This article presents an overview of approaches to modeling the human psyche in the context of constructing an artificial one. Based on this overview, a concept of cognitive architecture is proposed, in which the psyche is viewed as the operating system of a living or artificial subject, comprising a space of states, including the state of needs that determine the meaning of a subject's being in relation to stimuli from the external world, and intelligence as a decision-making system regarding actions in this world to satisfy these needs. Based on this concept, a computational formalization is proposed for creating artificial general intelligence systems for an agent through experiential learning in a state space that includes agent's needs, taking into account their biological or existential significance for the intelligent agent, along with agent's sensations and actions. Thus, the problem of constructing artificial general intelligence is formalized as a system for making optimal decisions in the space of specific agent needs under conditions of uncertainty, maximizing success in achieving goals, minimizing existential risks, and maximizing energy efficiency. A minimal experimental implementation of the model is presented.
Figures & tables
Figure 1: Conceptual cognitive architecture of artificial psyche.
Figure 2: Illustration of the decision making process in state st at time t , described by vector s1 , where 6 transitions to 6 states in the future time t+1 are possible, represented by vectors s2′ , s3′ , s4′ , s5′ , s6′ , s7′ , based on 4 possible actions a1′ , a2′ , a3′ , a4′ . In the case of actions a1′ and a2′ , a non-deterministic transition to new states with different probabilities may occur. Action a1′ may lead to s2′ or s3′ , while a2′ may cause s4′ or s5′ . Actions a3′ and a4′ lead to a deterministic outcome. Each state vector includes a corresponding action vector a′ , a feeling vector f′ , and a need satisfaction vector y′ , associated with the overall utility U(s)=U(y) of transitioning to this state and the probability of this transition P(s) . Each action a′ can be associated with a statistically estimated "prospected utility" such as ∑U∗P . Although statistical and economic evaluation based on "prospect theory" [ 25 ] suggests that action a2′ is optimal (either maximum profit with moderate probability, or no profit with low probability), the same research shows that people usually intuitively choose actions like a3′ , where a smaller profit is guaranteed with higher probability.
Figure 3: Illustration of the decision making process in the state of spaces based on state transition graphs stored in the episodic memory of an agent, where experienced states s are agent’s memories kept as precedents linked by transitions, associated with per-transition utility and evidence counts while predicted states s′ are expectations that an agent experiences at any given moment while making a decision or calmly observing an environment.
Figure 4: Four-layer memory architecture (from bottom to top): a) long-term episodic memory keeping logs of agent’s interactions with the environment; b) "model" of the agent-environment interactions that is either neural-associative (left) or symbolic (right); c) attention focus with current observations; d) short-term memory with current operational context. Memory data in each of the four layers can be limited in three dimensions: 1) size, capacity, or time period, limited to storing only recent events on certain retention horizon; 2) scope of evidence perception or number of input and action modalities; 3) precision or accuracy of the data (e.g., in the form of floating-point numbers, integers, or Boolean values).
Figure 5: Visualization of the process of learning to play ping-pong against the opposite wall in the space of needs: the opposite wall is on top, the racket is in the middle, the ball is between the wall and the racket, at the bottom are the plots rendering functions of satisfaction or dissatisfaction for four needs: Happiness ("Happy"), Sadness ("Sad"), "Novelty", "Expectedness"; and a separate function of explicit (positive or negative) reinforcement presence ("Feedback").
Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations. This paper argues that the failure is architectural, not merely a matter of scale or training data, and that its origins lie in the psychological learning theories from which AI paradigms were derived. The argument proceeds in three stages. First, drawing on the systematicity debate in cognitive science and on the demonstration of Aizawa that neither connectionism nor classicism can make systematicity a structural consequence of the architecture, the paper establishes that the corrective techniques proliferating in modern AI, from chain-of-thought prompting to alignment through human feedback, function as auxiliary hypotheses that address symptoms without resolving the underlying architectural indifference to systematicity. Second, it traces the genealogy from psychological learning theory to AI methodology, showing that behaviourism, cognitivism, and constructivism each bequeathed a specific structural limitation to the AI paradigm it inspired: the exclusion of internal structure, the opacity of representation, and the absence of formal construction operators. A cross-cultural reappraisal of rote learning reveals a further underexploited pathway. Third, the paper introduces ReSynth, a trimodular conceptual framework that proposes the principled separation of reasoning, identity, and memory as a path toward architectures in which systematic behaviour is a structural consequence of design rather than a correction applied after the fact.
Alex Anvi Eponon, Ildar Batyrshin, Christian E. Maldonado-Sifuentes +1
Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
In our book Why machines will never rule the world [13, 14] we argue that arti- ficial general intelligence is mathematically impossible. This is because the human beings and the processes which exhibit intelligence are complex systems whose be- haviour cannot be captured by the kinds of models that we can generate with or without computers. Proponents of contemporary machine intelligence respond with two lines of argument: a theoretical one, grounded in the universal approximation theorems for neural networks and the Church-Turing-Deutsch principle; and an em- pirical one, grounded in rapidly rising scores on standardized benchmarks. In this communication we examine and reject both responses. First, we show serious issues in the physicalist counter-argument based on the Church-Turing-Deutsch principle. Second, we review recent evidence to the effect that prominent benchmarks are compromised by training-data contamination, flawed test construction, and strate- gic optimization. Our central argument remains: That models required to perform cognitive behaviour in open-ended, thermodynamically complex and non-ergodic environments are not and will not become achievable.
Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs. Conversational agents operate in a different regime: their sensorimotor loop is linguistic, their environment is a user's evolving mental state, and their consequential actions are speech acts, tool invocations, and strategic silences. This paper proposes a conversational reinterpretation of the OpenPsi motivational lineage, coupled to MetaMo's higher-level motivational scaffold, for agents built on a modular execution substrate. Homeostasis is recast in dialogue-native terms: the agent regulates competence, uncertainty reduction, affiliation, affinity, legitimacy, nurturing, and aesthetic coherence rather than bodily deficits. We propose three contributions: a ten-stage motivational processing pipeline that architecturally separates cognitive modulation from situational appraisal; a dual decision strategy blending urgency-driven fast response with deliberative multi-goal optimization; and an architecturally useful distinction between pre-action feelings and post-action emotions as functionally different forms of affect. We specialize the framework to two example agents -- CompanionAgent and ResearchAgent -- and sketch its extension to social robotics and domain-generic human-level AGI.