Information processing stages

Lorist MM, Snel J, Kok A. Influence of caffeine on information processing stages in well rested and fatigued subjects. Psychopharmacology 1994 113 411421. 

On the basis of Nagel s (1988) information processing stages, 71 per cent of the 284 accidents studied were considered to involve human error, whereby 22 per cent were eoded as information errors, 35 per cent as decision errors, and 43 per cent as action errors (O Hare et al., 1994). However, a different picture emerged when the mishaps were divided according to accident severity. Of the 34 accidents in which there was a fatality or serious injury, 62.5 per cent were attributable to decision errors, while only 25 per eent involved the action stage (O Hare et al., 1994). Of the 169 cases involving minor/non-injury occurrences, only 30.5 per cent were the result of decision errors, while 45.6 per eent were attributed to action errors. These results closely mirror those reported by Jensen and Benel (1977) in their examination of NTSB reeords between 1970 and 1974 in whieh the majority of non-fatal incidents appeared to involve perceptual-motor factors, whereas most fatal accidents were associated with decision-related factors (Jensen and Benel, 1977). 

In BS/EN/ISO 11064-1 2001 preliminary function allocations are performed using an overview approach comprised of four basic heuristics. Human factors best practice offers an extension to this approach in the form of Fitts List (Fitts, 1951). The expanded collection of heuristics is anchored around information-processing stages and the relative performance of humans or automation in performing them. Fitts List is shown in Table 7.4. 

Figure 5,10 Accident sequence model showing failures in the various human information-processing stages leading to an accident.

The lighter arrows represent typical shortcuts, which omit particular stages in the information-processing chain. These shortcuts may be "legitimate," and would only lead to errors in certain cases. For example, the worker may erroneously believe that he or she recognizes a pattern of indicators and may immediately execute a skill-based response, instead of moving to the rule-based level to apply an explicit diagnostic rule. 

Human Information-Processing A view of the human operator as an information-processing system. Information-processing models are convention-aUy expressed in terms of diagrams which indicate the flow of information throu stages such as perception, decision-making, and action. 

Food additives (like color additives) can be added at various processing stages, but the additions are commonly made at the latest production stage before final marketing to ensure optimal functionality of the additive in the product as sold. Residues of additives used at earlier production stages may be present still in the final formulation. However, if they do not have further functions at that stage, they are considered process aids (to help preparation but without technological effects in the finished product) and are not indicated on labels. Some additives also decompose over time so that the levels present at the end of shelf life may be quite low. Several techniques and information sources can be used to assess contamination levels of chemicals ... 

Information processing in production scheduling is essentially the same as in planning. Both plants and individual process equipment take orders and make products. For a plant, the customer is usually external, but for a process (or work cell in discrete manufacturing parlance), the order comes from inside the plant or factory. In a plant, the final product can be sold to an external customer for a process, the product delivered is an intermediate or partially finished product that goes on to the next stage of processing (internal customer). 

In the second stage of the research, a higher level of organization of the biosystems was considered. To this aim, the basic system presented above was used to construct biochemical networks. This was achieved by connecting a number of basic systems according to the principles of neural networks. This part of the research allowed us to delineate the rules for connecting the basic systems into functional biochemical networks and to study the type of information processing that can be achieved in a defined network. 

At every stage of the development process, the results of a reaction or process stage will be analysed in one of two ways. The reaction mixture itself will be sampled and analysed to yield information such as extent/completion of reaction, reaction yield or reaction purity. Alternatively, the reaction product will be isolated and dried before sampling and analysis. Typical analytical information in this case would include both chemical and physical characterisation, plus quantitative data to ensure conformance with some pre-defined specification or to provide batch data on which a suitable specification will ultimately be based. Note that however quickly the analytical data are provided, there is a disconnect from the reaction, which means that reaction control is impossible and that... 

There is value in examining logs of equipment and physical plant maintenance. These documents can provide a chronological profile of the operating environment and reveal recent alterations to the process equipment that may have enough impact to disqualify the product from retrospective validation consideration. For this reason, it is always prudent to contemplate equipment status early in the information-gathering stage. The availability of such information should be ascertained for yet another reason rarely is equipment dedicated to... 

At this step, validation of the process serves as the means of setting and/or evaluating control parameters as established during experimentation, development activities, and scale-up. The purpose of validation is to verify or authenticate that the process is capable of reproduction with results within specified parameters. Part of this validation is to collect data over the various processing stages. The validation protocol should be based upon data and information collected during the development activities of the product s life cycle. 

Design of the experiment and scale-up. It is at this stage of the product s life cycle that many control parameters are evaluated (e.g., glass container in lieu of stainless steel, temperature controls). Information should be collected at critical processing stages and included in the master production record. This information will be used to assist in scale-up activities. 

An important way to improve network performance is through the use of prior knowledge, which refers to information that one has about the desired form of the solution and which is additional to the information provided by the training data. Prior knowledge can be incorporated into the pre-processing and post-processing stages (Chapter 7), or into the network structure itself. 

Component inventory All eomponents of the system including the computer, network connections, software, and MS instruments are listed in the test script (this is supphed from the system inventory and information gathering stages of the process outlined in Figure 20.9). 

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