezando a extinguir. Es por ello que se necesitará una base de datos que contemple todas las posibles especies tanto del reino animal como del reino vegetal. Para reconocer a una especie animal, simplemente bastará con tener sus 23 cromosomas; mientras que para reconocer a una especie vegetal, existen diversas formas. Aunque la más sencilla de todas es contar con la hoja de la especie puesto que es el elemento más fácil de escanear e introducir en el ordenador.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Recognition of Facial Expressions with Autoencoders and Convolutional-Nets

Les humains communiquent via différents types de canaux: les mots, la voix, les gestes du corps, des émotions, etc. Pour cette raison, un ordinateur doit percevoir ces divers canaux de communication pour pouvoir interagir intelligemment avec les humains, par exemple en faisant usage de microphones et de webcams. Dans cette thèse, nous nous intéressons à déterminer les émotions humaines à partir d’images ou de vidéo de visages afin d’ensuite utiliser ces informations dans différents domaines d’applications. Ce mémoire débute par une brève introduction à l'apprentissage machine en s’attardant aux modèles et algorithmes que nous avons utilisés tels que les perceptrons multicouches, réseaux de neurones à convolution et autoencodeurs. Elle présente ensuite les résultats de l'application de ces modèles sur plusieurs ensembles de données d'expressions et émotions faciales. Nous nous concentrons sur l'étude des différents types d’autoencodeurs (autoencodeur débruitant, autoencodeur contractant, etc) afin de révéler certaines de leurs limitations, comme la possibilité d'obtenir de la coadaptation entre les filtres ou encore d’obtenir une courbe spectrale trop lisse, et étudions de nouvelles idées pour répondre à ces problèmes. Nous proposons également une nouvelle approche pour surmonter une limite des autoencodeurs traditionnellement entrainés de façon purement non-supervisée, c'est-à-dire sans utiliser aucune connaissance de la tâche que nous voulons finalement résoudre (comme la prévision des étiquettes de classe) en développant un nouveau critère d'apprentissage semi-supervisé qui exploite un faible nombre de données étiquetées en combinaison avec une grande quantité de données non-étiquetées afin d'apprendre une représentation adaptée à la tâche de classification, et d'obtenir une meilleure performance de classification. Finalement, nous décrivons le fonctionnement général de notre système de détection d'émotions et proposons de nouvelles idées pouvant mener à de futurs travaux.

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

The development and evaluation of a computer-based program to test and to teach the recognition of facial affect

Autism is a chronic pervasive neurodevelopmental disorder characterized by the early onset of social and communicative impairments as well as restricted, ritualized, stereotypic behavior. The endophenotype of autism includes neuropsychological deficits, for instance a lack of "Theory of Mind" and problems recognizing facial affect. In this study, we report the development and evaluation of a computer-based program to teach and test the ability to identify basic facially expressed emotions. 10 adolescent or adult subjects with high-functioning autism or Asperger-syndrome were included in the investigation. A priori the facial affect recognition test had shown good psychometric properties in a normative sample (internal consistency: rtt=.91-.95; retest reliability: rtt=.89-.92). In a prepost design, one half of the sample was randomly assigned to receive computer treatment while the other half of the sample served as control group. The training was conducted for five weeks, consisting of two hours training a week. The trained individuals improved significantly on the affect recognition task, but not on any other measure. Results support the usefulness of the program to teach the detection of facial affect. However, the improvement found is limited to a circumscribed area of social-communicative function and generalization is not ensured.

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.

Development of a hand-gesture recognition system for human-computer interaction

The aim of this Master Thesis is the analysis, design and development of a robust and reliable Human-Computer Interaction interface, based on visual hand-gesture recognition. The implementation of the required functions is oriented to the simulation of a classical hardware interaction device: the mouse, by recognizing a specific hand-gesture vocabulary in color video sequences. For this purpose, a prototype of a hand-gesture recognition system has been designed and implemented, which is composed of three stages: detection, tracking and recognition. This system is based on machine learning methods and pattern recognition techniques, which have been integrated together with other image processing approaches to get a high recognition accuracy and a low computational cost. Regarding pattern recongition techniques, several algorithms and strategies have been designed and implemented, which are applicable to color images and video sequences. The design of these algorithms has the purpose of extracting spatial and spatio-temporal features from static and dynamic hand gestures, in order to identify them in a robust and reliable way. Finally, a visual database containing the necessary vocabulary of gestures for interacting with the computer has been created.

On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

Multiple face detection at different resolutions for perceptual user interfaces

[EN]This paper describes in detail a real-time multiple face detection system for video streams. The system adds to the good performance provided by a window shift approach, the combination of different cues available in video streams due to temporal coherence. The results achieved by this combined solution outperform the basic face detector obtaining a 98% success rate for around 27000 images, providing additionally eye detection and a relation between the successive detections in time by means of detection threads.

