DISCLAIMER: The following is generated by OpenAI’s DeepResearch on 2025-05-29.
Human thinking (cognition) comprises multiple mental processes – perception, attention, memory, language, reasoning, problem-solving, and consciousness – studied across disciplines. Cognitive psychology and neuroscience investigate how the brain enables functions like memory (storage and retrieval of information), reasoning (deduction, induction, decision-making), language (processing and producing speech/ideas), creativity, and conscious awareness. Developmental psychology examines how these abilities emerge and change from infancy through adulthood, while philosophy of mind addresses foundational issues (e.g. the nature of mental representations, mind-body relations, consciousness). In parallel, AI research seeks to model aspects of these processes, especially for artificial general intelligence (AGI). This report reviews major components of human cognition, key theories and findings in each area, and compares them with the state of AI modeling.
Key Components of Human Cognition
Memory: Human memory is multi-faceted. Working memory is a transient, active workspace (linked to prefrontal cortex) that holds and manipulates information for tasks like reasoning and language (pmc.ncbi.nlm.nih.gov). Long-term memory includes declarative (explicit) memory – facts and events consciously recalled – and non-declarative (implicit) memory such as skills and habits (pmc.ncbi.nlm.nih.gov). Neural studies show declarative memory relies on the hippocampus and medial temporal lobe to encode experiences, with consolidation (often during sleep) transferring memories to neocortex for long-term storage. Procedural skills involve basal ganglia and cerebellum. Working memory capacity grows in childhood and is thought to arise from interactions between prefrontal control and posterior cortical stores (pmc.ncbi.nlm.nih.gov).
Language and Communication: Humans uniquely use complex language. Neuroscience locates language networks mainly in the left hemisphere (e.g., Broca’s area in frontal cortex for production, Wernicke’s in temporal cortex for comprehension), though modern views emphasize distributed circuits and bilateral contributions. Theories vary: some posit innate grammatical rules (a “language acquisition device”), others emphasize learning and usage. Language strongly interacts with thought – e.g. vocabulary and grammar influence categorization (the Sapir-Whorf hypothesis debates this). In development, children typically acquire basic grammar by age 4–5 and continue refining language skills through adolescence.
Reasoning and Intelligence: Humans excel at diverse reasoning tasks. Deductive reasoning applies general rules to specifics (e.g. logic puzzles), whereas inductive reasoning infers generalizations from examples. Psychology identifies dual-process thinking: fast, intuitive “System 1” heuristics versus slow, analytical “System 2”. In everyday decisions, people often rely on heuristics (mental shortcuts) that yield “good enough” answers quickly (britannica.com). Nobel laureate Herbert Simon described human reasoning limits as “bounded rationality” (we satisfice rather than optimize)(britannica.com).
Intelligence is measured in many ways. Psychometrics finds a “general intelligence” or g factor underlying performance across tests (verbal, spatial, memory, etc.). Cognitive neuroscientists link higher g to efficient fronto-parietal networks. For example, the Parieto-Frontal Integration Theory (P-FIT) posits that variations in a distributed network of dorsolateral prefrontal, inferior/superior parietal, and other association areas predict individual intelligence differences (pubmed.ncbi.nlm.nih.gov). In other words, smarter individuals tend to recruit more effective frontal-parietal connectivity during reasoning. Intelligence also includes skills like fluid intelligence (solving novel problems) and crystallized intelligence (using acquired knowledge).
Creativity: Creative thought – generating novel, valuable ideas or solutions – involves both spontaneous and controlled processes. Neuroscientific studies highlight a balance between the brain’s Default Mode Network (DMN, active during mind-wandering, imagination) and Executive Control Network (lateral frontal-parietal areas for focused attention). The largest neuroimaging study to date found that creative ability correlates with dynamic switching between DMN and executive networks (nature.com). In effect, creativity arises when a person can fluidly alternate between free-associative thinking and deliberate evaluation. Too little or too much switching impairs creativity, suggesting an optimal “balanced” network dynamic (nature.com). Psychologically, creativity is often assessed via divergent-thinking tasks (e.g. generating many uses for an object), which show involvement of both associative (DMN) and control (prefrontal) processes.
