AMAZING Theories of Human Functional Brain Development

Maturation theory proposes that cognitive and behavioural functions emerge when genetically pre-programmed brain regions reach biological maturity.

Skill learning theory views development as driven by interaction with the environment, where repeated engagement with a skill strengthens neural pathways and leads to increasingly efficient and localised brain processing. Early learning recruits widespread brain regions, but with practice, activity becomes more focused and specialised.

Interactive Specialisation explains development as a bidirectional process in which brain regions begin broadly responsive and become specialised through ongoing interactions within networks. Genetic biases influence which regions are more sensitive to particular inputs, but experience determines how connectivity patterns are refined over time. Functional specialisation reflects the tuning of interacting networks rather than the activation of isolated modules, and development is probabilistic, time-sensitive, and constrained by competition and plasticity.

TUNING NOT JUST ON AND OFF SWITCH LIKE SKILL LEARNING. AND THEY WORK TOGETHER ONE AREA ISNT JUST ONE FUNCTION.

Typical and Atypical Development

Typical development refers to the common patterns and sequences by which cognitive, motor, sensory, and language abilities emerge across childhood.

Atypical development refers to developmental trajectories that diverge significantly from these common patterns and may result in developmental disorders.

Typical development is used as a benchmark, not because all children develop identically, but because:

• Development naturally varies within a normal range

• Only by knowing this range can we identify meaningful atypicality

• Assessment and intervention depend on distinguishing:

  • normal variation

  • temporary delay

  • qualitatively different developmental pathways

Understanding development requires considering information across contexts (e.g. behaviour at home and school) and over time, rather than relying on single observations.

Brain Development and Functional Systems

Development involves changes in both brain structure and brain function.

Key features of brain development include:

• Ongoing cortical development after birth

• Synapse formation followed by synaptic pruning

• Continued myelination into adolescence and adulthood

• Changes in metabolism and neural efficiency

Functions such as motor control, vision, language, and sensory integration do not develop in isolation. Development depends on how brain regions interact as part of larger networks.

A central question in developmental science is:

Theories of Brain Development

Three main theories explain how brain development gives rise to skills and cognition:
Maturation Theory, Skill Learning Theory, and Interactive Specialization (IS) Theory.
They differ in what drives development, how brain areas become specialised, and the role of experience.

Maturation Theory (Maturational Hypothesis)

Core Idea

Specific brain regions are genetically pre-programmed to support particular functions. A skill emerges only when its associated brain region has reached biological maturity.

Development is therefore driven by brain maturation, not experience.

How Development Happens

• Brain structures follow a biologically determined timetable

• Each function is encoded in a specific brain region from early life

• A skill cannot be expressed until the relevant neural substrate is mature

• Experience does not cause development; it only allows an already-mature function to be observed

If a skill is absent, the assumption is that the brain region supporting it is still immature.

Brain–Function Mapping

• Static and localised

• Each function is tied to a specific, pre-determined brain area

• Functional activation appears only once the area is fully developed

• Development is often sudden and non-linear, as different regions mature at different times

This view treats the brain as a mosaic of specialised areas.

Causality

• Unidirectional

• Brain maturation → skill emergence

• There is only one route to development: from biology to behaviour

Analogy

• A mosaic or light-switch model:

  • The function is already installed

  • It becomes visible when the neural “switch” turns on

  • Experience does not influence the wiring

What This Theory Predicts

• Skills emerge abruptly rather than gradually

• Developmental timing should be similar across environments

• Early damage to a brain region should permanently impair its associated function

• Learning cannot occur before the relevant brain area matures

Why This Theory Struggles

• Many skills develop gradually, not suddenly

• Early brain injury does not always result in permanent functional loss

• The theory does not account for neural plasticity or reorganisation

• It ignores the role of connectivity and networks between brain regions

• It underestimates the influence of experience and environmental stimulation

Where It Works Best

• More basic sensory and motor functions

• Processes that depend strongly on biological “hardware” readiness (e.g. early visual capacities)

Even here, development is often more graded than the theory predicts.

Key Takeaway

Maturation Theory explains development as the biological unfolding of pre-specified brain functions. Skills are not built or shaped by experience; they are revealed once the brain is ready.

Its main limitation is that it treats development as biologically fixed and isolated, leaving little room for learning, plasticity, or interaction between brain systems — which is why later theories were proposed.

Skill Learning Theory

Core Idea

Skills develop through practice and experience, and the brain changes as a consequence of learning. Brain specialisation is not pre-set, but emerges through engagement with the environment.

