On the difficulty of feature-based attentional modulations in visual object recognition: A modeling study.

Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural level, attention increases firing rates of neurons in visual cortex whose preferred stimulus is currently attended to. However, it is not yet known how these two phenomena are linked, i.e., how the visual system could be "tuned" in a task-dependent fashion to improve task performance. To answer this question, we performed simulations with the HMAX model of object recognition in cortex [45]. We modulated firing rates of model neurons in accordance with experimental results about effects of feature-based attention on single neurons and measured changes in the model's performance in a variety of object recognition tasks. It turned out that recognition performance could only be improved under very limited circumstances and that attentional influences on the process of object recognition per se tend to display a lack of specificity or raise false alarm rates. These observations lead us to postulate a new role for the observed attention-related neural response modulations.

Geometric Aspects of Visual Object Recognition

This thesis presents there important results in visual object recognition based on shape. (1) A new algorithm (RAST; Recognition by Adaptive Sudivisions of Tranformation space) is presented that has lower average-case complexity than any known recognition algorithm. (2) It is shown, both theoretically and empirically, that representing 3D objects as collections of 2D views (the "View-Based Approximation") is feasible and affects the reliability of 3D recognition systems no more than other commonly made approximations. (3) The problem of recognition in cluttered scenes is considered from a Bayesian perspective; the commonly-used "bounded-error errorsmeasure" is demonstrated to correspond to an independence assumption. It is shown that by modeling the statistical properties of real-scenes better, objects can be recognized more reliably.

Morphological processing in word recognition : a review with particular reference to Spanish data. 'Procesamiento morfológico en el reconocimiento de palabras : una revisión con especial referencia a los datos en español'.

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Hemispheric asymmetries in recognition memory for negative and neutral words

Federmeier and Benjamin (2005) have suggested that semantic encoding for verbal information in the right hemisphere can be more effective when memory demands are higher. However, other studies (Kanske & Kotz, 2007) also suggest that visual word recognition differ in function of emotional valence. In this context, the present study was designed to evaluate the effects of retention level upon recognition memory processes for negative and neutral words. Sample consisted of 15 right-handed undergraduate portuguese students with normal or corrected to normal vision. Portuguese concrete negative and neutral words were selected in accordance to known linguistic capabilities of the right hemisphere. The participants were submitted to a visual half-field word presentation using a continuous recognition memory paradigm. Eye movements were continuously monitored with a Tobii T60 eye-tracker that showed no significant differences in fixations to negative and neutral words. Reaction times in word recognition suggest an overall advantage of negative words in comparison to the neutral words. Further analysis showed faster responses for negative words than for neutral words when were recognised at longer retention intervals for left-hemisphere encoding. Electrophysiological data through event related potentials revealed larger P2 amplitude over centro-posterior electrode sites for words studied in the left hemifield suggesting a priming effect for right-hemisphere encoding. Overall data suggest different hemispheric memory strategies for the semantic encoding of negative and neutral words.

The development of a phonetically balanced word recognition test in the Ilocano language

Three different phonetically-balanced 50-word recognition lists were constructed in the Ilocano language. Factors that were considered in the construction of these lists were: phonetic balance, syllable structure, and commonness of words.

Auditory word-recognition by young deaf children

This paper reviews a study of a speech discrimination test for young profoundly deaf children.

A proposed plan for using Dr. Marie Carbo's tape-recorded books to improve word recognition skills in children with cochlear implants

This paper studies the effectiveness of the recorded books and teaching method developed by Dr. Marie Carbo in the aural habilitation of pre-lingual deaf children with cochlear implants.

Utilizing hearing assistive technology (HAT) to assess speech recognition: Comparison of word recognition scores obtained by hearing instrument users

The ability for individuals with hearing loss to accurately recognize correct versus incorrect verbal responses during traditional word recognition testing across four different listening conditions was assessed.

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

Spoken word recognition in adolescents with autism spectrum disorders and specific language impairment

Spoken word recognition, during gating, appears intact in specific language impairment (SLI). This study used gating to investigate the process in adolescents with autism spectrum disorders plus language impairment (ALI). Adolescents with ALI, SLI, and typical language development (TLD), matched on nonverbal IQ listened to gated words that varied in frequency (low/high) and number of phonological onset neighbors (low/high density). Adolescents with ALI required more speech input to initially identify low-frequency words with low competitor density than those with SLI and those with TLD, who did not differ. These differences may be due to less well specified word form representations in ALI.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Analysis of eye and head coordination in a visual peripheral recognition task

Coordinated eye and head movements simultaneously occur to scan the visual world for relevant targets. However, measuring both eye and head movements in experiments allowing natural head movements may be challenging. This paper provides an approach to study eye-head coordination: First, we demonstra- te the capabilities and limits of the eye-head tracking system used, and compare it to other technologies. Second, a beha- vioral task is introduced to invoke eye-head coordination. Third, a method is introduced to reconstruct signal loss in video- based oculography caused by cornea reflection artifacts in order to extend the tracking range. Finally, parameters of eye- head coordination are identified using EHCA (eye-head co- ordination analyzer), a MATLAB software which was developed to analyze eye-head shifts. To demonstrate the capabilities of the approach, a study with 11 healthy subjects was performed to investigate motion behavior. The approach presented here is discussed as an instrument to explore eye-head coordination, which may lead to further insights into attentional and motor symptoms of certain neurological or psychiatric diseases, e.g., schizophrenia.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Automaticity in L2 learning: Correlation between vocabulary proficiency and response time in word recognition

Automaticity (in this essay defined as short response time) and fluency in language use are closely connected to each other and some research has been conducted regarding some of the aspects involved. In fact, the notion of automaticity is still debated and many definitions and opinions on what automaticity is have been suggested (Andersson,1987, 1992, 1993, Logan, 1988, Segalowitz, 2010). One aspect that still needs more research is the correlation between vocabulary proficiency (a person’s knowledge about words and ability to use them correctly) and response time in word recognition. Therefore, the aim of this study has been to investigate this correlation using two different tests; one vocabulary size test (Paul Nation) and one lexical decision task (SuperLab) that measures both response time and accuracy. 23 Swedish students partaking in the English 7 course in upper secondary Swedish school were tested. The data were analyzed using a quantitative method where the average values and correlations from the test were used to compare the results. The correlations were calculated using Pearson’s Coefficient Correlations Calculator. The empirical study indicates that vocabulary proficiency is not strongly correlated with shorter response times in word recognition. Rather, the data indicate that L2 learners instead are sensitive to the frequency levels of the vocabulary. The accuracy (number of correct recognized words) and response times correlate with the frequency level of the tested words. This indicates that factors other than vocabulary proficiency are important for the ability to recognize words quickly.