So, what is general intelligence?

Community Article Published January 20, 2025

Summary

General intelligence is the ability of a system to generalise decision-making processes across both known and unknown situations in an adaptive way. This means it can apply knowledge learned in one context to new and different situations.
While basic intelligence involves processing inputs and producing outputs, general intelligence requires adaptability, especially in challenging or unfamiliar circumstances. This adaptability involves learning, anticipating, and resisting deterioration.
General intelligence is not just about problem-solving. It's a broader characteristic of decision-making that draws on multiple sources. These include:
- Subsymbolic generalisation: Learning associations from raw experiences, like deep learning models do.
- Generative generalisation: Using chaotic processes and dynamic systems to generate novel scenarios, which is important for fluid intelligence.
- Memory-driven generalisation: Integrating past experiences through abstraction, imagination, and re-representation.
- Temporal generalisation: Anticipating future states based on learned sequences and reward-based inversion.
- Distributed generalisation: Sharing knowledge across multiple agents.
- On-the-fly generalisation: Adapting knowledge in real-time, using working memory to integrate long-term and short-term memories.
- Similarity-based generalisation: Comparing new experiences to past ones and adjusting beliefs accordingly.
- Symbolic generalisation: Using rule-based reasoning, deduction, induction, and abduction to derive new knowledge.
- Cross-domain generalisation: Blending knowledge from different fields through analogy-making, concept blending, and other mechanisms.
- Metacognitive generalisation: Self-regulating learning and behaviour through conscious awareness, causal reasoning, and sophisticated inference.
In essence, general intelligence enables an agent to effectively generalise its internal computational processes ("reasoning") across various situations for adaptive behavior. This includes spontaneous learning, problem-solving, and applying knowledge from diverse domains to achieve goals. It combines associative learning, memory integration, anticipation, sophisticated reasoning, and other processes to achieve adaptability and flexibility in both artificial and biological systems.

Quotes

"Intelligence is the ability for an information processing system to adapt to its environment with insufficient knowledge and resources." P. Wang

"Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought." American Psychological Association

“We shall use the term 'intelligence' to mean the ability of an organism to solve new problems…” W. V. Bingham

“The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty.” F. Chollet

"The first of these assumptions is the widespread assumption that intelligence is a "quality of the mind," describable by such adjectives as clever, inventive, alert, etc. Actually, intelligence is an aspect of behavior; it has to do primarily with the appropriateness, effectiveness, and worthwhileness of what human beings do or want to do. ... it is not definable as a single trait or ability, and if treated as a capacity or as an ability, it must be perceived as an overall or global capacity ... To be rated intelligent, behavior must not only be rational and purposeful; it must not only have meaning but it must also have value, it must be esteemed. ... the capacity of an individual to understand the world around him and his resourcefulness to cope with its challenges." D. Wechsler

“Pragmatic general intelligence measures the capability of an agent to achieve goals in envi-ronments, relative to prior distributions over goal and environment space. Eﬃcient pragmatic general intelligence measures this same capability, but normalized by the amount of computational resources utilized in the course of the goal-achievement.” B. Goertzel

“Intelligence is popularly defined as the ability to learn, understand and deal with novel situations. The intelligent person is seen as quick-witted, acute, keen, sharp, canny, astute, bright and brilliant." P. Kline