Speech recognition in noise for cochlear implant listeners: Benefits of residual hearing

The purpose of this study was to explore the potential advantages, both theoretical and applied, of preserving low-frequency acoustic hearing in cochlear implant patients. Several hypotheses are presented that predict that residual low-frequency acoustic hearing along with electric stimulation for high frequencies will provide an advantage over traditional long-electrode cochlear implants for the recognition of speech in competing backgrounds. A simulation experiment in normal-hearing subjects demonstrated a clear advantage for preserving low-frequency residual acoustic hearing for speech recognition in a background of other talkers, but not in steady noise. Three subjects with an implanted "short-electrode" cochlear implant and preserved low-frequency acoustic hearing were also tested on speech recognition in the same competing backgrounds and compared to a larger group of traditional cochlear implant users. Each of the three short-electrode subjects performed better than any of the traditional long-electrode implant subjects for speech recognition in a background of other talkers, but not in steady noise, in general agreement with the simulation studies. When compared to a subgroup of traditional implant users matched according to speech recognition ability in quiet, the short-electrode patients showed a 9-dB advantage in the multitalker background. These experiments provide strong preliminary support for retaining residual low-frequency acoustic hearing in cochlear implant patients. The results are consistent with the idea that better perception of voice pitch, which can aid in separating voices in a background of other talkers, was responsible for this advantage.

NEURAL CORRELATES OF SCENT MARKING BEHAVIOR IN C57BL/6J MICE: DETECTION AND RECOGNITION OF A SOCIAL STIMULUS

Mice show urinary scent marking behavior as a form of social communication. Marking to a conspecific stimulus mouse or odor varies with stimulus familiarity, indicating discrimination of novel and familiar animals. This study investigated Fos immunoreactivity in inbred C57BL/6J (C57) males following scent marking behavior in response to detection of a social stimulus, or discrimination between a familiar and an unfamiliar conspecific. In Experiment 1 C57 mice were exposed for four daily trials to an empty chamber; on a test day they were exposed to the same chamber or to a male CD-1 mouse in that chamber. Increased scent marking to the CD-1 mouse was associated with increased Fos-immunoreactive cells in the basolateral amygdala, medial amygdala, and dorsal and ventral premammillary nuclei. In Experiment 2 C57 mice were habituated to a CD-1 male for 4 consecutive days and, on the 5th day, exposed to the same CD-1 male, or to a novel CD-1 male. Mice exposed to a novel CD-1 displayed a significant increase in scent marking compared to their last exposure to the familiar stimulus, indicating discrimination of the novelty of this social stimulus. Marking to the novel stimulus was associated with enhanced activation of several telencephalic, as well as hypothalamic and midbrain, structures in which activation had not been seen in the detection paradigm (Experiment 1). These included medial prefrontal and piriform cortices, and lateral septum; the paraventricular nuclei, ventromedial nuclei, and lateral area of the hypothalamus, and the ventrolateral column of the periaqueductal gray. These data suggest that a circumscribed group of structures largely concerned with olfaction is involved in detection of a conspecific olfactory stimulus, whereas discrimination of a novel vs. a familiar conspecific stimulus engages a wider range of forebrain structures encompassing higher-order processes and potentially providing an interface between cognitions and emotions. (C) 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

Parafoveal letter recognition at reduced contrast in normal aging and in patients with risk factors for AMD

Background Patients with early age-related maculopathy ( ARM) do not necessarily show obvious morphological signs or functional impairment. Many have good visual acuity, yet complain of decreased visual performance. The aim of this study was to investigate the aging effects on performance of parafoveal letter recognition at reduced contrast, and defects caused by early ARM and normal fellow eyes of patients with unilateral age-related macular degeneration (nfAMD). Methods Testing of the central visual field (8 radius) was performed by the Macular Mapping Test (MMT) using recognition of letters in 40 parafoveal target locations at four contrast levels (5, 10, 25 and 100%). Effects of aging were investigated in 64 healthy subjects aged 23 to 76 years (CTRL). In addition, 39 eyes (minimum visual acuity of 0.63; 20/30) from 39 patients with either no visible signs of ARM, while the fellow eye had advanced age-related macular degeneration (nfAMD; n=12), or early signs of ARM (eARM; n=27) were examined. Performance was expressed summarily as a ""field score"" (FS). Results Performance in the MMT begins to decline linearly with age in normal subjects from the age of 50 and 54 years on, at 5% and 10% contrast respectively. The differentiation between patients and CTRLs was enhanced if FS at 5% was analyzed along with FS at 10% contrast. In 8/12 patients from group nfAMD and in 18/27 from group eARM, the FS was statistically significantly lower than in the CTRL group in at least one of the lower contrast levels. Conclusion Using parafoveal test locations, a recognition task and diminished contrast increases the chance of early detection of functional defects due to eARM or nfAMD and can differentiate them from those due to aging alone.