How Development Happens

• Engagement with a task drives neural activation

• Repeated practice strengthens relevant neural pathways

• Brain organisation changes as skills become more efficient

• Maturation of brain structures occurs because skills are practiced, not before

Development is therefore experience-driven, not biologically unlocked.

Brain–Function Mapping

• Dynamic mapping

• A function is supported by different brain areas at different stages of learning

• Early stages:

  • Widespread brain activation

  • Strong involvement of frontal regions (planning, attention, working memory)

• Later stages:

  • Processing becomes more efficient

  • Activity becomes localised to key regions that act as hubs for the skill

Specialisation reflects proficiency, not age.

Causality

• Unidirectional

• Environment and experience → brain specialisation → skilled behaviour

• If the environment does not provide stimulation or demand for a skill, the brain will not specialise for it

Key Assumptions

• Learning any skill recruits multiple brain areas

• The brain becomes specialised through use

• There are no fully pre-devoted brain areas that simply “come online” when mature

• Different individuals may engage different neural routes while learning the same skill

Evidence Supporting Skill Learning

• Adult learning studies show:

  • Novel or unfamiliar stimuli activate widespread brain regions

  • With expertise, activation becomes more focused and efficient

• This pattern occurs even in expert adult brains, suggesting:

  • The developing brain may follow similar principles

  • Brain specialisation emerges through learning, not only maturation

What the Theory Explains Well

• Strong effects of environment, education, and training

• Cultural differences in skill development

• Individual differences in learning strategies

• Neural plasticity across the lifespan

What the Theory Struggles to Explain

• Some abilities emerge at similar ages across cultures

• Very young infants show early signs of neural specialisation

• Biological constraints clearly limit what can be learned and when

• Does not fully account for reciprocal influences between brain and environment

Relationship to Maturation Theory

• Skill learning arose as a response to limitations of maturation theory

• It rejects the idea that skills emerge solely because brain areas mature

• Instead, it argues that engaging with a skill causes the brain to specialise

• However, it still assumes one-way causality, just in the opposite direction

Key Takeaway

Skill Learning Theory proposes that:

• Skills drive brain development

• Brain specialisation is built through experience

• Learning recruits widespread networks before becoming efficient and localised

Its main limitation is that it cannot fully explain how biology and experience interact, which leads directly to the need for Interactive Specialization Theory.

Interactive Specialisation (IS) Theory (Johnson)

Core idea

Functional specialisation emerges through ongoing interactions within networks of brain regions, shaped by both biological predispositions and experience. Brain regions start broadly tuned, then become more selective as connectivity patterns are organised and refined.

How development happens

• Starting point: regions have broad response properties (not fully dedicated “modules”)

• Initial biases: some regions are more ready/permeable to certain inputs (e.g., visual, language)

• Network interaction: regions co-activate, compete, and cooperate during development

• Activity-dependent tuning: repeated patterns of activation change synapses and connectivity

  • strengthens useful connections

  • weakens irrelevant connections

• Outcome: specialisation reflects the fine-tuning of a network, not the “switching on” of a single area

• Development moves from widespread → more specialised, while still remaining network-based

Brain–function mapping

• Dynamic mapping (network form)

• A function is supported by multiple interacting regions from the start

• The “map” changes as connectivity becomes more efficient and selective

• Children can show similar behaviour to adults while using different (more distributed) neural patterns

Causality

• Bidirectional / circular causality

• Brain organisation influences learning and experience reshapes brain organisation

• Genes and maturation matter, but outcomes are probabilistic (not fixed) because experience tunes the system (“probabilistic epigenesis”)

Key assumptions / features

• Network-based: regions don’t develop in isolation; connectivity is central

• Transactional: genes, brain activity, body, and environment influence one another

• Experience is necessary: the environment must provide input that “tells” the system what to specialise for

• Specialisation relates to expertise (not just age): e.g., cortical specialisation may track vocabulary size more than chronological age

Example (how IS explains language)

• Some regions are initially biased toward language-relevant processing

• Language input (amount/type: deprivation, bilingual exposure, etc.) shapes which connections strengthen

• If rich input is missing during sensitive windows, the “ready” network may not specialise for language and may be recruited for other functions

What this theory predicts

• Early development: widespread activation and flexible processing

• Later development: increasingly selective networks as connectivity becomes tuned

• Different environments → different fine-tuning → individual differences in specialisation