Defining General Intelligence: Beyond Simple Problem-Solving

The term "general intelligence" has been a central topic across psychology, cognitive science, and artificial intelligence (AI). Each field provides its unique insights into what constitutes intelligence, yet the concept is often misunderstood, oversimplified, or reduced to specific traits like problem-solving. This post aims to delve deeper into general intelligence, exploring its layered, multi-faceted nature across diverse systems. The conventional interpretation of general intelligence often equates it with fluid intelligence. However, I propose that general intelligence, like intelligence in general, is a gradual attribute of a system, derived from a subset of processes. The more powerful these processes are, the higher the system's theoretical general intelligence. While fluid intelligence is part of this, it is only one aspect. It's also worth noting that some individuals in diversity, equity, and inclusion (DEI) circles may object to concepts like general intelligence due to its association with the "TESCREAL" community, but this doesn't affect its validity or intellectual significance. General intelligence in a system of decision-making processes refers to the system's ability to generalize decision-making across both familiar and unfamiliar situations in an adaptive way. So, what distinguishes general intelligence from intelligence itself? Intelligence typically refers to an agent that takes inputs, processes them in a systematic way, and produces outputs that are in harmony with its environment. This description doesn't specify the complexity or sophistication of the processes or reasoning occurring internally. For instance, an agent that generates random numbers without impacting itself or its environment, in a context where it has infinite energy and no consequences, could be considered just as intelligent as one that does nothing under the same conditions. If there are no consequences, any action—or inaction—is equally valid. In contrast, general intelligence involves adaptation in both internal and external environments, often including adversarial conditions. It manifests as an agent’s resistance to deterioration and requires learning. While intelligence can function with static rules and perform well in environments it was specifically designed for, general intelligence requires flexibility. For example, an artificial general intelligence (AGI) might write specialized programs in C code for certain tasks rather than relying on its own processes, and it would use relatively simple, static models to perform repetitive tasks efficiently, rather than running those tasks through its own complex mechanisms.

General intelligence of a system of decision making processes refers to the capacity of the system to generalize decision making processes across both known and unknown situations adaptively.

General intelligence can be defined as an agent’s ability to generalize its internal computational processes—often referred to as "reasoning"—across diverse situations to facilitate adaptive behavior. This includes spontaneous learning, the ability to solve new problems, and the capacity to apply knowledge from various domains to achieve specific goals. General intelligence is a multi-layered attribute that emerges from a combination of various decision-making processes rather than being reducible to a single cognitive skill.

The Difference Between Intelligence and General Intelligence

An important distinction lies between intelligence as traditionally conceived and general intelligence. Basic intelligence refers to an agent's capacity to process inputs, perform computations, and generate outputs in harmony with the environment. This intelligence can be static: a set of predefined rules may be sufficient to perform well in known, unchanging environments. However, general intelligence goes further. It demands adaptability across both internal and external environments, especially under adversarial or novel conditions. This requires the ability to learn, anticipate, and resist deterioration—traits that distinguish biological intelligence from narrowly focused AI systems. A general intelligence can adjust to shifting conditions, often through complex reasoning and learning mechanisms.

Different Interpretations of General Intelligence integrated above

Pragmatic General Intelligence in AI:
General intelligence is driven by multiple criteria:

Joy: Reusing knowledge
Growth: Acquiring or creating new, surprising knowledge
Choice: Preserving future freedoms or decision dominance
Intellectual breadth: How many different domains are covered
Goal achievement: Effectiveness in achieving objectives
Efficient resource utilization: e.g., skill acquisition efficiency or cognitive synergy

Adaptivity to New Situations and Embodiment

General Intelligence in Psychology and Cognitive Science:
The process overlap theory suggests that general intelligence (g) emerges from overlapping executive processes across domains such as visuospatial and verbal reasoning. Central to these processes are working memory and attention control, which are located in the fronto-parietal networks of the brain. These domain-general processes interact with domain-specific functions in various cognitive areas.

Cognitive architectures, which model the human mind, list several key processes that an AGI should support, including:

Recognition and categorization
Decision making and choice
Perception and situation assessment
Prediction and monitoring
Problem solving and planning
Reasoning and belief maintenance
Execution and action
Interaction and communication
Memory, reflection, and learning

Neuroscience:
Neuroscience presents various theories related to working memory, attentional integration, executive functions, and context-dependent adaptation. These processes also involve segmentation and sequencing of amodal and cross-modal representations across semantic, episodic, and perceptual memory. These mental representations function as a "language of thought," aligning with more abstract accounts of generalization.

