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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Limits to Natural Selection01:38

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.For one, natural selection can only act upon existing genetic variation. Hypothetically, redtusks may enhance elephant survival by deterring ivory-seeking poachers. However, if there are no gene variants—or alleles—for redtusks, natural selection cannot increase the prevalence of...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...

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Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and

Dmytro Kucherov1, Serge Dolgikh2, Serhii Podelskyi1

  • 1Department of Intelligent Cybernetic Systems, State University "Kyiv Aviation Institute", Kyiv, Ukraine.

Frontiers in Computational Neuroscience
|July 2, 2026
PubMed
Summary

Biological intelligence uses resource constraints for efficient learning, unlike unconstrained artificial systems. A meta-strategy approach is key for artificial intelligence to navigate complex sensory environments effectively.

Keywords:
artificial cognitive systemsbiological cognitive systemsconstrained optimizationevolutionary intelligenceresource-constrained cognition

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Natural and artificial systems exhibit significant differences in learning processes, cognitive architectures, and resource utilization for complex sensory data processing.
  • Resource constraints, including computational capacity, memory, and energy, critically shape learning dynamics and environmental sampling strategies.
  • Freely Evolving, Weakly constrained (FEW) artificial systems often lack intrinsic prioritization mechanisms present in biological intelligence.

Purpose of the Study:

  • To theoretically and quantitatively model early-stage learning under resource constraints, comparing biological intelligence with FEW systems.
  • To formalize how limitations in energy, memory, and computation impact sensory exploration and learning dynamics.
  • To propose strategies for optimizing artificial learning systems in complex sensory domains.

Main Methods:

  • Development of quantitative models for sensory exploration and learning under varying resource constraints (strong vs. weak).
  • Analysis of cognitive cost gradients and their effect on exploration strategies in biologically constrained systems.
  • Introduction of a comparative framework to evaluate sensory traversal strategies in FEW systems.

Main Results:

  • Biologically constrained systems exhibit prioritized, depth-oriented exploration, leading to efficient learning due to steep anisotropy in the cognitive cost gradient.
  • FEW systems, despite abundant resources, suffer from inefficient, uniform, or random sampling, facing a paradox of unconstrained learning without intrinsic evaluative feedback.
  • No single sensory traversal strategy is universally optimal; a meta-strategy approach is suggested for adaptive selection and optimization.

Conclusions:

  • Resource constraints play a crucial functional role in enabling robust and efficient biological learning.
  • Adaptive meta-strategy selection is vital for artificial learning systems to achieve empirical success and accountability in complex environments.
  • Findings provide guidance for designing next-generation artificial intelligence capable of operating effectively under resource limitations.