• Plasticity and reorganisation are expected because functions are distributed across networks

• Atypical outcomes can reflect disruption at different points:

  • weak initial predispositions

  • atypical connectivity/coordination

• impoverished or atypical input

  Why this theory can be difficult / limitations

• Harder to test cleanly because it involves multiple interacting causes, not a single driver

• Requires detailed measures of connectivity and development over time (often longitudinal)

• Can be challenging to specify exactly which interactions are most important for a given skill (complexity is the price of realism)

Interactive Specialisation (IS) is designed to address the limitations of both maturation and skill learning theories. Maturation assumes that brain development drives behaviour, with experience playing a minimal role, while skill learning assumes that experience drives brain change. IS rejects both one-way explanations and instead proposes that brain development and experience continuously shape one another. Neither the brain nor the environment “waits”; development emerges through their constant interaction.

At birth, the brain does not contain fully formed systems for language, face processing, or mathematics. Instead, it contains regions that are more sensitive to certain types of input and a large number of loose, overlapping connections between regions. Early brain areas are therefore biased but not specialised. This means they are more likely to respond to particular kinds of information, but their roles are not fixed or exclusive.

Early in development, brain activity is broad and distributed. Many regions respond to the same input, and functions are supported by loose networks rather than single areas. For example, early face processing does not rely on one dedicated “face area”; instead, multiple regions respond when a baby sees a face. This widespread activation is not an error, but a normal and necessary stage of development.

Experience then shapes these networks through activity-dependent tuning. When the same regions are repeatedly activated together by the environment, their connections strengthen. Regions that are not consistently involved in processing that input become less connected. Over time, the system narrows, becoming more efficient and selective. Nothing suddenly switches on; specialisation emerges gradually.

In IS, specialisation does not mean isolation. It is not a brain area waking up or a module appearing. Instead, it reflects a network becoming more efficient, with fewer regions involved and stronger coordination among the most relevant ones. Specialisation is therefore best understood as refined teamwork within a network, not the activation of a single region.

IS is described as “interactive” because development depends on the interaction between three factors: initial brain predispositions, interactions among brain regions, and the environment. Some regions are better suited to certain inputs, regions always develop as part of networks, and environmental exposure determines how those networks are tuned. None of these factors acts alone.

Finally, IS proposes bidirectional (circular) causality. The current organisation of the brain affects what can be learned, learning changes brain organisation, and those changes influence future learning. This loop continues throughout development, making brain specialisation flexible, experience-dependent, and constrained by biology rather than fixed in advance.

FACE PROCESSING

Face processing is often used to illustrate how different theories of brain development make different assumptions about causality and specialisation, even when they refer to the same brain region. A commonly discussed area is the inferior fusiform gyrus, often called the fusiform face area (FFA), which in adults responds strongly to faces.

From a maturational perspective, face processing is explained by assuming that the fusiform gyrus is a pre-devoted brain area for faces. According to this view, the area is genetically programmed to process faces and will begin to do so once it reaches biological maturity. Face recognition therefore emerges when the relevant brain structure is ready, largely independent of experience. If face processing is not observed early in development, this is taken to mean that the fusiform gyrus has not yet matured.

From a skill learning perspective, the fusiform gyrus becomes specialised for faces because the environment is rich in facial input. Faces are among the most frequent and meaningful visual stimuli infants encounter, so repeated exposure drives learning. Early in development, many brain regions help process faces, but with increasing experience and expertise, processing becomes more efficient and localised, eventually concentrating in the fusiform gyrus. In this view, specialisation reflects practice rather than pre-programming.

The Interactive Specialisation (IS) model offers a more nuanced explanation. It proposes that the fusiform gyrus is initially biased or permeable to face-like stimuli, but is not inherently a face-only module. Early in development, face processing involves a distributed network of regions that interact with one another. Whether a specialised face-processing network emerges depends on both these initial neural biases and the quality and timing of experience with faces.

Evidence from atypical development supports the IS account. For example, children who experience early facial deprivation, such as those born with congenital cataracts, often show long-term impairments in face processing even after vision is restored. This cannot be fully explained by maturation, because the relevant brain areas are present and biologically intact, nor by skill learning alone, because later exposure does not fully restore typical face processing. Instead, IS explains this by proposing that the brain networks that were initially ready to specialise for faces were not tuned during a sensitive period, and were subsequently recruited for other functions.