Consciousness and Self-awareness: Human consciousness – the subjective experience of awareness – remains a central mystery. Cognitive science distinguishes phenomenal consciousness (“what it’s like” to experience something) from cognitive aspects (reporting thoughts, attention). Theories propose different mechanisms. Global Workspace Theory (GWT) suggests that conscious content arises when information in specialized processors is globally broadcast across the brain. In this view, a “neural ignition” in frontal-parietal circuits amplifies a representation so it can be accessed by multiple systems (pubmed.ncbi.nlm.nih.gov). For example, briefly presented words can be processed unconsciously, but become consciously accessible when this recurrent global ignition occurs. Other models (e.g. Integrated Information Theory) tie consciousness to how integrated or differentiated brain activity is, but empirical consensus is lacking. Practically, many cognitive processes (perception, language parsing) occur without awareness, whereas consciousness is needed for introspection, complex planning, and explicit report. Self-awareness (recognizing oneself as an agent) involves higher-order prefrontal networks; children typically pass mirror self-recognition around age 18–24 months. In sum, consciousness is shaped by brain dynamics (fronto-parietal and thalamic interactions) (pubmed.ncbi.nlm.nih.gov), but its subjective qualities are still debated in philosophy of mind.
Development of Human Cognition
Human thinking skills develop dramatically from infancy through adulthood, influenced by biology and environment.
Early Stages (Piaget and Beyond): Classical theory (Piaget) proposed that children progress through sensorimotor, preoperational, concrete operational, and formal operational stages, gaining abstract reasoning by adolescence. Modern research refines this: development is more continuous and influenced by experience and culture. For example, infants as young as 6 months show rudimentary number sense, and toddlers rapidly learn words (often hundreds per month during the “vocabulary explosion”). Social interaction is key: Vygotsky emphasized that language and guided learning (the “zone of proximal development”) shape cognitive growth.
Memory and Executive Functions: Young children have more limited working memory and executive control. The prefrontal cortex (governing planning, impulse control, working memory) matures into the third decade of life. As PFC circuits mature (via synaptic pruning and myelination), children improve at tasks requiring sustained attention, rule-following, and flexible problem-solving. Memory also develops: children start forming lasting autobiographical memories around age 3–4 (before that is “infantile amnesia”), reflecting hippocampal maturation.
Social Cognition: A milestone is theory of mind – understanding that others have beliefs and desires separate from one’s own. Typically by age 4–5, children pass false-belief tests, showing they grasp that someone can hold a mistaken belief about reality. Theory of mind allows empathy, deception detection, and complex social reasoning. Frontally, medial prefrontal cortex and temporoparietal junction are implicated in ToM. One study summarizes: “Theory of Mind (ToM)…is the ability to attribute mental states…to other persons and to understand that their behavior is guided by mental states”, which generally emerges in preschool years (frontiersin.org). In short, cognitive abilities build on earlier ones: language facilitation memory of social interactions, which builds reasoning skills, all sculpted by neural development and learning experiences.
Theoretical Models and Debates
Cognitive science and philosophy offer frameworks to understand the mind:
Heuristics and Dual-Process: Psychology highlights that much thinking is automatic. Kahneman and Tversky showed people use heuristics (quick mental shortcuts) that “produce serviceable results quickly” but can also cause biases (britannica.com). For instance, the availability heuristic judges frequency by how easily examples come to mind. This stands in contrast to idealized logical algorithms. The dual-process view posits an intuitive, fast “System 1” versus a slow, effortful “System 2” (analytical reasoning). Herbert Simon’s concept of “bounded rationality” captures this limited, satisficing approach to problem-solving (britannica.com). Thus, human reasoning is not always strictly logical but shaped by cognitive constraints.
Symbolic vs. Connectionist (Neural) Models: A long-running debate concerns how the mind represents and manipulates knowledge. Classical “symbolic” models treat thought as rule-based manipulation of symbols (like logic programs). In contrast, connectionist models (neural networks) use distributed patterns of activation. Early critics (Fodor & Pylyshyn) argued simple neural networks could not capture the compositional, systematic structure of thought (e.g. language syntax) (raphaelmilliere.com). In the 1980s, neural nets were revived with multi-layer architectures. Today, deep learning has dramatically narrowed the gap: modern networks can learn to recombine parts of experience in novel ways under the right training. For example, a transformer model trained by meta-learning achieved near-human compositional generalization on synthetic tasks (raphaelmilliere.com). This shows that with appropriate learning regimes, connectionist systems can approximate structured reasoning. The debate continues: some view nets as implementing symbolic rules in neural form, while others see them as capturing cognition with weaker structure demands.
