Ctrl + Alt + Inner Speech: A Verbal–Cognitive Scaffold (VCS) Model of Pathways to Computational Thinking
Author:
Open Access
- 2 December 2025
- journal article
- research article
- Published by MDPI AG in Journal of Intelligence
- Vol. 13 (12) , 156
Abstract
This theoretical paper introduces the Verbal–Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model posits inner speech (InSp) as the predominant cognitive pathway supporting CT operations in neurotypical populations. Synthesizing interdisciplinary scholarship across cognitive science, computational theory, neurodiversity research, and others, this framework articulates distinct mechanisms through which InSp supports CT. The model specifies four primary pathways linking InSp to CT components: verbal working memory supporting decomposition, symbolic representation facilitating pattern recognition and abstraction, sequential processing enabling algorithmic thinking, and dialogic self-questioning enhancing debugging processes. Crucially, the model posits these verbally mediated pathways as modal rather than universal. Although non-verbal architectures are acknowledged as possible alternative routes, their precise mechanisms remain underspecified in the existing literature and, therefore, are not the focus of the current theoretical exploration. Given this context, this manuscript focuses on the well-documented verbal support provided by InSp. The VCS Model’s theoretical contributions include the following: (1) specification of nuanced cognitive support systems where distinct InSp functions selectively enable particular CT operations; (2) generation of empirically testable predictions regarding aptitude–pathway interactions in computational training and performance; and (3) compatibility with future empirical efforts to inquire into neurodivergent strategies that may diverge from verbal architectures, while acknowledging that these alternatives remain underexplored. Individual variations in InSp phenomenology are theorized to predict distinctive patterns of CT engagement. This comprehensive framework, thus, elaborates and extends existing verbal mediation theories by specifying how InSp supports and enables CT, while laying the groundwork for possible future inquiry into alternative, non-verbal cognitive pathways.Keywords
All Related Versions
Funding Information
- Robin Hood Foundation (A1N-5A000000IYl2QAA)
- City University of New York
- City University of New York Central Office’s research grant to the Computing & Cognition Collaborative (C3) project
This publication has 75 references indexed in Scilit:
- Promoting students’ metacognition and self-regulatory efficacy through metacognitive scaffolding in mobile technology-enhanced interactive classrooms: The moderating role of inner speechEducation and Information Technologies, 2024
- Young children’s computational thinking: educator pedagogy fostering children’s play and learning with a tangible coding deviceThe Australian Educational Researcher, 2024
- Tracing the essence: ways to develop abstraction in computational thinkingEducational Technology Research and Development, 2022
- Insights into computational thinking from think‐aloud interviews: A systematic reviewApplied Cognitive Psychology, 2022
- Expecting some action: Predictive Processing and the construction of conscious experienceReview of Philosophy and Psychology, 2022
- Computational Thinking, Between Papert and WingScience & Education, 2021
- Computational Thinking Is More about Thinking than ComputingJournal for STEM Education Research, 2020
- Empiricism without magic: transformational abstraction in deep convolutional neural networksSynthese, 2018
- Self‐reported inner speech use in university studentsApplied Cognitive Psychology, 2018
- The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable AnalysisCognitive Psychology, 2000