An eye-tracking attention based model for abstractive text headline

Online network platforms provide great convenience for users to obtain information. However, it’s challenging to select the required information from enormous texts. Automatic text headline generation methods not only guide users to select the information they are interested in, but also solve the problem of information overload. Nevertheless, the existing works mainly utilize the grammar rules to obtain the key information of the source text, while ignoring the dwell time of user’s attention on different text contents. To address this issue, this paper proposes an abstractive text headline generation model based on the eye-tracking attention mechanism. Specifically, this model first relies on the eye-tracking data to establish the mapping relationship between text words and the words’ reading time. Then, an eye-tracking attention mechanism is constructed to judge the importance of different words. Finally, this attention mechanism is integrated into the encoder-decoder framework to generate a high-quality headline. Experimental results obtained from different datasets demonstrate that the headline generated by our model is more concise. Moreover, our proposed model outperforms significantly the classical headline generation models on ROUGE-1, ROUGE-2 and ROUGE-L.     

New ideas for brain modelling 3

This paper considers a process for the creation and subsequent firing of sequences of neuronal patterns, as might be found in the human brain. The scale is one of larger patterns emerging from an ensemble mass, possibly through some type of energy equation and a reduction procedure. The links between the patterns can be formed naturally, as a residual effect of the pattern creation itself. This paper follows-on closely from the earlier research, including two earlier papers in the series and uses the ideas of entropy and cohesion. With a small addition, it is possible to show how the inter-pattern links can be determined. A compact Grid form of an earlier Counting Mechanism is also demonstrated and may be a new clustering technique. It is possible to explain how a very basic repeating structure can form the arbitrary patterns and activation sequences between them, and a key question of how nodes synchronise may even be answerable.     

Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.     

Mental models in the brain: On context-dependent neural correlates of mental models

In this paper, the concept of context-dependent realisation of mental models is introduced and discussed. Literature from neuroscience is discussed showing that different types of mental models can use different types of brain areas. Moreover, it is discussed that the same occurs for the formation and adaptation of mental models and the control of these processes. This makes that it is hard to claim that all mental models use the same brain mechanisms and areas. Instead, the notion of context-dependent realisation is proposed here as a better manner to relate neural correlates to mental models. It is shown in some formal detail how this context-dependent realisation approach can be related to well-known perspectives based on bridge principle realisation and interpretation mapping realisation.     

Restricting grammatical complexity

Theories of natural language syntax often characterize grammatical knowledge as a form of abstract computation. This paper argues that such a characterization is correct, and that fundamental properties of grammar can and should be understood in terms of restrictions on the complexity of possible grammatical computation, when defined in terms of generative capacity. More specifically, the paper demonstrates that the computational restrictiveness imposed by Tree Adjoining Grammar provides important insights into the nature of human grammatical knowledge.     

The dynamic mechanism of a novel stochastic neural firing pattern observed in a real biological system

In the experimental neural pacemaker of a rat, a novel firing pattern has been discovered. This pattern was generated between the period 2 firing pattern and the period 3 firing pattern during the periodic adding bifurcation and inverse periodic adding bifurcation. The pattern was observed and analyzed in the present investigation. The composition of this novel firing pattern could be regarded as a transition between a string of period 2 burst and a string of period 3 burst without single period 2 or period 3 burst, which was different from those chaotic and stochastic neural firing patterns in previous reports. It was identified to be stochastic by the inter-event intervals (IEIs) analysis, although it exhibited chaos-like characteristics with the results of the inter-spike intervals (ISIs) analysis. The numerical simulation suggested that the new pattern observed in the real biological system could be simulated in the stochastic Chay model but not in the deterministic model. With the signal to noise ratio (SNR) analysis and bifurcation analysis, this novel firing pattern was considered to be generated by stochastic resonance under the influence of noise near the periodic adding (inverse) bifurcation point. The probability analysis of transformed binary chain further confirmed that the origin of stochastic and chaos (deterministic)-like characteristics of this novel firing pattern.     

The conscious experience of color constancy and neural responses to subliminal deviations – A behavioral and EEG/ERP oddball study

IntroductionColor constancy, a property of conscious color experience, maintains object color appearance across illuminant changes. We investigated the neural correlates of subliminal vs. conscious stimulus deviations of color constancy manipulations.MethodsBehavioral and Oddball EEG/ERP experiments were conducted (n = 20). Psychophysical illuminant variation discrimination thresholds were first estimated, to establish individual perceptual awareness ranges, allowing for simulation of natural daylight spectral and spatial variations on colored surfaces, at different ambiguity levels.ResultsBehavioral results validated illuminant choice. ERPs showed a significant modulation of posterior P1 component specifically for the subliminal global uniform deviation condition, respecting color constancy. Neural correlates of conscious percepts were identified at posterior N2-P3 latencies, parietal (P3b) and frontal regions.ConclusionsWe identified an early subliminal correlate of low-level illuminant change, which reflects automatic unconscious detection of global color constancy deviations. Its suppression under conscious perception is probably due to top-down suppression according to prediction error models.