Cognitive Architectures: To model whole-person cognition, researchers design cognitive architectures like ACT‑R or Soar. These frameworks specify modules (memory, vision, motor, etc.) and decision cycles, aiming to simulate human performance on tasks (such as reaction times, error patterns). ACT‑R, for instance, integrates symbolic chunks of knowledge with subsymbolic activation (inspired by neural processes). Such architectures have successfully modeled specific tasks in psychology and HCI, but scaling them to AGI is challenging. They illustrate how memory and procedural rules might combine, reflecting a functionalist view: mental states are defined by their roles, not by substrate (plato.stanford.edu).
Philosophy of Mind: Philosophical positions influence how we think about cognition. Functionalism – widely held in cognitive science – holds that mental states are defined by their functional roles in the system (plato.stanford.edu), supporting the idea that minds are like computers (substrate-neutral). Other debates include physicalism versus dualism (is mind just brain activity?), and whether language is the “language of thought.” Thought experiments (e.g. Searle’s Chinese Room) question whether symbol manipulation by a program is genuine understanding. Embodied cognition theories argue that thinking arises from sensorimotor grounding and interactions with the environment, suggesting cognitive models should incorporate a body and world, not just abstract rules. These debates highlight that any AI model of thinking must account not just for behavior but for the nature of representation and meaning.
Neural Basis of Thinking
Cognitive neuroscience maps thinking onto brain activity:
Distributed Brain Networks: No single “seat” of thought exists. Instead, functions arise from interacting networks. For example, working memory tasks engage both prefrontal cortex (maintaining goals) and parietal regions (storing information) as an emergent fronto-parietal system (pmc.ncbi.nlm.nih.gov). Similarly, general intelligence correlates with a distributed fronto-parietal network. The P-FIT model describes how dorsolateral PFC, inferior/superior parietal lobules, anterior cingulate and other regions jointly predict higher intelligence scores (pubmed.ncbi.nlm.nih.gov).
Neural Mechanisms: At the cellular level, learning is thought to involve synaptic plasticity (changes in connection strength) and network dynamics (oscillations, synchronization). Dopaminergic and other neuromodulator systems influence learning and decision-making (e.g. reward prediction errors in reinforcement learning are linked to dopamine signals). Neural circuits operate via spikes and nonlinear dynamics; recurrent loops can sustain information (as in memory), while feedforward pathways process sensory data.
Default Mode and Executive Networks: Neuroimaging has identified the default mode network (DMN), active during rest and mind-wandering, overlapping regions like medial prefrontal cortex and posterior cingulate. The DMN appears involved in self-generated thought, memory retrieval, and social cognition. The executive control network (lateral PFC and parietal) activates during goal-directed tasks. Creativity and insight seem to involve dynamic interaction between these networks (nature.com). The Global Workspace model posits that conscious thoughts ignite widespread frontal-parietal activation, “broadcasting” content to many areas (pubmed.ncbi.nlm.nih.gov).
Language and the Brain: Language engages left-hemisphere regions and a network involving frontal (Broca’s), temporal (Wernicke’s), and parietal sites. Neuroimaging and lesion studies show syntax processing in Broca’s area and semantics in posterior areas, but both interact across a language network. Damage to these areas can cause aphasia (e.g. fluent vs. nonfluent aphasia). Broader cognitive functions (working memory, attention) support language processing. The brain’s connectome (wiring) and plasticity (changing circuits with learning) underlie all thinking.
AI Approaches to Cognition and AGI
AI models have captured aspects of human cognition, with varying success:
Perception and Pattern Recognition: Convolutional neural networks (CNNs) and vision models now rival human accuracy on many tasks (e.g. object recognition). They are loosely inspired by the visual cortex hierarchy. In language, large Transformer models (e.g. GPT-4) process and generate text using massive pre-training on corpora (raphaelmilliere.com). These models can translate languages, summarize text, answer questions, and even write code, often appearing to “understand” language on a surface level. Similarly, deep RL systems (AlphaGo, DQN) learn to play complex games at superhuman levels via trial and error and deep value functions.
Memory and Learning: Some AI architectures include explicit memory components (e.g. Neural Turing Machines, Memory Networks) that read/write to an external store, roughly analogous to working memory or episodic memory. However, these are engineered constructs and do not yet mirror human memory flexibility. Neural nets primarily learn via adjusting weights (a form of synaptic plasticity) from data; unlike humans, most require very large datasets. Techniques like meta-learning and few-shot learning are attempts to make AI learn more like humans (generalizing from few examples).
