A study on using corrective feedbacks in teaching essays at school of foreign languages, Thai Nguyen university

The ability of writing is considered as a main communication skill and “a unique asset” in SLA (Chastain, 1998) that language learners should be fully aware of. Methodology in teaching writing therefore has experienced considerable changes in the approach to teaching and assessing learners. Written corrective feedback as a response channel to students’ writings in SLA classrooms has been a topic of inclusive debates and inquiries amongst the scholarly sphere. Contributing to this bulk of controversy, the present study investigates teachers’ perceptions and their students’ attitudes and evaluations as to the practice of error corrective feedback. To collect data, two different questionnaires of suitable reliability were delivered to sample respondents of 12 teachers and 34 students respectively to elicit data catering the study’s purposes. Findings were also triangulated with 5 participant teachers invited for follow up interviews and a comparative reference to previous studies on written corrective feedbacks. The results revealed that there are no dramatic differences in teachers’ attitudes towards the usefulness of written corrective feedbacks. However, when it comes to types and amount of errors they should comment on, teachers’ responses and preference cover a wide spectrum.

How can we learn what attention is? Response gating via multiple direct routes kept in check by inhibitory control processes

To explore the time course of space- and object-based attentional selection processes I analysed the shapes of the response time (RT) and accuracy distributions of left/right arrow identification responses in the two-rectangle paradigm. After cueing one of the four ends of two horizontally or vertically oriented rectangles the arrow typically appears at the cued location (valid), or sometimes at an uncued location in the same (invalid-same) or other rectangle (invalid-different). The data point to a multiple-route model in which (a) an informative cue generates response channel activation before arrow signals emerge, (b) the task-irrelevant arrow location is represented in multiple egocentric and allocentric reference frames around 150 ms after target onset, with the former including a reference frame centered on the currently attended location, (c) the task-irrelevant spatial codes activate premature response tendencies that are actively inhibited to allow gating of arrow direction signals, (d) after an invalid cue the onset of the arrow triggers an “attention shift” – acting between 150 and 240 ms after target onset – that strongly interferes with task performance in certain conditions (invalid-same cueing with horizontal rectangles, and invalid-different cueing with vertical rectangles), and (e) participants differ in which task-irrelevant codes they preferentially inhibit. These results pave the way for future confirmatory studies to temporally characterize and disentangle the contributions of different types of response channel activation processes, from those of reactive cognitive control processes including active and selective response suppression.

What is causing “inhibition of return” in spatial cueing tasks? Temporally disentangling multiple cue-triggered effects on multiple time scales using response history and conditional accuracy analyses.

Research on spatial cueing has shown that uninformative cues often facilitate mean response time (RT) performance in valid- compared to invalid-cueing conditions at short cue-target stimulus-onset-asynchronies (SOAs), and robustly generate a reversed or inhibitory cueing effect at longer SOAs that is widely known as inhibition-of-return (IOR). To study the within-trial time course of IOR we employ discrete-time hazard and conditional accuracy analyses to describe and model the shapes of the RT and accuracy distributions measured in two experimental tasks. In contrast to the mean performance measures, our distributional analyses show that (a) the uninformative cue generates response channel activation, (b) which continues during the cue-target interval so that the cue location must be stored in spatial working memory, (c) the premature cue-triggered response is selectively inhibited before target onset, (d) the IOR effect (valid versus invalid cueing) emerges around 160 ms after target onset in the hazard functions when cue-target SOA exceeds ~200 ms, quickly increases and decreases in size, and is gone within 120 ms, (e) the inhibitory component does not diminish over the course of the experiment, and (f) the location of an additional central cue relative to the current focus of spatial attention can generate response channel activation as well. These distributional data show that mean performance patterns conceal crucial information about behavioral dynamics, and suggest that sensory IOR is the direct result of encoding the cue location in spatial working memory to promote change detection, instead of attention leaving an inhibitory tag to promote visual search.

Let’s Talk About This in Public: Consumer Expectations for Online Review Response

Online consumer reviews are becoming one of the key drivers of hospitality firm performance. Although research has investigated different aspects of online reviews such as their volume and length, issues regarding the effectiveness of review response demand for further investigation. Drawing on theories of expectancy value and communication, we develop and test a framework of consumer expectations regarding company responses. Results from two experiments show that consumer preferences for responses to their online reviews depend on the factors of valence (positive vs. negative), explanation type (explained action vs. explained reaction), and response channel (private vs. public). Perceived usefulness is found to be the underlying mechanism that explains these effects. The study’s theoretical contributions and managerial implications are discussed.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area’s representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network, and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model’s original feature space and the hypothesis space generated by linear transformations of that feature space.HighlightsWe tested computational models of representations in ventral-stream visual areas.We compared representational dissimilarities with/without linear remixing of model features.Early visual areas were best explained by shallow – and higher by deep – models.Unsupervised shallow models performed better without linear remixing of their features.A supervised deep convolutional net performed best with linear feature remixing.