Andreas Demetriou

In developmental research, it is assumed that development is ergodic, i.e., differences between children reveal how individuals change over time. Across four longitudinal studies, we examined ergodicity across cognitive systems—including attention, working memory, reasoning, and cognizance—timescales and developmental phases. We found that development was not globally ergodic. In Study 1, spanning ages 4–17, cross-sectional relations partially resembled patterns of change. Individual development involved overlapping, process-specific periods of acceleration, consolidation, and differentiation rather than one common developmental timetable. Study 2, spanning ages 4–12, showed short-term convergence and partial profile correspondence, with relational integration as the main carrier of change. Study 3, spanning ages 6–13, revealed that psychometric organization was highly stable across three waves, but children changing broadly in the first annual interval were not the same as children changing in the second. In Study 4, 5–7-year-old children were intensively trained on attention control and examined three times on other processes. The trained processes formed a recognizable common organization, but within-person coupling was substantially weaker than between-person coupling, rank correspondence declined with temporal distance between waves, and group dynamics represented individual children only approximately. Across phases, the carriers of change shifted from attention-related control, through relational integration, toward complex reasoning. Psychometric g, although strong across time, was very weakly related to developmental g (i.e., phase-sensitive mechanisms transforming states of psychometric g over time), indicating that population-level cognitive organization does not determine individual development. Implications for psychometric and developmental theories are discussed here.

We compared four large language models (LLMs; ChatGPT, Grok, Gemini, and DeepSeek) with humans on tests of cognitive development, assessing relational integration, linguistic awareness, general and domain-specific reasoning, and cognitive self-awareness to specify how LLMs compare with humans along cognitive development hierarchies. LLMs also discussed how Descartes’s Cogito applies to them and rated themselves on aspects of Artificial General Intelligence (AGI). Accordingly, we propose a novel interdisciplinary comparison of human and LLM capabilities that integrates developmental, cognitive, and psychometric psychology. Overall, the processes in humans and LLMs were highly similar. All LLMs attained perfect linguistic and metalinguistic performance. ChatGPT and Gemini outperformed university students in mathematics and causal reasoning. Grok performed slightly better and DeepSeek considerably worse. All LLMs underperformed in visual–spatial tasks. Self-evaluation profiles broadly mirrored performance profiles: ChatGPT and Grok rated themselves highly in reasoning and low in visualization, Gemini inflated visualization by reframing it as linguistic creativity, and DeepSeek consistently underrated itself. Each LLM restated Descartes’s Cogito differently, reflecting its own priorities, and denied having high AGI; these self-characterizations were generally stable about a year later. Therefore, LLMs displayed “subjective” task scaling, implying algorithmic or functional self-monitoring, capturing their architectural profile of performance, but they were modest in claiming above-human intelligence. We discuss implications for an integrated natural–artificial intelligence theory. We also sketch a developmental engineering model that might remove the limitations of each LLM.

We compared four Large Language Models (LLMs; ChatGPT, Grok, Gemini, DeepSeek) with humans on tests of cognitive development, assessing relational integration, linguistic awareness, general and domain-specific reasoning, and cognitive self-awareness to specify how LLMs compare with humans along cognitive development hierarchies. LLMs also discussed how Descartes’s Cogito applies to them and rated themselves on aspects of Artificial General Intelligence (AGI). Hence, we propose a novel interdisciplinary comparison between human and LLM capabilities integrating developmental, cognitive, and psychometric psychology. Overall, the structure of processes in humans and LLMs was highly similar. All LLMs attained perfect linguistic and metalinguistic performance. ChatGPT and Gemini outperformed university students in mathematics and causal reasoning. Grok performed slightly and DeepSeek considerably lower. All LLMs underperformed in visual–spatial tasks. Self-evaluation profiles broadly mirrored performance profiles: ChatGPT and Grok rated themselves high in reasoning and low in visualization, Gemini inflated visualization by reframing it as linguistic creativity, and DeepSeek consistently underrated itself. Each LLM restated Descartes’s Cogito differently, reflecting its own priorities, and denied having high AGI. Therefore, LLMs display human-like “subjective” task scaling implying algorithmic or functional metacognition, capturing their architectural gap between symbolic reasoning and visuospatial cognition, but they were modest in claiming top human intelligence. Implications for an integrated natural-artificial intelligence theory are discussed. Also, a developmental engineering model is sketched that might allow removing limitations of each LLM.