Accounts on Generalization (see below)

The Multiple Sources of Intelligence Generalization

The key feature of general intelligence is the power to generalize an agent's internal computational processes, in AI often referred to as ‘reasoning’, to a variety of situations for adaptive behavior, also but not only spontaneously. This means that there are multiple sources for an agent's general intelligence, it is not reducible to what we usually associate with the word from psychology, namely problem solving in working memory with reasoning. It is better thought as an attribute of decision making behaviors of a system than merely a particular cognitive process, although what’s special about humans is that they have, not only like some other animals, dedicated but also more sophisticated machinery for on the fly generalization. This difference explains a lot of confusion about the concept, especially in AGI research.

What Sources of Generalizing Decision-Making Processes Across Situations?

Subsymbolic Generalization: Learning Through Associations
Generative Generalization: Chaos Theory and Dynamic Systems
Memory-Driven Generalization: Integrating Past Experiences
Temporal Generalization: Anticipation and Future-State Prediction
Distributed Generalization: Sharing Knowledge Across Agents
On-the-Fly Generalization: Adapting Knowledge in Real Time
Similarity-Based Generalization: Drawing Parallels from Experience
Symbolic Generalization: Rule-Based Reasoning and Abstraction
Cross-Domain Generalization: Blending Knowledge Across Fields
Metacognitive Generalization: Self-Regulation and Learning Awareness

Apologies for the misunderstanding! Here's the original content with enumerated headings, without any changes to the text:

