Object database - Wikipedia
We briefly discuss how this work with young adults may inform other age groups . Knowledge of object relationships can be acquired automatically and .. Wu R ., Nako R., Band J., Pizzuto J., Shadravan Y., Scerif G., et al. The Unified Modeling Language (UML) is a de-facto standard for modeling object-oriented systems. In UML there are five different types of relationships: association, aggregation, composition, dependency, and inheritance. Association is a semantically weak relationship (a semantic. Mediator () Define an object that encapsulates how a set of objects .. The View-Controller relationship is an example of the Strategy () design pattern. . and flow of control, whereas the Behavioral object patterns describe how a group of . run-time association of a request to an object and one of its operations is.
These effects appear with both task-relevant and task-irrelevant knowledge.
Association (object-oriented programming) - Wikipedia
We then review how existing object associations may influence subsequent learning of new information, which is both a driver and a consequence of selection processes. These insights highlight the importance of one aspect of prior knowledge for attentional selection and information acquisition. We briefly discuss how this work with young adults may inform other age groups throughout the lifespan, as learners gradually increase their prior knowledge.
Importantly, these insights have implications for developing more accurate measurements of cognitive abilities. Even if simple and somewhat meaningful stimuli, such as letters and numerals, are used in a task, different features of the stimuli like color and shape are randomized from trial to trial.
These studies have been instrumental in identifying critical aspects of attentional selection, including the timing and the process of attentional selection. Performance on these tasks, as well as other tasks measuring other cognitive abilities such as working memory, executive function, and inhibition, has been used to determine the range for healthy cognitive development and aging see Park and Reuter-Lorenz, These tasks are also used to detect cognitive impairments in aging adults e.
Moreover, cognitive training programs have been using these modularized tasks to improve specific cognitive abilities that they were designed to measure e. In order to obtain more accurate and ecologically valid measures of cognitive abilities, it is important to investigate the influence of prior knowledge, which impacts performance on cognitive tasks in meaningful ways e.
This mini review focuses on prior knowledge of associations between individual objects, because it is an important, yet often ignored, aspect of attentional selection. Objects in the natural environment rarely appear on their own. Instead, they almost always appear with other objects.
Prior Knowledge of Object Associations Shapes Attentional Templates and Information Acquisition
Therefore, it is important to understand how these associations influence attentional selection and subsequent cognitive processes, namely information acquisition. Then, we review studies showing how prior knowledge of associations determines what and how new information is learned, which results from intermediary selection processes. We end with a brief discussion on how this work can inform research with other age groups throughout the lifespan, and aid in developing more accurate measurements of cognitive abilities.
Prior Knowledge of Task-Relevant Object Associations Shapes Attentional Templates During Visual Search Recent studies have shown that prior knowledge of object associations shapes how people search for information in the environment. Top-down, or goal-directed, search has been theorized to unfold in the following manner: The participant creates an attentional template, a prioritized working memory representation, of the to-be-searched target item, and then matches the attentional template to the stimuli presented e.
Without an attentional template, top-down search is inefficient, perhaps impossible. Attentional templates can contain a single feature, a combination of features, a rule, or even a category Luck and Hillyard, ; Eimer, ; Nako et al.
Building on the theoretical foundations of top-down search using simple meaningless stimuli, several recent behavioral studies have demonstrated that prior knowledge of object associations indeed impacts attentional templates and search efficiency Yang and Zelinsky, ; Wolfe et al.
For example, during visual search, participants could recall and recognize objects associated with the target more accurately than unrelated distractors Moores et al. Distractors in the same color as the target in the natural environment slowed visual search for the target in the laboratory setting, even if the target was grayscale Olivers et al.
After knowing the target e. In essence, prior knowledge has benefits and costs on visual search. Recent ERP studies using the N2pc component suggest that these behavioral benefits and costs may be due to grouping of associated objects into one unit e.
When controlling for factors such as salience, the N2pc ERP reflects the number of attentional templates used during a search task Nako et al. Therefore, the N2pc is a useful tool for investigating the grouping of associated objects into an attentional template.
In Nako et al. ERP results revealed that such category search produced similar N2pc components compared to searching for a specific letter target among distractors from the same letter category. In other words, searching for associated objects in one category is similar to searching for a specific object.
This finding has been replicated with naturalistic and artificial categories, such as clothing and kitchen items, human and ape faces Wu et al. Prior knowledge of object associations also induces costs when distractors are thought to be in the same category as the target or semantically related to the target Telling et al.
In these cases, prior knowledge encourages false alarms to distractors related to the target, resulting in poorer behavioral performance when indicating the absence of the target. Costs of prior knowledge emerge when experimental conditions deviate from the statistics in the familiar environment in which the knowledge was first acquired Green et al. Relying on prior knowledge allows people to be more efficient in familiar environments, at the cost of being less efficient in novel environments that encompass different constraints.
Prior Knowledge of Object Associations Shapes the Construction of Attentional Templates Prior knowledge induces costs and benefits on attentional selection because it dictates what is included in search templates.
This tradeoff due to prior knowledge is the focus of some recent studies investigating how these costs and benefits emerge with learning and experience. The vast majority of visual search studies provide participants with explicit instructions about the target and sometimes the distractors, and assume that the participant creates an attentional template identical to the target shown, or at least containing the relevant features.
This notion is consequential because the construction and use of relevant information for attentional templates typically determine search performance, and everyday activities do not often include simple meaningless stimuli or explicit instructions for every action.
Recent studies suggest that the amount of knowledge about object associations acquired prior to and during a task can determine how attentional templates are constructed.
For example, newly acquired categories may be more difficult to find initially, but they elicit fewer false alarms compared to highly familiar categories, such as letters and numerals Wu et al. Unfamiliar categories require learning to construct appropriate attentional templates, which may require learning new rules that may be based on seemingly arbitrary principles e.