IS therefore shows that specialisation is not guaranteed. A brain region can be ready to process a certain type of input, but without the appropriate experience at the right time, that specialisation may never occur. This explains why early experience matters, why development is sometimes irreversible, and why brain organisation reflects a history of interaction between neural predispositions and the environment.

Early visual deprivation from congenital cataracts disrupts activity and functional connectivity in the face network (Grady et al., 2014).

A study by Grady et al. (2014) examined young adults born with dense bilateral congenital cataracts who lacked patterned visual input until cataract removal in infancy, comparing them to controls using face-processing tasks and fMRI. Behaviourally, the cataract group showed marked impairments in configural face processing (spacing between features) but relatively preserved feature-based processing, replicating classic deprivation effects. Neurally, they recruited the same core and extended face-processing network as controls, including the fusiform gyrus, occipital face area, amygdala, and medial prefrontal cortex, indicating no relocation of face processing. However, face-specific responses were reduced, particularly in extended regions linked to emotion and person knowledge, and face-selective areas responded more strongly to objects, suggesting reduced selectivity and partial co-option by other functions. Connectivity analyses showed disrupted hemispheric organisation, with increased left fusiform connectivity and reduced right-hemisphere dominance, and altered relationships between fusiform connectivity and face-spacing performance. Together, these findings show that early deprivation does not erase the face system but mis-tunes its network organisation. In discussion, the authors argue for experience-expectant plasticity: early visual input is required to calibrate face-processing networks during a sensitive period. Although the brain is plastic, this plasticity leads to competition and premature specialisation when expected input is missing, limiting later recovery. As a result, later experience can improve performance but cannot fully restore typical configural face processing, demonstrating that plasticity is time-bound and constrained, not unlimited.

Plasticity in Interactive Specialisation (IS)

In Interactive Specialisation, plasticity does not mean unlimited flexibility. It refers to the brain’s capacity to reorganise through experience-dependent interactions, within time-sensitive and competitive constraints. Early in development, brain regions are broadly responsive, have biases toward certain inputs (e.g. fusiform regions toward faces), and are embedded in networks that are still being organised. Plasticity at this stage means that networks are open to shaping, connectivity patterns are not yet fixed, and functional roles are still negotiable.

The congenital cataract study illustrates this clearly. The face-processing network still forms, the fusiform gyrus remains involved, and face-related regions are not destroyed or absent, showing that plasticity is real and active. However, IS predicts that plasticity operates through competition between functions. During early deprivation, face input is missing while the brain continues developing, so networks cannot remain idle. Regions biased for face processing are recruited by other visual or object-processing functions, connectivity patterns stabilise around these alternative uses, and synaptic pruning locks them in.

By the time vision is restored, the face network exists anatomically but is mis-tuned and partially occupied by other functions. IS therefore predicts exactly what the study finds: reduced face selectivity, increased object responses in face regions, and abnormal connectivity, particularly disrupted right-hemisphere dominance.

Why this supports IS over other theories

The findings challenge a pure maturation account, because face-processing areas were biologically intact yet function did not fully recover once vision was restored. They also challenge simple skill learning, because later exposure and practice were insufficient to normalise face processing. Instead, the outcomes depended on early interaction between brain biases and environmental input, with timing and network tuning playing a critical role. The study therefore supports Interactive Specialisation by showing that plasticity is constrained, competitive, and time-bound, rather than unlimited.

Conclusion

There are three main theories of functional brain development—maturation, skill learning, and interactive specialisation—which differ in how they define functional specialisation, the direction of causality, and how functions are mapped in the brain. Maturation emphasises biologically pre-programmed brain areas, skill learning emphasises experience-driven change, and interactive specialisation integrates both by proposing that specialisation emerges through ongoing interactions between brain regions and the environment.

These theories are implicit in all developmental research. They shape the rationale of a study, influence methodological choices (such as which ages or brain regions are examined), and guide how findings are interpreted. Being able to identify the theoretical assumptions underlying a study is therefore essential for critically evaluating developmental neuroscience research.

Importantly, the theories are not mutually exclusive. Some developmental processes may be better explained by maturational constraints, while others depend strongly on experience and learning. Interactive specialisation provides a framework that can accommodate overlap between biological predispositions and environmental influences, particularly when explaining variability, plasticity, and atypical developmental outcomes.

Overall, understanding these theories provides a foundation for interpreting both typical and atypical development. Rather than offering a single correct explanation, they serve as conceptual tools for explaining how complex cognitive functions emerge from the developing brain.

