# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 2 Related Work  
Recall or retrieval is a cognitive process [38] of remembering a thing or an event. While recalling, the brain activates a neural assembly that was created when the original event occurred [38]. In psychology, there is a plethora of articles studying the recall process. Psychologists used free-recall, cued-recall, and serial-recall as tools to investigate memory processes [39]. Recall has been used to study the effect of cognitive strategies, such as chunking and the use of mnemonics for memorization of things (such as large numbers) [40]. One interesting study reported the benefits of subsequent recall in retrieval operations where memories are related or competing [41]. The proverb \"practice makes a man perfect\" relates to the fortification of memory, and an investigation shows how retrieval plays an important role in this memory strengthening [42]. Technologies such as functional magnetic resonance imaging (fMRI), magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG) played an active role in validating many recall related hypotheses [43; 44; 45; 41]. Notable contributions to the modeling of memory recall procedures are observed. Based on the temporal context model [46; 47] of human behavior, human memory performance was modeled using a probabilistic approach during free-recall experiments [48]. A computational model of interaction between the prefrontal cortex and medial temporal lobe in memory usage was designed to study the prefrontal control in a recall process [49]. The model was a simple neural network with quick and flexible reinforcement learning exhibiting strategic recall. Another computational model differentiates recall from the recognition process depending upon the number of cues involved in the retrieval procedure [50]. For encoding, the model used an inference-based model of memory [51], and retrieval was carried out using a Bayesian observer model [52]. A large number of computational psychology contributions examining the recall process and recognition using the neural networks are available [53; 54; 55; 56]. An interesting study simulated the free-recall process using the ACT-R architecture [57], showing that the classical effect of primacy and recency can be recreated through rehearsal theory based upon ACT-R and Baddeley's phonological Loop [58]. ACT-R architecture was also used to propose a new theory of memory retrieval to predict for intricate serial and free-recall operations [59]. This research also focused on the prospects of associative learning by introducing a strengthening and decaying mechanism depending upon the similarity of the input stimulus. The serial recall has been modeled in a scientific contribution using ACT-R architecture to explain the processes involved while recalling a list of words [60]. The traditional ACT-R recall operations had a limitation: here, the memory access depends upon limiting the capacity of the activation process, consequently inducing errors in the contents being recalled. This theory overcomes the limitations by predicting the latency and errors in a serial recall process. The free recall process was also modeled using CLARION to determine the role of distractions in an incubation task [61]. This study made a striking observation that rehearsals play an important role in memory consolidation during the free recall procedure,and distractions can hinder the free recall and eventually effect memory strengthening. CLARION was also used to emulate, acquire, and expound human-centric data relevant to incubation and insight through free recall, lexical decision, and problem-solving tasks [62]. This article introduces a novel algorithm named geometric back-propagation, which enable us to simulate the recall simulation using RAN modeling. The main objective of the experiments in this paper is to demonstrate the role cue (activation) on abstract concepts (nodes) in recall operations.