Scientists Invent New Hypotheses, Do Brains?

Nir Fresco, Lotem Elber-Dorozko

Research output: Contribution to journalArticlepeer-review


How are new Bayesian hypotheses generated within the framework of predictive processing? This explanatory framework purports to provide a unified, systematic explanation of cognition by appealing to Bayes rule and hierarchical Bayesian machinery alone. Given that the generation of new hypotheses is fundamental to Bayesian inference, the predictive processing framework faces an important challenge in this regard. By examining several cognitive-level and neurobiological architecture-inspired models of hypothesis generation, we argue that there is an essential difference between the two types of models. Cognitive-level models do not specify how they can be implemented in brains and include structures and assumptions that are external to the predictive processing framework. By contrast, neurobiological architecture-inspired models, which aim to better resemble brain processes, fail to explain important capacities of cognition, such as categorization and few-shot learning. The “scaling-up” challenge for proponents of predictive processing is to explain the relationship between these two types of models using only the theoretical and conceptual machinery of Bayesian inference.

Original languageEnglish
Article numbere13400
JournalCognitive Science
Issue number1
StatePublished - 1 Jan 2024


  • Bayes rule
  • Bayesian hypotheses
  • Cognition
  • Hierarchy
  • Hypothesis evaluation
  • Hypothesis generation
  • Inductive bias
  • Predictive processing

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Artificial Intelligence
  • Cognitive Neuroscience


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