Language and memory for object location

In three experiments, we investigated the influence of two types of language on memory for object location: demonstratives (this, that) and possessives (my, your). Participants first read instructions containing demonstratives/possessives to place objects at different locations, and then had to recall those object locations (following object removal). Experiments 1 and 2 tested contrasting predictions of two possible accounts of language on object location memory: the Expectation Model (Coventry, Griffiths, & Hamilton, 2014) and the congruence account (Bonfiglioli, Finocchiaro, Gesierich, Rositani, & Vescovi, 2009). In Experiment 3, the role of attention allocation as a possible mechanism was investigated. Results across all three experiments show striking effects of language on object location memory, with the pattern of data supporting the Expectation Model. In this model, the expected location cued by language and the actual location are concatenated leading to (mis)memory for object location, consistent with models of predictive coding (Bar, 2009; Friston, 2003)

Interaction between high-level and low-level image analysis for semantic video object extraction

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A psychology literature study on modality related issues for multimodal presentation in crisis management

The motivation of this psychology literature study is to obtain modality related guidelines for real-time information presentation in crisis management environment. The crisis management task is usually companied by time urgency, risk, uncertainty, and high information density. Decision makers (crisis managers) might undergo cognitive overload and tend to show biases in their performances. Therefore, the on-going crisis event needs to be presented in a manner that enhances perception, assists diagnosis, and prevents cognitive overload. To this end, this study looked into the modality effects on perception, cognitive load, working memory, learning, and attention. Selected topics include working memory, dual-coding theory, cognitive load theory, multimedia learning, and attention. The findings are several modality usage guidelines which may lead to more efficient use of the user’s cognitive capacity and enhance the information perception

Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling

We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image. This strategy partitions training into two distinct stages. First, in an unsupervised manner, we learn a set of generic dictionaries optimized for sparse coding of image patches. We train a multilayer representation via recursive sparse dictionary learning on pooled codes output by earlier layers. Second, we encode all training images with the generic dictionaries and learn a transfer function that optimizes reconstruction of patches extracted from annotated ground-truth given the sparse codes of their corresponding image patches. At test time, we encode a novel image using the generic dictionaries and then reconstruct using the transfer function. The output reconstruction is a semantic labeling of the test image. Applying this strategy to the task of contour detection, we demonstrate performance competitive with state-of-the-art systems. Unlike almost all prior work, our approach obviates the need for any form of hand-designed features or filters. To illustrate general applicability, we also show initial results on semantic part labeling of human faces. The effectiveness of our approach opens new avenues for research on deep sparse representations. Our classifiers utilize this representation in a novel manner. Rather than acting on nodes in the deepest layer, they attach to nodes along a slice through multiple layers of the network in order to make predictions about local patches. Our flexible combination of a generatively learned sparse representation with discriminatively trained transfer classifiers extends the notion of sparse reconstruction to encompass arbitrary semantic labeling tasks.Comment: to appear in Asian Conference on Computer Vision (ACCV), 201

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this article, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the immensely popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts its synapses in a biologically-plausible fashion, while another, complementary neural system rapidly learns to direct and control this cortex-like structure by mimicking the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting as compared to standard neural models and outperforms a wide swath of previously proposed methods even though it is trained across task datasets in a stream-like fashion. The promising performance of our complementary system on benchmarks, e.g., SplitMNIST, Split Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, a new possibility for tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split mnist/fmnist/not-mnist. Task selection/basal ganglia model has been integrate

Effective declutter of complex flight displays using stereoptic 3-D cueing

The application of stereo technology to new, integrated pictorial display formats has been effective in situational awareness enhancements, and stereo has been postulated to be effective for the declutter of complex informational displays. This paper reports a full-factorial workstation experiment performed to verify the potential benefits of stereo cueing for the declutter function in a simulated tracking task. The experimental symbology was designed similar to that of a conventional flight director, although the format was an intentionally confused presentation that resulted in a very cluttered dynamic display. The subject's task was to use a hand controller to keep a tracking symbol, an 'X', on top of a target symbol, another X, which was being randomly driven. In the basic tracking task, both the target symbol and the tracking symbol were presented as red X's. The presence of color coding was used to provide some declutter, thus making the task more reasonable to perform. For this condition, the target symbol was coded red, and the tracking symbol was coded blue. Noise conditions, or additional clutter, were provided by the inclusion of randomly moving, differently colored X symbols. Stereo depth, which was hypothesized to declutter the display, was utilized by placing any noise in a plane in front of the display monitor, the tracking symbol at screen depth, and the target symbol behind the screen. The results from analyzing the performances of eight subjects revealed that the stereo presentation effectively offsets the cluttering effects of both the noise and the absence of color coding. The potential of stereo cueing to declutter complex informational displays has therefore been verified; this ability to declutter is an additional benefit from the application of stereoptic cueing to pictorial flight displays