REVSION

Theories of Human Functional Brain Development

Core question across all theories:
How does the adult pattern of functional brain specialisation emerge during development?

All theories differ along three dimensions:

• What drives development (biology vs experience)

• Direction of causality (brain → behaviour, behaviour → brain, or both)

• How functions are mapped in the brain (static areas vs dynamic networks)

1. Maturational Theory

Core idea

• Brain regions are genetically pre-specified for functions.

• A skill appears only when its brain area matures.

Key assumptions

• Development is biologically driven

• Experience does not shape brain organisation

• Skills emerge suddenly, not gradually

Brain–function mapping

• Static and localised

• One function ↔ one brain area (“mosaic / light-switch”)

Causality

• Brain maturation → behaviour (one-way)

Predictions

• Similar timing across children and cultures

• Early brain damage → permanent deficit

Limits

• Cannot explain plasticity, reorganisation, or experience effects

2. Skill Learning Theory

Core idea

• Experience and practice drive brain development

• Brain specialisation is a result of learning

Key assumptions

• Learning recruits widespread brain areas

• With practice, processing becomes efficient and localised

• Specialisation reflects proficiency, not age

Brain–function mapping

• Dynamic

• Early: distributed activation

• Later: focused “hub” regions

Causality

• Experience → brain specialisation → behaviour (one-way)

Predictions

• Training and environment strongly shape development

• Lack of exposure → lack of specialisation

Limits

• Struggles with early infant specialisation

• Ignores biological constraints and sensitive periods

3. Interactive Specialisation (IS)

Core idea

• Functional specialisation emerges through interaction between:

  • Initial brain biases

  • Network connectivity

  • Experience

Key assumptions

• Brain regions are biased but not fixed

• Development is network-based

• Experience is necessary but time-sensitive

Brain–function mapping

• Dynamic, distributed networks

• Specialisation = fine-tuning of connectivity

Causality

• Bidirectional / circular

• Brain ↔ experience ↔ future learning

Predictions

• Early: widespread activation

• Later: selective, efficient networks

• Missed early input → long-term atypical outcomes

Strengths

• Explains plasticity and its limits

• Explains individual and cultural differences

Key Differences & Commonalities

Maturation

• Biology-driven

• Static mapping

• Brain → behaviour

Skill learning

• Experience-driven

• Dynamic mapping

• Behaviour → brain

Interactive specialisation

• Biology + experience

• Network-based

• Brain ↔ behaviour

Commonality

• All aim to explain how specialisation emerges, not just where it ends up.

Applying the Theories (Exam Skill)

When given a scenario, ask:

If the account emphasises…

• Fixed timing, readiness, sudden onset → Maturation

• Practice, training, gradual improvement → Skill learning

• Timing + experience + connectivity + partial recovery → Interactive specialisation

Example hypotheses

• Maturation: “The skill failed to emerge because the relevant brain region had not yet matured.”

• Skill learning: “Reduced exposure limited practice, preventing specialisation.”

• IS: “Early deprivation disrupted network tuning during a sensitive period, leading to long-term atypical organisation.”

Skill Learning vs Interactive Specialisation (IS): Core Differences

Interactive Specialisation emphasises competition and timing in development. Brain regions begin broadly responsive but biased toward certain inputs and develop together within interacting networks. Through repeated experience, some regions are consistently co-activated, strengthening their mutual connections, while others lose involvement in that function. As a result, a particular network becomes committed to supporting the function, making alternative organisations increasingly unlikely. This commitment is time-sensitive, meaning that early experience plays a critical role in determining long-term functional specialisation.

Cataract example (why IS works better)

From a skill learning perspective, the brain is initially relatively unspecialised, and skills develop through experience and practice. Learning a new skill recruits many brain regions at first, but with repeated practice processing becomes more efficient and increasingly localised to a main hub. Applied to congenital cataracts, this theory assumes that once vision is restored, sufficient exposure to faces should allow the face-processing system to specialise normally, regardless of age. Plasticity is therefore viewed as ongoing and largely reversible, with repetition expected to drive recovery and reorganisation.

Skill learning theory assumes that functional specialisation of brain regions is driven by learning and experience.

In other words:

• Brain regions become specialised because they are used

• Practice and engagement with a skill cause neural organisation

• Without relevant experience, a region will not specialise for that function

Skill learning does not usually claim that:

• the brain has no structure at all

• or that nothing develops biologically

What it does claim is:

• biological maturation alone is not sufficient

• experience is necessary for functional development