Therefore, search for newly learned categories may be initially inefficient. These studies also showed that probabilistic information of object associations can be used to determine which features and objects to prioritize in the attentional template. For example, Wu et al. Participants implicitly extracted the category information based on the co-occurrence of the characters and formed a unified search template for the two categories of Chinese characters, albeit weaker templates than for familiar letters and numerals.
Prior Knowledge of Task-Irrelevant Object Associations Impacts Search Prior knowledge of object associations guides the spatial allocation of attention, even when completely task-irrelevant e.
In one study Zhao et al. Participants were faster to detect the target when it appeared in the structured location compared to the random location, suggesting that attention was biased toward the regularities of the object associations in the structured location. This attentional bias persists even when the regularities are later removed, or when new regularities emerge in a different location Yu and Zhao, Moreover, depending on how objects co-occur in space, local and global regularities draw attention to local and global levels, respectively Zhao and Luo, These studies demonstrate that the prior knowledge of object associations and co-occurrences biases attention to the spatial location containing regularities, possibly in order to facilitate further learning of regularities.
This attentional bias can be both beneficial in allowing more learning to occur, and costly in terms of perhaps hindering learning of new information elsewhere. Prior Knowledge of Object Associations Dictates how new Information is Acquired As both a consequence and a driver of the attentional selection process, prior knowledge of object associations can guide how new information is learned and created.
Knowledge of object relationships can be acquired automatically and implicitly through statistical learning, which involves the extraction of reliable co-occurrences between individual objects over space and time e.
This ability is present in early infancy Saffran et al. A notable consequence of statistical learning is the generation of the knowledge that certain objects co-occur, and such knowledge is often implicit Baker et al.
This learning process occurs incidentally to the task without conscious intent, and can guide the spatial allocation of attention in a spontaneous, implicit, and persistent manner Zhao et al. Recent studies have demonstrated that prior knowledge of how objects are related to each other generates new knowledge of associations Mole and Zhao, ; Luo and Zhao, ; Zhao and Yu, In Luo and Zhaoparticipants were first exposed to a sequence of colored circle pairs, in which one circle appeared before another in a fixed order.
After learning the color circle pairs, participants automatically inferred new color pairs AC even though they never appeared together before. Both the prior knowledge and the newly acquired knowledge were implicit, in that no participant was explicitly aware of the pairs.
Moreover, after acquiring the prior knowledge of one pair at one categorical level, participants implicitly inferred the same association at the subordinate level and the superordinate level, even if the subordinate or superordinate objects were never presented before.
For example, after learning a city pair New York-Vancouver, participants could implicitly infer the corresponding park pair Central Park-Stanley Park, and the corresponding country pair United States-Canada. These results suggest that prior knowledge automatically generates new knowledge of object associations through transitive relations, even outside of explicit awareness.UML Tutorial: Association, Aggregation, Composition, Dependency, Generalization, and Realization
This study with young adults builds on infant studies demonstrating that prior knowledge of older regularities biases learning of new regularities Marcus et al. Lew-Williams and Saffran exposed infants to disyllabic or trisyllabic nonsense words, and then a new set of disyllabic or trisyllabic nonsense words. Listening times showed that infants were able to learn words only when the words were uniformly disyllabic or trisyllabic throughout the entire experiment.
Previous exposure to disyllabic words impaired the ability to learn trisyllabic words, and vice versa. Thus, prior knowledge about word length produces expectations that facilitate processing of future word information.
Prior Knowledge of Object Associations Shapes Attentional Templates and Information Acquisition
Relationships[ edit ] UML relations notation A relationship is a general term covering the specific types of logical connections found on class and object diagrams. UML defines the following relationships: Dependency[ edit ] A dependency is a semantic connection between dependent and independent model elements.
This association is uni-directional. Association[ edit ] Class diagram example of association between two classes An association represents a family of links.
A binary association with two ends is normally represented as a line. An association can link any number of classes. An association with three links is called a ternary association. An association can be named, and the ends of an association can be adorned with role names, ownership indicators, multiplicity, visibility, and other properties.
There are four different types of association: Bi-directional and uni-directional associations are the most common ones. For instance, a flight class is associated with a plane class bi-directionally. Association represents the static relationship shared among the objects of two classes. Aggregation[ edit ] Class diagram showing Aggregation between two classes.
Here, a Professor 'has a' class to teach.
Aggregation is a variant of the "has a" association relationship; aggregation is more specific than association. It is an association that represents a part-whole or part-of relationship. As shown in the image, a Professor 'has a' class to teach.
As a type of association, an aggregation can be named and have the same adornments that an association can. However, an aggregation may not involve more than two classes; it must be a binary association. Furthermore, there is hardly a difference between aggregations and associations during implementation, and the diagram may skip aggregation relations altogether. The contents of the container still exist when the container is destroyed. In UMLit is graphically represented as a hollow diamond shape on the containing class with a single line that connects it to the contained class.
The aggregate is semantically an extended object that is treated as a unit in many operations, although physically it is made of several lesser objects. Here the student can exist without library, the relation between student and library is aggregation. Composition[ edit ] Two class diagrams. The diagram on top shows Composition between two classes: A Car has exactly one Carburetor, and a Carburetor has at most one Car Carburetors may exist as separate parts, detached from a specific car.
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The diagram on bottom shows Aggregation between two classes: A Pond has zero or more Ducks, and a Duck has at most one Pond at a time. The UML representation of a composition relationship shows composition as a filled diamond shape on the containing class end of the lines that connect contained class es to the containing class.
Differences between Composition and Aggregation[ edit ] Composition relationship 1.