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Observational Learning01:12

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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...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Introduction to Learning01:18

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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.
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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Perception01:28

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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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Related Experiment Video

Updated: Oct 9, 2025

Decoding Natural Behavior from Neuroethological Embedding
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Learning to sense from events via semantic variational autoencoder.

Marcos Paulo Silva Gôlo1, Rafael Geraldeli Rossi2, Ricardo Marcondes Marcacini1

  • 1Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, São Paulo, Brazil.

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|December 23, 2021
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Summary

This study introduces "learning to sense" to understand interdependent events using semantic vector spaces. A Semantic Variational Autoencoder (SVAE) effectively maps textual and spatio-temporal event data, improving event analysis and sensing capabilities.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Human reasoning involves monitoring interdependent events for decision-making.
  • Event datasets combine textual data with spatio-temporal features.
  • Mapping related events in a semantic vector space is key for contextual understanding.

Purpose of the Study:

  • Introduce the concept of "learning to sense" to emulate human reasoning.
  • Address the challenge of creating semantic vector spaces for events that satisfy both textual and spatio-temporal constraints.
  • Investigate a Semantic Variational Autoencoder (SVAE) for event analysis and sensing.

Main Methods:

  • Developed a Semantic Variational Autoencoder (SVAE) model.
  • Fine-tuned pre-trained embeddings using textual and spatio-temporal event data.
  • Employed a one-class classification baseline for comparison.

Main Results:

  • The SVAE model demonstrated superior performance compared to the baseline.
  • Experiments involved over one hundred sensors.
  • The SVAE successfully mapped related events in a semantic vector space, considering both text and spatio-temporal data.

Conclusions:

  • The proposed SVAE is effective for learning to sense by integrating textual and spatio-temporal event information.
  • The SVAE provides valuable tools for sensing scenarios, including visualization of decision functions and geographic impact heat maps.
  • This approach enhances event analysis and decision-making processes in complex environments.