Subsymbolic Generalization: Learning Through Associations
Subsymbolic generalization, also known as connectionist generalization, refers to the ability to generalize by learning reusable associations from raw experiences. This form of generalization is fundamental to modern AI systems, such as AlphaZero or GPT models. These systems are trained on a wide range of experiences and, through their training objectives, adjust their internal weights to better reflect the patterns in their environment. This process creates an associative "world model," essentially a direct map of their environment—whether it's through experienced episodes of gameplay or predicted texts from large corpora.
Similarly, in humans, the brain automatically forms new associations based on experiences. These associations, familiar in the processes of classical and operant conditioning, help enhance generalization. Relevant experiences enable us to generalize knowledge to locally similar situations, and in more advanced cases, even to broadly similar ones, as seen in deep learning models. By continuously learning from experience, both AI and biological systems improve their ability to adapt to new, related situations.
Generative Generalization: Chaos Theory and Dynamic Systems
The associative foundation of generalization can be enhanced by incorporating generativity and principles from dynamical systems and chaos theory. Traditional artificial neural networks (ANNs) are often designed to fit static data distributions, and while some are capable of online learning, true lifelong learning—the ability to continuously adapt without forgetting previous knowledge—remains an unsolved challenge in ANNs, as evidenced by the phenomenon of catastrophic forgetting. In essence, many ANNs function as massive lookup tables, comprised of highly complex but ultimately static rules.
In contrast, biological neurons, particularly due to dendritic non-linearities, can support a wide range of dynamic activity, far beyond what static ANNs can currently achieve. A single neuron’s complexity can be so broad that it would take a deep feedforward network to fully describe its behavior. Beyond the individual neuron, the human brain as a whole is considered a generative model of the world, functioning as a guided chaotic system. This system uses self-steering mechanisms like attractor states to create stable patterns amid dynamic processes.
At a fundamental level, effective generalization is not only about being well-embedded in relevant environmental experiences, but also about structuring knowledge on a chaotic backbone that fosters creativity. Such systems, whether biological or artificial, are capable of generating a vast array of novel but grounded scenarios, even without higher cognitive reasoning. This creativity is evident in the unusual content of dreams, which arise from these chaotic processes. Moreover, such generative models are highly potent for fluid intelligence, making neuromorphic computing—which seeks to emulate these biological dynamics—a promising area for future research in adaptive generalization.
Memory-Driven Generalization: Integrating Past Experiences
Memory integration plays a crucial role in generalization, closely related to the deep learning ability to abstract across a variety of experiences. This process involves integrating new experiences with pre-existing knowledge, enhancing the system’s ability to generalize. Memory integration encompasses various mechanisms, such as:
- Abstraction: Simplifying complex experiences into more general concepts.
- Imagination/Simulation: Using mental models to predict outcomes or test scenarios.
- Re-representation: Reorganizing existing knowledge to accommodate new information.
- Differentiation: Distinguishing new information from previous knowledge to refine understanding.
- Dreaming: For humans, dreaming serves as an offline generalization process, simulating scenarios based on prior experiences.
- Semantic and Episodic Memory Generalization: Integrating factual (semantic) and personal (episodic) memories to form generalized knowledge.
- Cognitive Synergy: The collaboration between different cognitive processes and modules to integrate knowledge and improve generalization.
  Through memory integration, systems—both biological and artificial—can apply previously learned information to novel situations, enhancing adaptability and decision-making. This process of merging new and old experiences allows for broader and more effective generalization across a wide range of contexts.
Temporal Generalization: Anticipation and Future-State Prediction
Anticipation and understanding represent a temporal form of memory integration, where knowledge is generalized over time. In deep learning, this involves learning from sequences or time-based data, enabling systems to predict future states of themselves or their environment. These predictions allow for policy optimization—aligning actions to maximize rewards or achieve specific goals.
By learning from past sequences and outcomes, agents can anticipate future situations and apply learned strategies to new contexts. This process involves:
- Temporal sequence learning: Deep learning models analyze patterns over time, understanding how current states transition into future ones.
- Reward-based inversion: Using feedback from scored reward functions, agents adjust their strategies to optimize future actions.
- Generalization of strategies: Learned strategies, once effective in past situations, can be generalized and applied to new, similar contexts, enhancing adaptability and decision-making.
  Through this mechanism, both AI and biological systems leverage their understanding of past events to anticipate future states, leading to more effective actions and better long-term outcomes.
Distributed Generalization: Sharing Knowledge Across Agents
This form of generalization involves the integration of knowledge from multiple agents, abstracted and applied to an individual agent's own experience and context. It serves as an efficient means of knowledge sharing across a population, fostering cross-pollination of ideas and insights among agents with diverse backgrounds and perspectives.
On-the-Fly Generalization: Adapting Knowledge in Real Time
On-the-fly generalization refers to the real-time adaptation of knowledge stored in working memory to address a current task or problem. It involves the flexible integration of both long-term and short-term memories to help achieve immediate goals. This process is described by an iterative updating model of working memory, where new information is retrieved and matched to similar items already stored, both on macro and micro levels. On-the-fly generalization enables both the adaptation of long-term memory for specific tasks and the spontaneous learning of new insights, where prediction errors trigger conscious recognition of new experiences. This allows for the labeling of new knowledge in working memory, making it available for immediate use. An example of this process is when you mentally simulate a scenario or plan a solution for a new problem.
Similarity-Based Generalization: Drawing Parallels from Experience
