Hierarchical Complexity and its emergence in psychology

From: jimruttshow8596

Hierarchical complexity is a concept describing the qualitative increase in complexity of cognitive processes, skills, and understanding. It is a fundamental property of mental processes [00:04:25] and a ubiquitous process in the construction of skill [00:08:13].

Historical Context and Emergence

The notion of hierarchical complexity represents one of the longest continuous research projects in psychology, emerging almost a century ago with Jean Piaget’s 1923 publication of The Language and Thought of the Child [00:04:04]. Earlier shades of the concept can be found in the works of James Mark Baldwin, Herbert Spencer, and natural philosophies of mind [00:04:15].

By the 1980s and 1990s, a “neo-Piagetian consensus” formalized the definition of stages across lifespan development [00:04:36]. Key figures in this development include:

• Michael Commons: Coined the term “Model of Hierarchical Complexity” [00:03:38] and articulated it as an almost mathematical construct [00:09:47]. His work provided a formal definition of stages in neo-Piagetian theory [01:03:38].
• Kurt Fischer: A prominent neo-Piagetian who conducted important empirical work to validate the construct of hierarchical complexity [00:03:30]. His “Dynamic Skill Theory” contextualizes the abstract model within a richly dynamical, embodied, and embrained person [00:09:51], showing how it manifests in human skill and behavior [01:00:37]. Fischer also pioneered applying dynamical systems modeling techniques from the complexity sciences to human development [01:00:37].
• Theo Dawson: Psychometrically innovated around the construct, developing “Lectica levels” for formal psychological measurement [00:03:18], which are considered the measurement standard for hierarchical complexity [01:00:37].

Hierarchical vs. Horizontal Complexity

A key distinction is drawn between hierarchical complexity (also called vertical complexity) and horizontal complexity [00:02:22].

• Horizontal Complexity: Refers to doing more of the same task at the same level of complexity [00:06:36].
  • Example: Tying a thousand shoes in half an hour involves more of the same task [00:07:14]. Taking an elevator from the lobby to the seventh floor, where each discrete sequence (pressing a button, waiting, walking in/out) is at about the same level and disjoint, is considered non-hierarchical in comparison [00:11:25].
• Hierarchical Complexity: Involves performing a qualitatively more complex task [00:06:50]. It requires integrating lower-order capacities that have already been mastered [00:07:54].
  • Example: Making a shoe is hierarchically much more complex than tying a shoe [00:07:07]. Disassembling, replacing a part, and reassembling a lawnmower engine is a more complex task, as every move depends on other moves and requires ancillary skills like organizing parts [00:11:55].

Every skill has a certain amount of hierarchical complexity [00:13:22]. For instance, focusing on an elevator button, accurately reaching out, and pushing it involves coordinating sensory-motor skills that were built through hierarchical integration [00:12:49].

Nature of Hierarchical Complexity

Hierarchical complexity is defined by the number of non-repeating recursions that coordinating actions must perform on a set of primary elements [00:35:30]. Higher-order actions organize and transform lower-order actions, producing new, non-arbitrary organizations that cannot be accomplished by the lower orders themselves [00:35:41]. This process is often referred to as “chunking” [00:16:11] or “hierarchical integration” [00:08:57].

• Chunking: The process where many lower-order processes are brought up and integrated by an emergent higher-order process [00:19:37]. This occurs across different scales, from chunking sensory-motor systems to chunking complex theories [00:17:37]. For example, a sprawling array of sensory-motor schemes (like dressing, brushing teeth) can be integrated into a single representation, “bedtime” [00:14:53].
• Recursion: A formal scoring criterion related to the non-arbitrary recursions needed to be integrated into a higher-order element to give it a specific score [00:37:42]. This means the concept can be applied to computer programming and other information processing systems [00:38:19].

Relationship to Broader Complexity Science and Emergence

The property of hierarchical complexity is viewed as an almost general property of information across the biological spectrum [00:19:02]. It reflects the broader phenomenon of emergence in complex adaptive systems [00:54:59], where complexity often emerges over multiple levels from simplicity [00:19:55].

• This process can be seen from atoms forming molecules, then cells, then multicellular organisms, organs, and systems of organs within an ecosystem [00:20:06].
• The human mind’s hierarchical complexity is another example of this emergence of complexity from simplicity [00:21:13].
• There is a continuity between human and universal cosmological evolutionary processes, particularly articulated by process philosophy or complexity science, regarding the emergent higher-order or hierarchical complexity [00:21:42].

Drivers of Hierarchical Complexity

Several factors drive the emergence of hierarchical complexity in the human mind:

• Working Memory Bottleneck: The remarkably small short-term memory (e.g., seven plus or minus two items) is a significant driving function for hierarchical complexity [00:27:57]. To work on more difficult problems, chunks of information must become denser, deeper, and more abstract [00:29:05].
• Complexity of the World: If the world were not complex, a working memory bottleneck wouldn’t matter [00:29:33]. The environment’s complexity, especially its abstract nature, is a driver [00:30:57].
• Epistemic Motivation: Humans have an innate need to understand, which drives information processing through the memory bottleneck [00:29:50]. Piaget distinguished between success (accomplishing a task) and understanding (knowing why it worked) [00:29:59]. Most animals operate at the level of success, while humans often cross a threshold into seeking understanding [00:30:13].
• Lawful Universe: The fact that the universe is lawful and patterns can be extracted incentivizes the biological cost of developing machinery to process reality [00:31:38].

