Purpose This paper aims to introduce the cognitive function synthesis (CFS) conceptual framework to artificial general intelligence. CFS posits that at the “core” of intelligence in hybrid architectures, “interdependent” cognitive functions are synthesised through the interaction of various associative memory (AM)-based systems. This synthesis could form an interface layer between deliberative/symbolic and reactive/sub-symbolic layers in hybrid cognitive architectures. Design/methodology/approach A CFS conceptual framework, specifying an arrangement of AMs, was presented. The framework was executed using sparse distributed memory. Experiments were performed to investigate CFS autonomous extraction, consciousness and imagination. Findings Autonomous extraction was achieved using data from a Wi-Fi camera with the CFS auto-associative AM handling “Sensor Data”. However, noise reduction degraded the extracted image. An environment, simulated in V-REP 3.3.1, was used to investigate consciousness and imagination. CFS displayed consciousness by successfully tracking/anticipating the object position with over 90 per cent congruence. CFS imagination was seen by its predicting two time steps into the future. Originality/value Preliminary results demonstrate the plausibility of CFS claims for autonomous extraction, consciousness and imagination.