![]() ![]() ![]() Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as we find in many intelligence tests. This is the first brain-inspired neural network model of creative cognition, as most prior studies mainly provide theoretical and conceptual models of creativity.Ĭurrent deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generalization. The algorithmic implementation of our model would enable us to describe commonalities and differences between these creative processes based on the proposed neural circuitry. Our neurocomputational perspective is based on three creative processes based on novelty seeking, subserved by the prefrontal cortex, hippocampus, cerebellum, basal ganglia, and dopamine. The cerebellum is responsible for the precise control of movements, which is particularly important in improvisation. Novelty can also be about the different combinations of earlier learned processes, such as the motor sequencing mechanism of the basal ganglia. Thinking about novel solutions activates distant or loosely connected neurons of a semantic network that involves the hippocampus. The prefrontal cortex plays a role in creative ideation by providing a control mechanism. We argue that creative cognitive processes, divergent thinking, abstraction, and improvisation, are constructed on different novelty-based processes. We argue that this provides a biologically plausible mechanism that approximates a key component of symbol processing, exhibiting both the flexibility, but also some of the limitations, that are associated with this ability in humans.Ĭreativity is related to finding novel, surprising, and useful solutions. We show how indirection enables the system to flexibly generalize its behavior substantially beyond its direct experience (i.e., systematicity). Here, we provide an example of how the structure and functioning of the prefrontal cortex/basal ganglia working memory system can support variable binding, through a form of indirection (akin to a pointer in computer science). Whereas symbol processing is a fundamental feature of all computer systems, it remains a mystery whether and how this ability is carried out by the brain. It has frequently been argued that this ability relies on symbol processing, which depends critically on the ability to represent variables and bind them to arbitrary values. In our everyday lives we are often faced with arbitrary instructions that we must understand and follow, and we are able to do so with remarkable ease. The ability to flexibly, rapidly, and accurately perform novel tasks is a hallmark of human behavior. ![]()
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