Similarity-based generalization involves comparing new experiences to those previously encountered, assessing their similarity, and integrating this knowledge into existing memory. Approaches like active inference illustrate this process, where pre-existing beliefs frame new experiences, either guiding behavior or adjusting the knowledge itself by incorporating new beliefs. Imprecise learning, which allows for the relaxation of model conditions, helps generalize to out-of-distribution instances, either during training or testing, by adapting the model to fit the data more flexibly.
Symbolic Generalization: Rule-Based Reasoning and Abstraction
Symbolic generalization and reasoning involve using formal rules to deduce new knowledge. Deduction applies established rules to make propositional predictions, while induction infers new rules from a limited set of observations. Abduction, on the other hand, derives an explanation for a given set of observations, or assigns the most probable rule based on similarities. All of these processes can be framed as cognitive control through active inference, where context-dependent generalization shapes decision-making. Symbolic processing, however, can be seen as a form of associative processing. Active inference loops constantly adjust beliefs to meet the constraints of cognitive control and planning. This means reasoning can collapse into abduction, where reasoning becomes an ongoing feedback loop, applying predictions as rules and revising them based on internal feedback. In this framework, deduction operates like associative reasoning, using abductive logic to align with constraints that are most likely to lead to logical conclusions. Induction introduces probabilistic rules based on observations and updates them according to their likelihood of being true. A single-process model of reasoning suggests that reasoning strength is evaluated on a continuum, depending on the decision criterion. For induction, the criterion is the plausibility of generalization, while for deduction, it’s logical validity (often involving further inferences). Reasoning, in this Bayesian view, interprets beliefs as descriptions of the world. Inductive reasoning can be seen as a generalization of deductive reasoning, where for deductive reasoning, the agent’s belief uncertainty is binary—either true or false.
Cross-Domain Generalization: Blending Knowledge Across Fields
Cross-domain reasoning and theory blending are key mechanisms for creativity and generalizing knowledge by connecting seemingly unrelated domains. These processes allow for the transfer and transformation of knowledge, enabling new insights and solutions. Several mechanisms support this, including:
- Analogy-Making: This process involves identifying structural correspondences between two domains, even when they appear superficially distinct. By recognizing underlying similarities, knowledge from one domain can be applied to another. Heuristic-Driven Theory Projection (HDTP) is a framework for analogy-making, where domains are represented using first-order logic and analogies are detected through anti-unification.
- Cross-Domain Generalization: This mechanism abstracts common features across multiple domains to form a more generalized understanding. By iteratively generalizing from specific examples, higher-order concepts and principles can emerge. For instance, recognizing shared properties across mathematical domains (like object collections) can lead to a more abstract understanding of arithmetic.
- Cross-Domain Specialization: This involves applying a generalized theory to a specific domain by tailoring abstract concepts to particular contexts. Terms and relationships from the general theory are translated into forms applicable to the target domain, allowing the abstract knowledge to solve concrete problems.
- Detection of Congruence Relations: This mechanism seeks to identify structural congruences between domains, enabling new ways of categorizing and representing knowledge. By identifying relationships that act similarly to equality in different domains, it facilitates the creation of new representation systems and deeper understanding of complex relationships.
- Concept Blending: Concept blending is the ability to combine elements from distinct domains to generate novel concepts. Drawing from Goguen’s formalization of concept blending, this process involves finding common generalizations between two domains and constructing a "blend space" that maintains the relationships established by the generalization. The process includes:
  - Input Domains (I1 and I2): Two initial conceptual spaces, each formalized as a theory.
  - Generalization (G): An abstract space that highlights commonalities between the two input domains.
  - Blend Space (B): A newly formed space resulting from the blending process, inheriting elements from both domains while creating novel properties.
  - Morphisms: Mappings that connect the conceptual spaces, ensuring the blend space preserves essential relationships.
    Additional cross-domain reasoning mechanisms include:
- Analogy-Making: Solving problems in new situations by transferring solutions from known contexts.
- Cross-Domain Generalization: Compressing knowledge, forming abstract concepts, and learning from few examples.
- Cross-Domain Specialization: Applying abstract knowledge to specific situations.
- Detection of Congruence Relations: Facilitating new representations, categorization, and concept formation.
- Re-Representation: Shifting perspectives to generate alternative representations of a problem.
- Frequency Effects: Exploiting statistical patterns to identify connections and facilitate generalization.
- Abduction: Generating hypotheses and reasoning from observed effects to potential causes.
  These mechanisms enhance our ability to blend knowledge across fields, fostering creativity and new insights.
Metacognitive Generalization: Self-Regulation and Conscious Awareness
While on-the-fly generalization can occur incidentally, it can also be intentional, driven by higher-order executive functions that sharpen reasoning and enhance world model coherence, aligning with Nietzsche's concept of the will to power. Successful intelligence, in this context, refers to the ability to achieve personal goals by adapting to, shaping, and selecting environments using analytical, creative, and practical intelligence.
Cognitive control can be understood as a form of covert and nested Bayesian inference, where causal knowledge of the world is used to break down a problem into manageable components, or "chunks," that fit within the limited capacity of working memory. It also involves binding individual items into a coherent whole, representing the situation or solution to the problem, or inferring sequences of actions for planning.
Emergent problem understanding and planning trigger selective attention, which is deployed to execute the current plan or adapt to newly emergent structures. Selective attention can also be seen as covert Bayesian inference, where task-relevant cognitions are processed more precisely, and working memory items that are important to the task are selectively primed. These primed items, which were involved in generating the task-relevant information in the first place, bias the retrieval of related knowledge, aiding in decision-making and action coordination.
Thus, attention shifts from top-down imposed sets to biases in memory retrieval for task-relevant representations, which then guide actions in sequences. Expectations about the environment and the impact of actions on outcomes are continuously monitored, compared, and evaluated for potential adjustments, updating the attentional set depending on the success of the focal action.

