Modelling Memory and Learning Consistently from Psychology to Physiology

Abstract

Natural selection pressures have resulted in the physical resources of the brain being organized into modules that perform different general types of information processes. Each module is made up of submodules performing different information processes of the general type, and each submodule is made up of yet more detailed modules. At the highest level, modules correspond with major anatomical structures like the cortex, hippocampus, basal ganglia, cerebellum etc. In the cortex, for example, the more detailed modules include areas, columns, neurons, and a series of neuron substructures down to molecules. Any one memory or learning phenomenon requires many information processes performed by many different anatomical structures. However, the modular structure makes it possible to describe a memory phenomenon at a high (psychological) level in terms of the information processes performed by the major anatomical structures. The same phenomenon can be described at each level in a hierarchy of more detailed descriptions, in terms of the information processes performed by anatomical substructures. At higher levels, descriptions are approximate but can be mapped to more detailed, more precise descriptions as required down to neuron levels and below. The total information content of a high level description is small enough that it can be fully understood. Small parts of a high level phenomenon, when described at a more detailed level, also have a small enough information content to be understood. The information processes and resultant hierarchy of descriptions therefore make it possible to understand cognitive phenomena like episodic, semantic or working memory in terms of neuron processes via a series of intermediate levels of description.

40 years of cognitive architectures: core cognitive abilities and practical applications

“…This applies to the graph-based knowledge representation where nodes refer to concepts and weights assigned to edges correspond to the associations between the concepts (Soar [304], CAPS [461], ADAPT [49], DUAL [288], Sigma [426], CELTS [149], NARS [578], Novamente [192], MAMID [245]). Naturally, activation can be used in neural network representation as well (Shruti [584], CogPrime [254], Recommendation Architecture [106], SASE [590], Darwinian Neurodynamics [156]). Activation mechanisms contribute to modeling many properties of working memory, such as limited capacity, temporal decay, rapid updating as the circumstances change, connection to other memory components and decision-making.…”

“…Priming is well studied in psychology and neuroscience [592] and two major theoretical frameworks for modeling priming are found in cognitive architectures: spreading activation (ACT-R [532], Recommendation Architecture [106], Shruti [486], CELTS [147], LIDA [169], ARS/SiMA [467], DUAL [414], NARS [577]) and attractor networks (CLARION [220], Darwinian Neurodynamics [156].). The current consensus in the literature is that spreading activation has greater explanation power, but attractor networks are considered more biologically plausible [325].…”

Synchronizing asynchronous learning - Combining synchronous and asynchronous techniques

“…Coward reports how investigation of the physiology of the brain provides insights into how the brain works as well as a way to emulate it in software [9]. This may provide new insights which could be applied to education.…”

Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex

“…Comparisons between this recommendation architecture and a wide range of other cognitive models demonstrate that it is better able to support higher cognitive processes in a plausible fashion [ 2 , 4 ]. There is extensive psychological, anatomical, and physiological evidence that the mammal brain has been constrained into this architecture [ 3 , 5 ] and that the different predicted architectural subsystems correspond with the major anatomical structures of the mammal brain, as illustrated in Figure 1 .…”