ON THE QUANTIFICATION OF HUMAN RELIABILITY MODELS BASED ON COGNITIVE MECHANISMS OF HUMAN ERRORS

The second generation human reliability models have as a main feature the incorporation of cognitive models and a detailed taxonomy of human errors, in such a way that they present coherence in terms of error classification with the adopted cognitive model. This work has as a main goal a preliminary revision of the main second-generation human reliability models, trying to show their advantages and shortcomings, particularly in what concerns the quantification of the human error in complex tasks of industrial plants. Three second-generation models will be examined: ATHEANA, CREAM and DYLAM. These three models are sufficiently complex to be named cognitive reliability models. Two main aspects will be explored in these models: the error mechanisms and the adopted or proposed quantification methods. Concerning the error mechanisms, the chosen models will be compared with the Reason’s model of human errors in three performance levels: skills, rules and knowledge, considering the concept of unsafe acts and the basic error types related to such acts, in other words, slips, lapses and mistakes. Besides the basic error types, the error forms and the activation mechanisms that occur in the retrieval of incomplete semantic knowledge related to such error forms will also be investigated. 1. WORKING MEMORY AND ITS ACTIVATION MECHANISMS The activation mechanisms are responsible for the recovery of the knowledge stored in the long-term memory and the retrieval of this knowledge in the working memory (short-term memory). Three main activation mechanisms are identified by Reason [1] in his model of a fallible machine: similarity, frequency and directed search. However, any model of activation of the knowledge stored in the long-term memory for recovery and retrieval in the short-term memory should consider a specific model of the information processing in the working memory. The best model of working memory is that of Baddeley and Hitch [2], in which the working memory consists of the following components: a central executive, independent of modality, that resembles an allocation control of attention and two “slave” processors, an “articulatory loop”, that retains the information under a phonological form and a “visualspace sketch pad”, specialized in the visual space coding. Due to evidences of neuropsychology, Baddeley established a distinction between a speech-based phonological store and an articulatory refreshing process, which consists of: a passive phonological store that is directly linked to speech perception, and an articulatory process that is linked to language production. The technical literature of cognitive sciences registers 32 phenomena of the working memory classified according to: 1) the size and decay of verbal working memory; 2) context effects; 3) working memory representation; 4) chunking; 5) skilled memory; 6) spatial working memory and 7) long-term memory effects. The Baddeley and Hitch’s model can cover 26 of the 32 pointed phenomena, more than any other model. Unfortunately, it has not been reduced to a mathematical or computational form, and this is one of the greatest challenges for cognitive models, particularly for the cognitive reliability models. On the other hand, another model named ACT [3], which has a more elaborated cognitive architecture, has three types of memory: declarative memory, working memory and production memory. The ACT model can cover one third of the phenomena of the working memory. Another model, Schneider and Detweiler’s [2], uses a hybrid cognitive, connectionist/symbolist architecture, and thus competes with the Baddeley-Hitch’s model in terms of modeled 1 SMiRT 19, Toronto, August 2007 Transactions, Paper # M08/4