It aids in the quick retention of information by keeping it in phonological loops. The articulatory loop, in essence, reactivates or “refreshes” representations that are rapidly decaying in a store. The store will keep auditory information alive for 2 seconds unless it is rehearsed. The phonological store is a structure that stores temporarily auditory information made up of a fast-decaying store and an articulatory loop. The phonological store is activated or refreshed through the articulatory loop through which inner speech is used to reactivate or refresh the representations. Phonological stores, in addition to holding auditory information, are a short decaying storage device that is designed to hold working memory.

A phonological store contains an articulatory loop, which is used temporarily to store verbal information. It is used as the central executive’s storage system, so information integration will be impossible unless it is integrated with other systems.Īudiological loops are a component of the WMM that deals with auditory information. What Connects The Phonological Loop And Visuospatial Sketchpad?Įpisodic buffer integrates information from phonological loops and visuospatial sketchespads, but also from long-term memory in a logical loop. It is assumed that the phonological loop manipulates speech-based information, whereas the visuospatial sketchpad manipulates visual images. What Is The Difference Between The Phonological Loop And The Visuospatial Sketchpad? The two systems are separate but interconnected, and they work together to help us remember information. The visuospatial sketchpad is responsible for storing and manipulating visual information, while the phonological loop stores and processes auditory information. This is the approach we take with the PBWM framework.The visuospatial sketchpad and phonological loop are both components of working memory, but they serve different functions. On the other hand, the goal of the neural network approach we advocate is to provide a more bottom-up model that tries to provide a reductionist account for the emergence of control-like processing based on underlying automatic mechanisms. In particular, the traditional AI approach, by going straight to solving a high level problem e.g., arithmetic. For example, the Leabra approach is being hybridized with the ACT-R approach in an architecture called SAL.Īll of these approaches are not mutually exclusive, but instead share many common ideas and can be complementary in many ways. Thus, these two approaches can be nicely complementary and hybrid approaches are being pursued. It is for these latter areas, that the biologically informed neural network approach has been most helpful. What they have done less well in has been in accounting for many of the kinds of things that might be considered less high-level, or even lower-level, things which we often take to be automatic. Perhaps for obvious reasons, it has turned out that these kinds of models of cognitive function have been most successful in dealing with the kinds of cognitive function that we listed as being at the highest level - that is, in modeling systems able to do formal mathematics and logic. This is not to say that these kinds of approaches are wholly without merit, only that they are fundamentally limited in what they can ultimately explain. A fundamental problem with this kind of approach is that it basically designs in the very functionality it aims to explain. There is an irony in this approach in that researchers taking this approach are using the very higher-level cognitive functionality they are trying to explain in order to design a system that will reproduce it. Motivated largely by the kinds of cognitive functions listed above, traditional AI has largely focused on a design-oriented approach using symbols that has focused on trying to figure out what it would take to solve a particular kind of problem, and then designing a model that does things that way. Finally, there is also the very influential traditional AI approach, which we will discuss briefly below. Another highly influential theoretical approach came from Tim Shallice (e.g., 1988,2007) who described a supervisory attentional system (SAS) framework. Probably the most influential model came from Alan Baddeley (1986), who especially focused on working memory, but also argued for a 'central executive.' In particular, he postulated two specific forms of working memory: 1) a phonological loop for maintaining verbal information and 2) a visuospatial scratchpad for spatial information. In this chapter we have focused on one particular theoretical framework, but there have been many other approaches described over the years.

Visuospatial scratchpad