## 2 Methods and Architecture of the Model  
In our study, we relied on an iterative approach, including the basis of a multimodal corpus and previous experiments from which we selected communicative functions that might distinguish conscious behavior if simulated by a companion robot. We tried to extend the previously developed architecture of a companion robot to simulate the observed functions and attempted to combine them with other, previously developed behavioral cues and strategies. Furthermore, we tested the impact of the modeled cues within the experiments to evaluate their contribution to the perception of the robot as a conscious creature. In this series of experiments, we extended the model to simulate the gaze management patterns in three symmetrical communicative situations. In previous experiments, we have noticed that people may follow some nonverbal cues of the robot and attribute internal states to some specific movements and to the changes of the robot's gaze direction. In the developed architecture, nonverbal cues are combined with verbal responses; a robot can perform gestures and/or utterances in response to different incoming stimuli, including users' utterances, tactile events, and users' movements. In order for the robot to react to the most essential stimuli, or to express the most essential internal states, we implemented a compound architecture in which the internal units compete and concurrently gain control over all or some of the robot's effectors to perform movements or phrases. This architecture is also applicable to the control of gaze direction; an applied gaze management system should be organized to hold and constantly solve the conflicts between numerous units of attention and expression, competing to gain control over the direction of eyes. [PERSON] ([PERSON], 2018) suggested the classic architecture of proto-specialists to handle such conflicts in a system, by which numerous cognitive or emotional units try to control the body of an artificial agent or robot. Following [PERSON], each proto-specialist constitutes a simple cognitive unit, responsible for handling some simple stimulus (e.g., a threat) or goal (e.g., hunger). Proto-specialists change their activation over time; the leading unit gains control over the required effectors, and then discharge, being satisfied, while losing the initiative in favor of other proto-specialists. This architecture has been extended by [PERSON] within the Cognition and Affects Project (CogAff). He has distinguished reactive units into different cognitive levels; while emotions and drives stay at the primary level, deliberative mechanisms (second level) or reflective reasoning (third level) can also gain control over the body of the agent to suggest the execution of longer and more sophisticated behavioral programs. In this architecture, deliberative reasoning may suppress emotions, conciliating the agent via rational reasoning. On the other hand, emotions can return the reasoning process of the agent to the object of desire or anxiety, thus reactivating themselves via the mechanism of positive feedback ([PERSON], 2018). Our companion robot, F-2, has quite simple hardware and is controlled by an extended software architecture, which processes natural language texts (oral speech and written sources), as well as the events from a computer vision system to provide the robot with reasonable reactions (see Figure 1). Its central component conceptually corresponds to the CogAff architecture and operates with a set of scripts that are activated by incoming events and sending their behavioral packages to be executed by the robot. Different incoming stimuli are processed within several components. Speech and text are processed by a semantic parser [25], which receives written text at its input, passes the stages of morphological and syntactic analysis, and constructs a semantic predication--a frame--for each sentence.