Reasoning and Problem-Solving: Traditional AI includes symbolic logic engines and expert systems that explicitly manipulate symbols and rules (useful for deduction and structured problem-solving). More recently, neural models attempt reasoning: for example, transformers can solve some math or logic puzzles, and neurosymbolic systems combine deep learning with symbolic planners. Yet purely neural models often struggle with multi-step reasoning, planning under uncertainty, or common-sense inference. Classic successes like program synthesis and constraint solving rely on symbolic algorithms. Lake and Baroni (2023) demonstrated that a transformer with meta-learning can exhibit near-human systematic compositionality in a controlled task (raphaelmilliere.com), but this required careful training on structured tasks. This suggests modern AI can approach certain reasoning abilities, but typically needs human-like curricula
Language and Common Sense: While LLMs generate fluent prose, they lack grounded understanding. They often “hallucinate” facts or fail at basic common-sense reasoning. AI knowledge bases (e.g. Cyc, ConceptNet) encode world facts explicitly but struggle to integrate seamlessly with learning. Theory-of-mind (predicting others’ beliefs) is largely absent in current AI; multi-agent systems are beginning to explore it, but true empathy or understanding of intention is not yet achieved.
Creativity: Generative models (GANs, VAEs, diffusion) produce artwork, music, and text that can surprise and delight. In that sense, they simulate creative outputs. However, whether this equals human creativity – which often involves intrinsic motivation, emotion, and serendipity – is debatable. Current systems remix learned patterns rather than generating truly novel concepts beyond their training.
Consciousness and Agency: No AI system is conscious or self-aware in any meaningful sense. While some models have internal “workspaces” (e.g. transformer attention mechanisms) that loosely echo broadcasting, there is no evidence they “experience” or introspect. Philosophers note that computational architectures could in principle implement aspects of consciousness (e.g. a workspace for attention), but it is a big leap from functional architecture to subjective experience.
Limitations of Current AI Compared to Human Cognition
Despite impressive progress, current AI has clear limitations:
Narrow vs General: AI systems are typically narrow. They excel in the specific domains they are trained on but lack the broad adaptability of human cognition. A model trained on language cannot inherently perform vision tasks without retraining, whereas humans seamlessly integrate modalities.
Data and Efficiency: Humans learn robust concepts from few examples; young children can learn words from minimal exposure. By contrast, many AI models require millions of examples and vast computation. Sample efficiency and lifelong (continual) learning remain challenging in AI.
Common Sense and World Model: Humans possess intuitive physics, social norms, and common-sense knowledge (e.g. understanding gravity, cause and effect, or social cues). AI systems have no innate world model and must infer or be given knowledge. Without such grounding, AI can make obvious errors that humans would never make.
Robustness and Flexibility: Human cognition is robust to noise and novel situations; AI can be brittle. Neural networks can be fooled by adversarial perturbations or distribution shifts. They lack the ability to generalize convincingly outside their training domain without explicit retraining.
Explainability and Control: AI systems, especially deep nets, are often opaque (“black boxes”). Humans can usually explain their reasoning (at least partially) and can reflect on their thought processes; current AI cannot introspect or justify its “decisions” in human terms.
No Emotions or Motivations: Human thinking is intertwined with emotions, motivations, and values, which shape priorities and behavior. AI lacks genuine desires or feelings, and only “motivates” behavior according to programmed objectives (which may misalign with human values).
Future Directions and AGI Prospects
Building AI that truly emulates human thought will likely require new approaches blending cognitive insights and engineering:
Hybrid and Neurosymbolic AI: Combining neural networks’ learning with symbolic reasoning could harness the strengths of both (flexible learning and explicit logic). Early efforts (e.g. embedding logic into networks, or neural planners) aim to give AI better compositional reasoning and interpretability.
Brain-Inspired Architectures: Neuromorphic computing (chips that mimic spiking neurons, synapses) and insights from neuroscience (like dendritic processing, plasticity rules) may yield more brain-like efficiency. Large-scale brain simulations (e.g. Blue Brain Project) explore whether whole-brain emulation is feasible, but their relation to cognition is still unclear.
Embodied and Developmental Learning: Creating AI that learns through interaction with a physical or simulated body (like a child) may lead to more grounded understanding. Robotics combined with developmental curricula (curriculum learning, intrinsic motivation, curiosity-driven exploration) could yield more human-like cognitive development.
