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81.
Recently, Brain-Computer Interfaces (BCIs) have been extensively popular for employing Electroencephalography (EEG) signals to control devices with different applications. The use of BCIs currently involves for lots of applications to help the disabilities who cannot communicate with other people, as it is an alternative way for communication by passing the need of speech. Although the applications to spell the character with BCI systems (e.g., P300-speller, SSVEP-speller, Hex-O-spell) have been already developed, but these techniques are not flexible in the real scenarios because they require the stimulus all the time or stopping the activity to focus on the limb movement in order to provide the accuracy of brain responses. In this paper, the feasibility of brainwave classification for the applications of character-writing by considering only the EEG signals without the need of stimulus unlike the literature is newly introduced. This paper adopts a classification technique named Artificial Neural Network (ANN) and focuses on two different characters; straight line and circle. From the experimental results, the suitable position of electrodes are the pair of electrodes (F3 and F4) at the frontal lobe, which provide the best result as compared to other areas due to its important role in perception, maintenance and revival of the information. The experimental results indicate that the classification accuracy of the proposed technique is about 70%, which in turn leads to a significant achievement for the development of character-writing applications. 相似文献
82.
Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used. 相似文献
83.
Lauren Fletcher;Hugh Rabagliati;Jennifer Culbertson; 《Cognitive Science》2024,48(11):e70007
There is ample evidence that individual-level cognitive mechanisms active during language learning and use can contribute to the evolution of language. For example, experimental work suggests that learners will reduce case marking in a language where grammatical roles are reliably indicated by fixed word order, a correlation found robustly in the languages of the world. However, such research often assumes homogeneity among language learners and users, or at least does not dig into individual differences in behavior. Yet, it is increasingly clear that language users vary in a large number of ways: in culture, in demographics, and—critically for present purposes—in terms of cognitive diversity. Here, we explore how neurodiversity impacts behavior in an experimental task similar to the one summarized above, and how this behavior interacts with social pressures. We find both similarities and differences between autistic and nonautistic English-speaking individuals, suggesting that neurodiversity can impact language change in the lab. This, in turn, highlights the potential for future research on the role of neurodivergent populations in language evolution more generally. 相似文献
84.
Distributional information is a potential cue for learning syntactic categories. Recent studies demonstrate a developmental trajectory in the level of abstraction of distributional learning in young infants. Here we investigate the effect of prosody on infants' learning of adjacent relations between words. Twelve‐ to thirteen‐month‐old infants were exposed to an artificial language comprised of 3‐word‐sentences of the form aXb and cYd, where X and Y words differed in the number of syllables. Training sentences contained a prosodic boundary between either the first and the second word or the second and the third word. Subsequently, infants were tested on novel test sentences that contained new X and Y words and also contained a flat prosody with no grouping cues. Infants successfully discriminated between novel grammatical and ungrammatical sentences, suggesting that the learned adjacent relations can be abstracted across words and prosodic conditions. Under the conditions tested, prosody may be only a weak constraint on syntactic categorization. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
85.
Recent challenges to Chomsky's poverty of the stimulus thesis for language acquisition suggest that children's primary data may carry \"indirect evidence\" about linguistic constructions despite containing no instances of them. Indirect evidence is claimed to suffice for grammar acquisition, without need for innate knowledge. This article reports experiments based on those of Reali and Christiansen (2005) , who demonstrated that a simple bigram language model can induce the correct form of auxiliary inversion in certain complex questions. This article investigates the nature of the indirect evidence that supports this learning, and assesses how reliably it is available. Results confirm the original finding for one specific sentence type but show that the model's success is highly circumscribed. It performs poorly on inversion in related constructions in English and Dutch. Because other, more powerful statistical models have so far been shown to succeed only on the same limited subset of cases as the bigram model, it remains to be seen whether stimulus richness can be substantiated more generally. 相似文献
86.
We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem-solving strategy, the relative difficulty of faults, and average fault-finding time. More important, because the model learns and transfers its learning across problems, it also accounts for the faster problem-solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition-based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem-solving episodes. 相似文献
87.
The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Causality emerges not from a central controlling stored program but directly from the architecture. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map, with a resultant navigation signal obtained. Navigation can similarly be for the physical world as well as for a landscape of higher cognitive concepts. There is good explainability for causal decisions. A simulation of the CCA1 controlling a search and rescue robot is presented with the goal of finding and rescuing a lost hiker within a grid world. A simulation of the CCA1 controlling a repair robot is presented that can predict the movement of a series of gears. 相似文献
88.
The term “Cognitive Architectures” indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research. 相似文献
89.
To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China. 相似文献
90.
Gheysen F Fias W 《Advances in cognitive psychology / University of Finance and Management in Warsaw》2012,8(2):73-82
Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. Dissociable judgment-linked versus motor-linked and implicit versus explicit neural systems have been proposed. This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. Instead, a broader theoretical framework is necessary providing a more continuous means of dissociating sequence learning systems. We argue that a more recent theory, dissociating multidimensional versus unidimensional neural systems, might provide such framework, and we discuss this theory in relation to more general principles of associative learning and recent imaging findings. 相似文献