Detailed Mechanisms of Metacognitive Generalization: Self-Regulation, Reasoning, and Conscious Awareness

Causal Reasoning: Understanding Cause-Effect Relationships
System 2 Reasoning: Enhancing Cognitive Control and Problem-Solving
Fluid Intelligence and Working Memory: The Role of Dynamic Relationships
Recursive Active Inference and Sophisticated Inference: Predicting Beliefs, Adjusting World Models, and Reasoning with Constraints
Chunking and Generalization: Compressing Knowledge for Efficiency
Self-Regulation in Generalization: Motivational and Behavioral Adaptations
Metacognitive Control and Monitoring: Managing Learning and Directing Attention

1. Causal Reasoning: Understanding Cause-Effect Relationships

Causal reasoning, within a Bayesian framework, involves predicting and understanding cause-effect relationships by evaluating the temporal order of events and their interconnections. Agents develop beliefs about causal linkages through both overt actions and covert knowledge. They test hypotheses by interacting with their environment, and as actions are repeatedly observed in relation to a presumed cause-effect link, the agent’s belief in that relationship strengthens. Covert reasoning processes, such as counterfactual reasoning, allow the agent to imagine alternative scenarios and assess the plausibility of the cause-effect relation.

Key attributes that aid in establishing causal relationships include:

Whether A is a necessary condition for B.
Whether both events are real or one is imagined.
The availability of counterexamples.
Whether an action by the agent alters the outcome.
The repeatability of the observed pattern.
The extent to which the relation is explainable by further knowledge.
The temporal proximity of cause and effect.
Whether other observed causes exclude a hypothesized cause-effect link.

2. System 2 Reasoning: Enhancing Cognitive Control and Problem-Solving

System 2 reasoning involves rule-based thinking that builds upon causal reasoning, further refining cognitive control. Additional processes enhance reasoning, such as applying constraints, differentiation, and problem segmentation, and using sophisticated inference to break down complex goals. This form of reasoning helps manage task complexity by focusing on constraints and resources, ensuring efficient problem-solving. It aids in decision-making and aligns actions with overarching goals by segmenting tasks, evaluating relationships, and considering various possible outcomes.

3. Fluid Intelligence and Working Memory: The Role of Dynamic Relationships

Fluid intelligence involves recognizing relationships between different concepts and organizing them effectively in working memory. This ability allows for inductive and analogical reasoning, critical to structure mapping and solving problems. Dynamic relationships are represented through rhythmic activation patterns in the brain, coordinating elements in a way that organizes their relevance. As the brain organizes and desynchronizes information, cognitive control is used to maintain stability and coherence in mental representations, ensuring that the "train of thought" stays intact.

When memory capacity is reached, the brain must switch focus to maintain clarity, a function served by cognitive control.

