Synthetic emotions and consciousness: exploring architectural boundaries

Summary

Artificial agents increasingly display emotion-like behaviors, raising the question of whether such systems risk instantiating consciousness. This paper proposes a hierarchical, dual-source control architecture for synthetic emotion that deliberately excludes architectural features associated with access-like consciousness, as defined by major theories. The architecture combines motivational signals from immediate needs with affective guidance from episodic memory to modulate action selection. The authors specify four engineering risk-reduction constraints: no global broadcast, no metarepresentation, no autobiographical consolidation, and bounded learning. They demonstrate that an emotion-like controller can satisfy these constraints, identify safe extensions, and map gradual transitions that increase access risk. The work provides a methodological template for converting consciousness-related questions into auditable architectural tests and preliminary audit indicators for governance frameworks.

Study at a glance

Abstract

As artificial agents display increasingly sophisticated emotion-like behaviors, frameworks for assessing whether such systems risk instantiating consciousness remain limited. This contribution asks whether synthetic emotion-like control can be implemented while deliberately excluding architectural features that major theories associate with access-like consciousness. We propose architectural principles ( A1 – A8 ) for a hierarchical, dual-source implementation in which (i) immediate needs generate motivational signals and (ii) episodic memory provides affective guidance from similar past situations; the two sources converge to modulate action selection. To operationalize consciousness-related risk, we distill predictions from major theories into four engineering risk-reduction constraints: ( R1 ) no content-general, workspace-like global broadcast, ( R2 ) no metarepresentation, ( R3 ) no autobiographical consolidation, and ( R4 ) bounded learning. We address three questions: ( Q1 ) Can emotion-like control satisfy R1–R4? We present a concrete architecture as an existence proof. ( Q2 ) Can the architecture be extended without introducing access-enabling features? We identify stable modifications that preserve compliance. ( Q3 ) Can we trace graded paths that plausibly increase access risk? We map gradual transitions that progressively violate the constraints. While we cannot resolve questions about consciousness, our contribution operates at three levels: on the engineering side, we present a modular, biologically motivated control architecture; on the theoretical side, we propose a control model of emotions and a methodological template for converting consciousness-related questions into auditable architectural tests; on the safety side, we sketch preliminary audit indicators that may inform future governance frameworks. The architecture functions independently as an emotion-like controller, while the risk-reduction criteria may extend to other AI systems. We offer no definitive answers about phenomenology, but we provide starting points for navigating uncertainty—tools intended to evolve as understanding advances.