In view of current scientific knowledge, it seems premature to hypothesize a qualitative distinction between processes, networks, and structures involved in abstract processes from those based on perception, episodic, or procedural memories. Predictive thought and mental travel strongly rely, at different levels of consciousness, on past and ongoing sensory input, bodily information (e.g., interoception), and the results of perceptual elaboration.

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.

Hierarchical structures are rapidly and flexibly built up in the domains of human language and music. These domains require a tree-building capacity – “dendrophilia” – to dynamically infer hierarchical structures from sensory input (or to hierarchically structure output), based on subunits stored in a lexicon. This dynamic process involves a crucial class of abstracta overlooked in the target article.

Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

We propose that abstraction is an interpersonal process and serves a social function. Research on shared reality shows that in communication, people raise their level of abstraction in order to create a common understanding with their communication partner, which can subsequently distort their mental representation of the object of communication. This work demonstrates that, beyond building accurate models, abstraction also functions to build socially shared models – to create a shared reality.

Gilead et al. provide a unified account of predictive cognition in which abstract representations play an essential role. Although acknowledging the similarity to linguistic concepts toward the higher end of the proposed abstraction gradient, Gilead et al. do not consider the potential of their account to embrace phonetic and phonological speech sound representations and their neural bases.

Language comprehension of action verbs recruits embodied representations in the brain that are assumed to invoke a mental simulation (e.g., “grasping a peanut”). This extends to abstract concepts, as well (“grasping an idea”). We, therefore, argue that mental simulation works across levels of abstractness and involves higher-level schematic structures that subsume a generic structure of actions and events.

Although endorsing the authors’ concentration on the issue of abstraction, I critique (a) the philosophical nature of their abstract–concrete dimension, (b) their view of the brain–world barrier, and (c) their implicit positivist one-way hierarchy that has abstraction as the goal.

The costs of and returns from actions are varied and individually concrete dimensions, combined in heterogeneous ways. The many needs of the body also fluctuate. Making action selection efficiently track some ultimate goal, whether fitness or another utility function, itself requires representational abstraction. Therefore, predictive brains need abstract value representations.

To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

According to Gilead and colleagues, to be efficient abstraction requires a hierarchical organization of information into long-term memory. But, how and when are abstract representations consolidated into long-term memory and how are they integrated with pre-existing abstracta are questions not discussed by Gilead and colleagues. Here, we propose that these processes occur preferentially during offline periods such as sleep.

Although many simulations draw upon only one level of abstraction, the process for generating rich simulations requires a dynamic interplay between abstract and concrete knowledge. A complete model of simulation must account for a mind and brain that can bridge the perceptual with the conceptual, the episodic with the semantic, and the concrete with the abstract.

The commentaries address our view of abstraction, our ontology of abstract entities, and our account of predictive cognition as relying on relatively concrete simulation or relatively abstract theory-based inference. These responses revisit classic questions concerning mental representation and abstraction in the context of current models of predictive cognition. The counter arguments to our article echo: constructivist theories of knowledge, “neat” approaches in artificial intelligence and decision theory, neo-empiricist models of concepts, and externalist views of cognition. We offer several empirical predictions that address points of contention and that highlight the generative potential of our model.

Cumulative technological culture (CTC) refers to the increase in the efficiency and complexity of tools and techniques in human populations over generations. A fascinating question is to understand the cognitive origins of this phenomenon. Because CTC is definitely a social phenomenon, most accounts have suggested a series of cognitive mechanisms oriented toward the social dimension (e.g., teaching, imitation, theory of mind, and metacognition), thereby minimizing the technical dimension and the potential influence of non-social, cognitive skills. What if we have failed to see the elephant in the room? What if social cognitive mechanisms were only catalyzing factors and not the sufficient and necessary conditions for the emergence of CTC? In this article, we offer an alternative, unified cognitive approach to this phenomenon by assuming that CTC originates in non-social cognitive skills, namely technical-reasoning skills which enable humans to develop the technical potential necessary to constantly acquire and improve technical information. This leads us to discuss how theory of mind and metacognition, in concert with technical reasoning, can help boost CTC. The cognitive approach developed here opens up promising new avenues for reinterpreting classical issues (e.g., innovation, emulation vs. imitation, social vs. asocial learning, cooperation, teaching, and overimitation) in a field that has so far been largely dominated by other disciplines, such as evolutionary biology, mathematics, anthropology, archeology, economics, and philosophy.

This commentary draws connections between technological culture emergence and recent trends in using assistive technology to reduce the burden of Alzheimer's disease. By the technical-reasoning hypothesis, cognitively-impaired individuals will lack the cognitive ability to employ technologies. By the technological reserve hypothesis, social-motivational and cultural transmissibility factors can provide foundations for using technology as cognitive prosthetics even during neurodegenerative illnesses.

Following the tradition of comparing humans with chimpanzees placed under unfavorable conditions, the authors suggest many uniquely human technological abilities. However, chimpanzees use spontaneously tools in nature to achieve many different goals demonstrating technological skills and reasoning contradicting the authors contrast. Chimpanzees and humans develop skills through the experiences faced during their upbringing and neglecting this leads to fake conclusions.

Osiurak and Reynaud argue that children are not a good methodological choice to examine cumulative technological culture (CTC). However, the paper ignores other current work that suggests that young children do display some aspects of creative problem-solving. We argue that using multiple methodologies and examining how technical-reasoning develops in children will provide crucial support for a cognitive approach to CTC.

The target article reviews evidence showing that technological reasoning is crucial to cumulative technological culture but it fails to discuss the implications for the emergence of cumulative cultural evolution (CCE) in general. The target article supports the social view of CCE against the more ecological alternative and suggests that CCE appears when specialised individual-learning mechanisms evolve.