# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 1 Introduction  
Concepts are an important object of research in cognitive and psychological research. Usually, the conceptual representations are process-oriented, symbolic or distributed, and knowledge-based [1, 2, 3]. In general, a hierarchical structure defines an organization of concepts where the concrete concepts are placed in the lower level, and the abstract Concepts occupy the higher levels (see the example of the vehicle and cars in Figure 1). Therefore, abstract concepts are also seen as the generalization of concrete concepts [4, 5]. Abstract concepts are studied mathematically [6] and theoretically [7, 8], but computational studies are scarce [1]. This article uses a computational model, regulated activation network (RAN) [9, 10, 11], capable of building representation of convex abstract concepts, which are later used in recall simulations. The prime aspect of this article is to emulate the recall procedure, that can be viewed as the cognitive process of remembering. Context plays an eminent role in the recall of concrete concepts (such as the word \"table\"), which is often termed as concreteness effect. The concreteness effect is expressed through the dual-coding theory [12, 13] and the context availability hypothesis [14]. According to this theory, individually, abstract concepts are abstruse in context retrieval when compared with concrete concepts; therefore, theirrecall procedure is complex. However, an interesting work suggests that context retrieval of abstract concepts is possible when response pairs of abstract concepts are related to one another, thus providing context for the abstract stimuli [15]. In RAN modeling we can learn associations among concepts (including the abstract concepts). These learned associations provide adequate context relations among the abstract concepts. In this work, we exploit these learned associations among the abstract concepts to simulate a regulated recall operation. In recent years, advances in technologies have played an essential role in cognitive and psychological research, e.g., use of devices like GP3 [16], TheEyeTribe [17], and electroencephalography (EEG) [18; 19] to study visual attention. In this article, we focus on representations of computational models that are also very useful in understanding the psychological and cognitive phenomena, validating the existing cognitive theories, and helping to formulate fresh ideas related to cognition [20; 21; 22; 23]. The representations of these computational approaches are either symbolic (amodal), distributed (multimodal), or hybrid [24], which helps in simulating or understanding various cognitive phenomena. Below are a few examples of computational models or architecture used to study psychological and cognitive phenomena based upon their representation:  
* Symbolic: Adaptive Control of Thought-Rational (ACT-R) [25] is a symbolic architecture intended to model memory [26] and simulate attention [25; 27], decision-making [28], recognition [29], and forgetting [29]. * Distributed: Multimodal approaches such as artificial neural networks (ANNs), including the restricted Boltzmann machine (RBM) [30], deep neural networks [31], stacked auto-encoders [32], and convolution neural networks (CNN) [33], have significant contributions in feature recognition [34] and distributed memory representation [35]. Methods like Random Forests have also been used in studies related to visual attention [36]. * Hybrid: Cognitive architectures like Connectionist Learning with Adaptive Rule Induction On-line (CLARION) [37] simulate scenarios related to cognitive and social psychology. This article makes use of the RAN's hybrid nature and modeling to build the representation of convex abstract concepts and further simulate recall of abstract concepts. F First, the model generation takes place with four basic steps of the RAN approach [9], i.e., concept identification, concept creation, inter-layer learning, and upward activation propagation.