A predictive processing framework of tool use

Abstract

In this paper I introduce the theory of predictive processing as a unifying conceptual framework to account for the human ability to use and innovate tools. I explain the basic concepts of predictive processing and illustrate how this framework accounts for the development of tool use in young infants and for findings in the neuropsychological and neuroscientific literature. Then, I argue that the predictive processing model needs to be complemented with a functional-evolutionary perspective, according to which the developmental and neurocognitive mechanisms should be understood in relation to the adaptive function that tools subserve. I discuss cross-cultural and comparative studies on tool use to illustrate how tools could facilitate a process of cumulative cultural and technological evolution. Furthermore, I illustrate how central premises of the predictive processing framework, such as the notion of Bayesian inference as a general principle and the role of prediction-error-updating, speak to central debates in evolutionary psychology, such as the massive modularity hypothesis and the trade-off between exploitation and innovation. Throughout the paper I make several concrete suggestions for future studies that could be used to put the predictive processing model of tool use to the test.