Structuring an internal representation of text: A basis of literacy

1980 ◽  
pp. 227-239
Author(s):  
Wayne L. Shebilske
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson

Built-in datatypes and C++ classes are introduced in this chapter, and discussed in relation to the important notion of encapsulation, which refers to the separation between the internal representation of the datatype and the operations to which it responds. Encapsulation later becomes an important consideration in the design of custom C++ classes that programmers develop themselves. It is illustrated with built-in floating-point datatypes float and double and with the complex class from the C++ standard library. While a sophisticated programmer is aware of the internal representation of data and its resulting limitations, encapsulation allows one to consider these as details and frees one to think at a higher level of program design. Some simple numerical examples are discussed in the text and in the exercises.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.


Perception ◽  
1998 ◽  
Vol 27 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Michel-Ange Amorim ◽  
Jack M Loomis ◽  
Sergio S Fukusima

An unfamiliar configuration lying in depth and viewed from a distance is typically seen as foreshortened. The hypothesis motivating this research was that a change in an observer's viewpoint even when the configuration is no longer visible induces an imaginal updating of the internal representation and thus reduces the degree of foreshortening. In experiment 1, observers attempted to reproduce configurations defined by three small glowing balls on a table 2 m distant under conditions of darkness following ‘viewpoint change’ instructions. In one condition, observers reproduced the continuously visible configuration using three other glowing balls on a nearer table while imagining standing at the distant table. In the other condition, observers viewed the configuration, it was then removed, and they walked in darkness to the far table and reproduced the configuration. Even though the observers received no additional information about the stimulus configuration in walking to the table, they were more accurate (less foreshortening) than in the other condition. In experiment 2, observers reproduced distant configurations on a nearer table more accurately when doing so from memory than when doing so while viewing the distant stimulus configuration. In experiment 3, observers performed both the real and imagined perspective change after memorizing the remote configuration. The results of the three experiments indicate that the continued visual presence of the target configuration impedes imaginary perspective-change performance and that an actual change in viewpoint does not increase reproduction accuracy substantially over that obtained with an imagined change in viewpoint.


2013 ◽  
Vol 110 (4) ◽  
pp. 984-998 ◽  
Author(s):  
Wilsaan M. Joiner ◽  
Jordan B. Brayanov ◽  
Maurice A. Smith

The way that a motor adaptation is trained, for example, the manner in which it is introduced or the duration of the training period, can influence its internal representation. However, recent studies examining the gradual versus abrupt introduction of a novel environment have produced conflicting results. Here we examined how these effects determine the effector specificity of motor adaptation during visually guided reaching. After adaptation to velocity-dependent dynamics in the right arm, we estimated the amount of adaptation transferred to the left arm, using error-clamp measurement trials to directly measure changes in learned dynamics. We found that a small but significant amount of generalization to the untrained arm occurs under three different training schedules: a short-duration (15 trials) abrupt presentation, a long-duration (160 trials) abrupt presentation, and a long-duration gradual presentation of the novel dynamic environment. Remarkably, we found essentially no difference between the amount of interlimb generalization when comparing these schedules, with 9–12% transfer of the trained adaptation for all three. However, the duration of training had a pronounced effect on the stability of the interlimb transfer: The transfer elicited from short-duration training decayed rapidly, whereas the transfer from both long-duration training schedules was considerably more persistent (<50% vs. >90% retention over the first 20 trials). These results indicate that the amount of interlimb transfer is similar for gradual versus abrupt training and that interlimb transfer of learned dynamics can occur after even a brief training period but longer training is required for an enduring effect.


Author(s):  
Vilson J. Leffa

A typical problem in the resolution of pronominal anaphora is the presence of more than one candidate for the antecedent of the pronoun. Considering two English sentences like (1) "People buy expensive cars because they offer more status" and (2) "People buy expensive cars because they want more status" we can see that the two NPs "people" and "expensive cars", from a purely syntactic perspective, are both legitimate candidates as antecedents for the pronoun "they". This problem has been traditionally solved by using world knowledge (e.g. schema theory), where, through an internal representation of the world, we "know" that cars "offer" status and people "want" status. The assumption in this paper is that the use of world knowledge does not explain how the disambiguation process works and alternative explanations should be explored. Using a knowledge poor approach (explicit information from the text rather than implicit world knowledge) the study investigates to what extent syntactic and semantic constraints can be used to resolve anaphora. For this purpose, 1,400 examples of the word "they" were randomly selected from a corpus of 10,000,000 words of expository text in English. Antecedent candidates for each case were then analyzed and classified in terms of their syntactic functions in the sentence (subject, object, etc.) and semantic features (+ human, + animate, etc.). It was found that syntactic constraints resolved 85% of the cases. When combined with semantic constraints the resolution rate rose to 98%. The implications of the findings for Natural Language Processing are discussed.


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