## 1 Introduction  
The human brain, complex as it is as a result of unplanned evolution, stores memories in a rather disorganized way compared to the precise addressing system of computers [1, 2]. It is organized according to context, and topics are interwoven in complex associative networks and are retrieved by clues and associations, not by pure addressing in a memory as with a computer. Human memory is superior to that of computers in certain respects, for example, through automatic prioritization with regard to rewards and frequently used elements [3, 4]. However, memories that are unused soon decay into oblivion. Furthermore, unlike the addressing system of a computer, which is the result of planned engineering following the [PERSON] architecture, the human way of thinking is susceptible to the formation of false recollections through incorrect associations. However, perhaps the Cartesian mind-body dualism, which ignores the importance of brain-inspired embodiment for the development of human-like Artificial Cognition (ACo), leads to a dead end [5]. Personal experiences, partly mediated by social interaction and accessible to recollection, are essential for an Artificial Intelligence (AI) agent to develop cognitive abilities and learn about its environment. Such an agent should be able to imagine actions using mental representations before executing them and justify them using the cognitive ability of prospection, including planning, predicting, imagining scenarios, and possible future events. A cognitive architecture seems to be crucial in equipping the agent with such capabilities. In this work, we ask the question whether a social robot can be endowed with human-like memory capabilities by using a cognitive architecture for information retrieval and processing and a Large Language Model (LLM) that is also capable of vision. A key criterion here is the ability to retrieve recollections from memory using associated expressions and to prepare the ground for further human-like cognitive abilities. We show a technical method for implementation. LLMs like ChatGPT amaze the world with their seemingly human-like abilities in some respects. They provide advanced reasoning capabilities, blending intuitive and deliberate cognitive processes [6]. With their sophisticated probabilistic model of language use to generate a semantically consistent text, they require a form of effective memory that has surprising similarities with central features of human memory [7]. LLMs also help robotic systems improve their generalization capabilities in dynamic and complex real-world environments and can significantly increase their behavior planning and execution capabilities, enabling robots to engage with their environment in a human-like manner [8]. Vision-Language Models (VLMs) are multimodal AI systems created by combining an LLM with a vision encoder that gives the LLM the ability to \"see\". They provide assistance with complex tasks such as creating captions and answering visual questions [9]. VLMs are capable of performing a variety of tasks after learning relationships between images and language from large data sets, such as answering questions about images, finding sentences that correspond well with images, and finding image regions that correspond to texts. These skills can be used in many ways for robotics, for example, for robot movements, state recognition, object recognition, affordance recognition, relation recognition, and anomaly detection [10,11]. Cognitive architectures, on the other hand, refer both to a theory about the structure of the human mind and to a computer-based implementation of such a theory. They are particularly suitable for general problem solving in areas that are not clearly defined and for human cognitive modeling [12]. Cognitive architectures attempt to describe and integrate the basic mechanisms of human cognition. In doing so, they rely on empirically supported assumptions from cognitive psychology.