The authors Ivancovsky, Baror & Bar aim to reconcile the phenomena of creativity and curiosity via a shared novelty-seeking basis. For this, they describe common cognitive key features such as memory, cognitive control, attention and reward including empirical evidence from network neuroscience. We agree with their efforts to reconcile those concepts under one explanatory framework. However, we argue that it can be further expanded by linking the novelty-seeking basis to the process of prediction error minimization – a common underlying computational principle both central to predictive coding (Clark, Reference Clark2013) and reinforcement learning theories (Glimcher, Reference Glimcher2011). This principle connects both phenomena as has been argued before (Friston et al., Reference Friston, Lin, Frith, Pezzulo, Hobson and Ondobaka2017; Van de Cruys et al., Reference Van de Cruys, Damiano, Boddez, Król, Goetschalckx and Wagemans2021).

According to predictive coding theories, our sensory and cognitive systems have a fundamental aim: Constructing reliable representations of the world to enable adaptive behaviour. To this end, the brain engages in a process of generating predictions about its own perceptual experiences, actions and cognitive processes serving as models of those experiences and processes (Den Ouden, Kok, & De Lange, Reference Den Ouden, Kok and De Lange2012). These predictions are subsequently compared with the corresponding incoming sensory, motor or cognitive input, resulting in the computation of a prediction error (Den Ouden et al., Reference Den Ouden, Kok and De Lange2012). The bigger the mismatch between input and predictions, the bigger the resulting error, prompting an update to those predictive models which ultimately forms the basis for learning (Friston & Kiebel, Reference Friston and Kiebel2009). Prediction errors can be unsigned, representing the magnitude of the surprise related to a perception or cognitive outcome. Prediction errors can also be signed, indicating the valence of the outcome (whether it is better or worse than expected) often related to reward (Den Ouden et al., Reference Den Ouden, Kok and De Lange2012). Importantly, the concept of prediction errors are inherently related to novelty because minimizing prediction errors by updating one's models entails constantly seeking out unexpected, novel information. This process again leads to more prediction errors generating a continuous cycle of learning and adaptation (Schwartenbeck, FitzGerald, Mathys, Dolan, & Friston, Reference Schwartenbeck, FitzGerald, Mathys, Dolan and Friston2015).

From this perspective, curiosity and creative problem solving are two phenomena that relate to different aspects within this same continuous prediction error minimization process. Consistent with this view, the phenomenology and neurobiology of curiosity (Gruber, Gelman, & Ranganath, Reference Gruber, Gelman and Ranganath2014; Gruber & Ranganath, Reference Gruber and Ranganath2019) and creative problem solving (Becker, Wang, & Cabeza, Reference Becker, Wang and Cabeza2023; Dubey, Ho, Mehta, & Griffiths, Reference Dubey, Ho, Mehta and Griffiths2021; Friston et al., Reference Friston, Lin, Frith, Pezzulo, Hobson and Ondobaka2017; Savinova & Korovkin, Reference Savinova and Korovkin2022), have both been explained via different kinds of signed and unsigned prediction error signals. In the following, we argue that curiosity reflects expected information gain while creative problem solving or at least its end result – the AHA experience – represents absolute information gain (Van de Cruys et al., Reference Van de Cruys, Damiano, Boddez, Król, Goetschalckx and Wagemans2021). Information gain quantifies how much a model is updated due to new information causing a prediction error.

Researchers commonly define curiosity as a motivational state that stimulates exploration and information seeking to reduce uncertainty (Gruber & Ranganath, Reference Gruber and Ranganath2019; Ivancovsky, Baror, & Bar). When we encounter something unexpected, like a clown at a professional gathering or an unfamiliar problem, it can trigger our curiosity and motivate us to explore novel information, possibly to understand the clown's presence or to attempt to solve the problem (Friston et al., Reference Friston, Lin, Frith, Pezzulo, Hobson and Ondobaka2017). It has been suggested that curiosity is triggered by strong prediction errors that are seen as indicators of potentially valuable future information (Gruber & Ranganath, Reference Gruber and Ranganath2019). Essentially, curiosity can be characterized as expected information gain, where prediction errors arising from unexpected events, such as encountering an unfamiliar problem, provide an estimate of how much a new piece of information (its solution) is expected to minimize these prediction errors, leading to a model update. Note, the more substantial the expected model update, the higher the new information's expected gain.

Creativity involves breaking away from typical expectations and generating novel and useful ideas or solutions (Mednick, Reference Mednick1962). Insight is a fundamental process in creative problem-solving that occurs when a non-obvious problem is solved via a novel solution approach often eliciting an AHA experience (Danek, Williams, & Wiley, Reference Danek, Williams and Wiley2020; Dietrich & Kanso, Reference Dietrich and Kanso2010). The AHA experience describes the solver's conviction that the solution arrived suddenly, is surprising, certainly correct, involves a feeling of pleasure and internal reward (Kizilirmak & Becker, Reference Kizilirmak and Becker2023). Due to its close conceptual proximity to surprise and reward, the AHA! experience has recently been reframed as a combination of different prediction errors tied to different aspects of the problem-solving process, such as the timing of a solution, the solvability of the problem or accuracy of the solution content (Becker et al., Reference Becker, Wang and Cabeza2023; Dubey et al., Reference Dubey, Ho, Mehta and Griffiths2021; Friston et al., Reference Friston, Lin, Frith, Pezzulo, Hobson and Ondobaka2017). For example, it is assumed that individuals maintain a metacognitive model of their abilities that predicts when they will solve a problem. A prediction error occurs when the solution is found faster than expected, generating a sense of surprise and internal reward (Dubey et al., Reference Dubey, Ho, Mehta and Griffiths2021). In that sense, the AHA experience during creative problem solving reflects the actual information gain. Actual information gain here describes the (not expected but) actual size of the prediction errors caused by the new piece of information (the solution) leading to a model update and ultimately to a more reliable representation of the world.

In sum, we argued that the shared novelty-seeking basis of curiosity and creativity can be related to one underlying computational principle of prediction error minimization. Curiosity corresponds to an expected gain (model update) for new information that has not yet emerged but whose size is estimated by a current prediction error. In contrast, the AHA experience in creative problem-solving corresponds to the actual gain (model update) of new information that has just become available resulting in a prediction error. In reinforcement learning and the predictive coding theories, this principle has been associated with several other phenomena, such as perception, decision making under uncertainty, memory and reversal, habit or reward-based learning (Friston et al., Reference Friston, Lin, Frith, Pezzulo, Hobson and Ondobaka2017; Glimcher, Reference Glimcher2011). Considering Ivancovsky et al.'s effort to reconcile creativity and curiosity through a common novelty-seeking basis, we believe that this computational principle represents an important perspective for further consideration.