4. Recursive Active Inference and Sophisticated Inference: Predicting Beliefs, Adjusting World Models, and Reasoning with Constraints

Recursive active inference involves predicting beliefs about beliefs, utilizing counterfactual reasoning and hypotheticals to project future outcomes. This form of inference enables agents to anticipate and adjust their world models, testing hypotheses against evidence, and seeking new information to refine understanding. Epistemic foraging—seeking out information that either strengthens or challenges existing beliefs—helps optimize the mental representation of the world and resolve tasks.

Through recursive inference, agents can form integrated solutions and create coherent models of situations, constantly updating their understanding as new data is incorporated.

In addition, sophisticated inference enhances this process by employing advanced reasoning strategies such as differentiation and constraints. By identifying key properties of concepts—such as ontological properties or ranges of values—agents can filter relevant information and re-represent knowledge to suit specific contexts. Constraints—whether hard or soft—are placed on potential solutions, limiting the scope of possible outcomes and optimizing cognitive efficiency. Differentiation and constraint satisfaction also facilitate domain segmentation, helping break down complex problems into manageable subcomponents for clearer decision-making.

5. Chunking and Generalization: Compressing Knowledge for Efficiency

Chunking involves the compression of detailed, granular knowledge into more abstract, general representations. This allows for more efficient memory usage and problem-solving. Through chunking, agents can use patterns and similarities to organize knowledge more effectively, applying learned experiences to new situations.

This ability to generalize is crucial for efficiency, particularly in adapting to unfamiliar or complex scenarios. The process can also result in the phenomenon of “tip of the tongue,” where an individual may feel they know the answer but cannot retrieve the precise representation.

6. Self-Regulation in Generalization: Motivational and Behavioral Adaptations

Self-regulation is key in guiding temporal decision generalization through motivational and behavioral adaptations. By creating an idealized self-image, agents can derive values and principles that guide action and behavior. These self-imposed frameworks help to define how knowledge should be applied and generalized across varying situations.

The abstract nature of self-regulation supports generalization by focusing attention on specific goals, increasing motivation, and optimizing cognitive resources for new challenges. Means-ends analysis involves comparing the current state of a situation to a desired goal state. By reasoning through the differences between these states, agents can determine actions to bridge the gap. This process supports effective decision-making by aligning current actions with long-term goals, selecting strategies to reduce disparities and achieve desired outcomes.

It also allows for the segmentation of tasks, where actions are selected to address specific needs that progressively move the agent toward the goal.

7. Metacognitive Control and Monitoring: Managing Learning and Directing Attention

Metacognitive control and monitoring work together to optimize learning and cognitive performance. Metacognitive control refers to the ability to self-regulate attention, directing focus toward valuable information and using effective strategies to enhance learning outcomes. It involves prioritizing tasks and managing cognitive resources for efficient learning.

Metacognitive monitoring, on the other hand, involves self-assessing learning progress, making judgments about what has been learned, and evaluating what will be remembered. Together, these processes ensure that attention is focused on the most relevant information and resources are allocated efficiently to maximize learning and problem-solving success.

Let’s repose the definition with this knowledge:

Definition of General Intelligence:

General intelligence can be defined as an agent’s ability to effectively generalize its internal computational processes—often referred to as "reasoning"—across diverse situations to facilitate adaptive behavior. This includes spontaneous learning, the ability to solve new problems, and the capacity to apply knowledge from various domains to achieve specific goals. General intelligence emerges from multiple sources and is not limited to the cognitive processes traditionally associated with reasoning in psychology (such as working memory and problem-solving). Instead, it is a characteristic of the decision-making behaviors of a system that integrates different levels of processing. These sources range from subsymbolic learning and associative memory, where knowledge is acquired through raw experience, to higher-level processes like cognitive control, rule-based reasoning, and metacognitive regulation. In summary, general intelligence reflects an agent's capacity to generalize across contexts by leveraging a combination of processes, including associative learning, memory integration, anticipation, sophisticated reasoning and more. It includes the ability to plan, problem-solve, and adapt efficiently to new environments, making it a central aspect of decision-making and behavioral flexibility in both artificial and biological systems.

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