Meta-Learning and Continual Learning: Systems that learn how to learn and adapt across tasks (meta-learning) are a growing area. Humans constantly transfer knowledge to new domains; achieving this in AI could narrow the flexibility gap.
Consciousness and Self-modeling: Some researchers explore implementing ideas from consciousness science, such as workspace models or self-referential systems, to give AI a kind of introspective loop. However, there is no consensus on whether or how subjective consciousness can arise in machines.
Ethical and Philosophical Considerations: AGI raises questions about ethics, alignment with human values, and the nature of machine “mind.” Debates about embodied vs. purely computational thought, the necessity of emotions, and the criteria for “understanding” will guide future architectures.
In summary, human thinking is a rich interplay of biological structures, developmental processes, and psychological principles. While AI has modeled certain elements (perception, pattern recognition, even some language and strategic reasoning), it still lacks many core features of human cognition: flexible generalization, deep understanding, and consciousness. Ongoing research across cognitive neuroscience, psychology, and AI is gradually illuminating these gaps. By drawing on multiple disciplines – for instance, using neural data to inspire algorithms and cognitive models to structure learning – researchers aim to eventually create AI systems with more human-like intelligence. However, achieving true AGI will require not only more advanced architectures but also deeper insights into how cognition works in our own minds. Sources: Authoritative reviews and research are cited throughout (e.g., cognitive neuroscience of memory (pmc.ncbi.nlm.nih.gov), intelligence and brain networks (pubmed.ncbi.nlm.nih.gov), heuristics and reasoning (britannica.com), creativity networks (nature.com), consciousness theories (pubmed.ncbi.nlm.nih.gov), and recent AI/cognitive science analysis (raphaelmilliere.com)). These reflect current interdisciplinary understanding of human thought and its computational modeling.
Citations
Cognitive neuroscience perspective on memory: overview and summary - PMC
https://pmc.ncbi.nlm.nih.gov/articles/PMC10410470/ Cognitive neuroscience perspective on memory: overview and summary - PMC
https://pmc.ncbi.nlm.nih.gov/articles/PMC10410470/ Cognitive neuroscience perspective on memory: overview and summary - PMC
https://pmc.ncbi.nlm.nih.gov/articles/PMC10410470/ Favicon Heuristic | Definition, Examples, Daniel Kahneman, Amos Tversky, & Facts | Britannica
https://www.britannica.com/topic/heuristic-reasoning Favicon Heuristic | Definition, Examples, Daniel Kahneman, Amos Tversky, & Facts | Britannica
https://www.britannica.com/topic/heuristic-reasoning Favicon The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence - PubMed
https://pubmed.ncbi.nlm.nih.gov/17655784/ Favicon Dynamic switching between brain networks predicts creative ability | Communications Biology
https://www.nature.com/articles/s42003-025-07470-9?error=cookies_not_supported&code=a4144024-f8de-4d84-a9bb-d32232220963 Favicon Dynamic switching between brain networks predicts creative ability | Communications Biology
https://www.nature.com/articles/s42003-025-07470-9?error=cookies_not_supported&code=a4144024-f8de-4d84-a9bb-d32232220963 Favicon Conscious Processing and the Global Neuronal Workspace Hypothesis - PubMed
https://pubmed.ncbi.nlm.nih.gov/32135090/ Favicon Frontiers | Theory of Mind in Pre-school Aged Children: Influence of Maternal Depression and Infants’ Self-Comforting Behavior
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.741786/full Favicon Philosophy of cognitive science in the age of deep learning
https://raphaelmilliere.com/pdfs/millierePhilosophyCognitiveScience2024.pdf Favicon Philosophy of cognitive science in the age of deep learning
https://raphaelmilliere.com/pdfs/millierePhilosophyCognitiveScience2024.pdf Functionalism (Stanford Encyclopedia of Philosophy)
https://plato.stanford.edu/entries/functionalism/ Favicon Philosophy of cognitive science in the age of deep learning
https://raphaelmilliere.com/pdfs/millierePhilosophyCognitiveScience2024.pdf All Sources pmc.ncbi.nlm.nih Favicon britannica Favicon pubmed.ncbi.nlm.nih Favicon nature Favicon fronti
Questions
Should memory be symbolic (i.e. specific tokens), or stared implicitly (e.g. weights in a network, encoded with an encoder).