Levels and Tiers of Hierarchical Complexity

Various models, including those by Commons, Fischer, and Dawson, describe similar sets of developmental levels, often seen as isomorphic [01:00:37]. Fischer’s model outlines three primary tiers:

1. Actions (Sensory-Motor): This tier begins with single actions (e.g., focusing on a face) [00:43:00], progresses to coordinating actions (e.g., looking and reaching to knock something off a table) [00:43:08], and then to coordinating systems of sensory-motor skills (e.g., looking, reaching, grasping, bringing to mouth and drinking) [00:43:17]. This rapid integration is evident in infancy and toddlerhood [00:44:44]. Early childhood engagement with diverse sensory-motor environments is crucial for building this foundational “pyramid” of skills [00:45:57].
2. Representations: This tier begins with the emergence of a “semiotic function,” where sprawling sets of sensory-motor experience are summarized in a single utterance or gesture (e.g., “bedtime,” “mommy,” “doggy”) [00:47:20]. Children start to use linguistic signs to represent non-present realities, enabling counterfactuals or lying [00:48:12]. As representations map together (e.g., “mommy water” for “mom, I would like some water”) [00:48:40], representational systems develop, allowing for endless descriptive stories [00:49:02].
3. Abstractions: This tier involves integrating many examples of representations into higher-order concepts [00:50:25]. Unlike representations, abstractions cannot be directly pointed at (e.g., “quality family time” generalizes across bedtime, dinner time, car time, but is not a specific event) [00:50:36]. This level corresponds to Piagetian formal operational levels [00:49:46]. Abstraction requires education [00:51:09]; basic understanding of democracy and law requires formal and systemic abstraction [00:51:20].
4. Principles (Meta-Systematic): This highest tier involves multi-systemic integration, creating a single theory from several different fields, like John Rawls’s “justice as fairness” [01:09:41]. It allows for norming the norms of conventions and making rules about the making of rules [01:10:37].

Application in Psychology and Education

Hierarchical complexity can be operationalized in educational and research contexts [00:09:36].

• Diagnostic Assessment: Lectica, co-founded by Zach Stein, developed a system for measuring cognitive development using hierarchical complexity [01:13:20]. Unlike standardized tests that simply pass or fail, Lectica assessments diagnose what a person understands and suggest the next best thing for them to learn, creating a specific learning sequence [01:15:46].
• Curriculum Development: By using hierarchical complexity as a universal ruler, domain-specific learning sequences can be rationally reconstructed [01:11:19].

Hierarchical Complexity vs. General Intelligence (Spearman’s G)

While there are correlations between hierarchical complexity and general intelligence (Spearman’s G) [00:57:50], they are not the same.

• Oversimplification: General intelligence tests (IQ tests) offer a summary statistic based on a narrow range of indices [00:59:00], often leading to the oversimplified and problematic classification of an entire person as “smarter” than another [00:59:37]. This approach is seen as a “demi-reality” that distorts social practice [01:00:49].
• Domain-Specific Measurement: Hierarchical complexity is not assigned to a person, but to a particular task they accomplished [00:59:23]. A person can demonstrate high hierarchical complexity in physics but lower complexity in small engine repair [01:01:29].
• Misapplication to Populations: Speculating about large populations reaching specific hierarchical complexity levels (e.g., “X percentage of people reach level 12”) is considered bogus due to insufficient empirical research [01:03:12]. Classifying whole populations or persons based on a single level is strongly discouraged [01:05:37].

Application in Leadership and Business

Hierarchical complexity has been applied to leadership development in various organizations, including intelligence agencies and municipalities [01:17:53].

• Complexity Gap: There’s often a “complexity gap” between the task demands of leadership roles (which have increased qualitatively over time) and the capacities of leaders [01:19:19].
• Focus Areas: Research found struggles emerging particularly in domains of:
  • Perspective Taking: Imagining how others might react to a decision [01:19:54].
  • Perspective Seeking: Actively seeking out the perspectives of employees [01:22:26].
  • Perspective Integration: Integrating multiple perspectives to form a comprehensive understanding [01:22:13].
• Expertise Fallacy: Individuals with highly developed expertise in one abstract and complex area often have a complexity deficit in other areas and may apply irrelevant skills [01:20:41].
• Diagnostic Tools: Assessments based on hierarchical complexity are used for leadership development, not primarily for hiring or firing. They provide diagnostic reports with educational supports, identifying areas for growth [01:24:04]. They can also help specify the minimum complexity threshold required for a particular role [01:24:27]. These measures are more ecologically valid than traditional personality tests (e.g., Myers-Briggs) or IQ tests because they directly relate to the job’s actual complexities [01:25:20].