We compared four large language models (ChatGPT, Grok, Gemini, DeepSeek) with humans in reference to tests of cognitive development addressed to relational integration, linguistic awareness, general and domain-specific reasoning, and cognitive self-awareness. We aimed to specify how LLMs compare with humans along several cognitive development hierarchies. Given their theoretical importance for intelligence, LLMs were also asked to indicate how Descartes’s Cogito applies to them and self-rate on aspects of Artificial General Intelligence (AGI). There was a huge divide between verbal and logico-mathematical tasks, on the one hand, and visuo-spatial tasks, on the other hand. All LLMs attained perfect linguistic and metalinguistic performance. ChatGPT and Gemini matched or exceeded university-level human performance in mathematics and causal reasoning, Grok performed slightly lower, and DeepSeek weakest overall. All LLMs underperformed in visual–spatial tasks or reasoning tasks when shown visually as presented to children. Performance recovered when these tasks were presented in a fashion allowing LLMs to employ an analytical approach to visual patterns, signifying their unique architecture. Self-concept ratings broadly mirrored performance profiles: ChatGPT and Grok rated themselves high in reasoning and low in imagination, Gemini inflated imagination by reframing it as linguistic creativity, and DeepSeek consistently underrated itself. Each LLM restated Descartes’s Cogito differently as a description of itself and denied having much AGI. Hence, LLMs display human-like “subjective” task scaling implying algorithmic or functional metacognition, which captures the architectural gap between symbolic reasoning and imaginative cognition, but they are modest in claiming top human intelligence. Overall, LLMs display "savant-like intelligence" rather than top expert intelligence. Implications for an integrated natural-artificial intelligence theory are discussed. Also, a developmental engineering model is sketched that would allow removing limitations of each LLM.

We focus on active sensing and trait-linkage to show that a core mechanism, SARA-C ( S earch, A lign, R elate, A bstract, C ognize), underlying human g, may unify the emergence of cognitive complexity (CC) in evolution and development. Bayesian modeling shows how SARA-C evolves, advancing from reflex-driven behavior (Annelids) to associative learning (arthropods), abstraction (cephalopods), and hierarchical control (vertebrates) to reasoning and self-awareness (humans).

We present a theory of atypical development based on a developmental theory of the typical mind integrating developmental, cognitive, and psychometric theory and research. The paper comprises three parts. First, it outlines the theory of typical development. The theory postulates central cognitive mechanisms, such as relational integration, executive and inferential processes, and domain-specific processes underlying different environmental relations, such as visuospatial or quantitative relations. Cognitive development advances in cycles satisfying developmental priorities in mastering these systems, such as executive control from 2–6 years, inferential control from 7–11 years, and truth control from 12–18 years. Second, we discuss atypical development, showing how each neurodevelopmental disorder emerges from deficiencies in one or more of the processes comprising the architecture of the mind. Deficiencies in relational integration mechanisms, together with deficiencies in social understanding, yield autism spectrum disorder. Deficiencies in executive processes yield attention-deficit and hyperactivity disorder. Deficiencies in symbolic representation yield specialized learning difficulties, such as dyslexia and dyscalculia. Finally, we discuss clinical and educational implications, suggesting the importance of early diagnosis of malfunctioning in each of these dimensions and specific programs for their remediation.

We present a theory of atypical development based on a developmental theory of the typical mind integrating developmental, cognitive, and psychometric theory and research. The paper comprises three parts. First, it outlines the theory of typical development. The theory postulates central cognitive mechanisms, such as relational integration, executive and inferential processes, and domain-specific processes underlying different environmental relations, such as visuospatial or quantitative relations. Cognitive development advances in cycles satisfying developmental priorities in mastering these systems, such as executive control from 2-6 years, inferential control from 7-11 years, and truth control from 12-18 years. Second, we discuss atypical development, showing how each neurodevelopmental disorder emerges from deficiencies in one or more of the processes comprising the architecture of the mind. Deficiencies in relational integration mechanisms, together with deficiencies in social understanding, yield ASD. Deficiencies in executive processes yield ADHD. Deficiencies in symbolic representation yield specialized learning difficulties, such as dyslexia and dyscalculia. Finally, we discuss clinical and educational implications, suggesting the importance of early diagnosis of malfunctioning in each of these dimensions and specific programs for their remediation.

This commentary discusses the papers included in this special issue which focuses on the role of consciousness in various aspects of behavior. The commentary first outlines dominant theories of consciousness and awareness. It then focuses on the papers included in the special issue, outlining the main findings or claims of each and embedding them in the general frame of the current study of consciousness. Finally, we conclude by outlining some general premises a general theory of consciousness would have to satisfy.

We agree with the target article that assuming language-of-thought (LoT) is useful for the development of cognitive and developmental theories. We note that the target article is weak in its assumptions about development of LoT and possible existence of multiple LoTs. In response to these weaknesses, we outline several developmental principles for LoT development, showing how a developmental theory of LoT springs from probabilistic LoT. We suggest a system 1.5 of reasoning allowing interchange between Bayesian and logical rules as it fits purposes or domain.