Memory and Working with Memory: Evaluation of a Component Process Model and Comparisons with Other Models

1992 ◽  
Vol 4 (3) ◽  
pp. 257-267 ◽  
Author(s):  
Morris Moscovitch

A neuropsychological model of memory is proposed that incorporates Fodor's (1983) idea of modules and central systems. The model has four essential components: (1) a non-frontal neocortical component that consists of perceptual (and perhaps interpretative semantic) modules that mediate performance on item-specific, implicit tests of memory, (2) a modular medial temporal/hippocampal component that mediates encoding, storage, and retrieval on explicit, episodic tests of memory that are associative/cue dependent, (3) a central system, frontal-lobe component that mediates performance on explicit tests that are strategic and on procedural tests that are rule-bound, and (4) a basal ganglia component that mediates performance on sensorimotor, procedural tests of memory. The usefulness of the modular/central system construct is explored and evidence from studies of normal, amnesic, agnosic, and demented people is provided to support the model.


2020 ◽  
Author(s):  
Laura Israel ◽  
Felix D. Schönbrodt

Appraisal theories are a prominent approach for the explanation and prediction of emotions. According to these theories, the subjective perception of an emotion results from a series of specific event evaluations. To validate and extend one of the most known representatives of appraisal theory, the Component Process Model by Klaus Scherer, we implemented four computational appraisal models that predicted emotion labels based on prototype similarity calculations. Different weighting algorithms, mapping the models' input to a distinct emotion label, were integrated in the models. We evaluated the plausibility of the models' structure by assessing their predictive power and comparing their performance to a baseline model and a highly predictive machine learning algorithm. Model parameters were estimated from empirical data and validated out-of-sample. All models were notably better than the baseline model and able to explain part of the variance in the emotion labels. The preferred model, yielding a relatively high performance and stable parameter estimations, was able to predict a correct emotion label with an accuracy of 40.2% and a correct emotion family with an accuracy of 76.9%. The weighting algorithm of this favored model corresponds to the weighting complexity implied by the Component Process Model, but uses differing weighting parameters.


Author(s):  
Felipe Roma´n ◽  
Bert Bras

In order for companies to attain greater reductions in environmental burdens during manufacturing operations, they need to consider environmental aspects during their initial product development stages. This places an additional burden on designers and process engineers who already need to meet multiple objectives within tight constraints. Consequently, environmental aspects are rarely considered in their decision making process. Thus, if environmentally conscious decisions are to be made, the utilization of existing information must be maximized. In response to this problem we propose an approach for storing and reusing environmentally-related process information of similar component process plans via environmental process model templates. These templates will contain standard environmental information and models associated with manufacturing operations and thus should effectively support environmentally conscious decisions in component process planning and eventually design. We illustrate how the templates can be developed for a gear process planning example.


2012 ◽  
Vol 3 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Marcello Mortillaro ◽  
Ben Meuleman ◽  
Klaus R. Scherer

Most models of automatic emotion recognition use a discrete perspective and a black-box approach, i.e., they output an emotion label chosen from a limited pool of candidate terms, on the basis of purely statistical methods. Although these models are successful in emotion classification, a number of practical and theoretical drawbacks limit the range of possible applications. In this paper, the authors suggest the adoption of an appraisal perspective in modeling emotion recognition. The authors propose to use appraisals as an intermediate layer between expressive features (input) and emotion labeling (output). The model would then be made of two parts: first, expressive features would be used to estimate appraisals; second, resulting appraisals would be used to predict an emotion label. While the second part of the model has already been the object of several studies, the first is unexplored. The authors argue that this model should be built on the basis of both theoretical predictions and empirical results about the link between specific appraisals and expressive features. For this purpose, the authors suggest to use the component process model of emotion, which includes detailed predictions of efferent effects of appraisals on facial expression, voice, and body movements.


1979 ◽  
Vol 44 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Carol A. Pruning

A rationale for the application of a stage process model for the language-disordered child is presented. The major behaviors of the communicative system (pragmatic-semantic-syntactic-phonological) are summarized and organized in stages from pre-linguistic to the adult level. The article provides clinicians with guidelines, based on complexity, for the content and sequencing of communicative behaviors to be used in planning remedial programs.


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