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The brain's superpowers: How does parallel processing analyze different stimuli simultaneously?
Parallel processing is an important ability of the human brain that allows stimuli of varying quality to be processed simultaneously. This ability is particularly evident in the visual system, where the brain separates what we see into four components: color, motion, shape, and depth, and then compares this information with stored memories to help us recognize what we are looking at. . Ultimately, this information is synthesized into a comprehensible vision, a process that is continuous and seamless. For example, when a person stands between two groups of people who are having different conversations, he may capture partial information of both conversations at the same time.
The concept of parallel processing describes how the brain allocates and uses its resources in an efficient way.
Parallel Processing vs. Sequential Processing
Serial processing, as opposed to parallel processing, involves processing information one at a time in sequence, meaning that the processing times do not overlap. The difference between these two processing styles is most evident when visual stimuli are targeted and processed. In sequential processing, elements are searched sequentially to find the target, and when the target is found, the search process ends. Conversely, if the target was not found, the search continued until it ended, resulting in decreased accuracy and increased time when more objects were presented.
In parallel processing, all objects are processed at the same time, so even if the displayed size varies, the time to complete may be similar.
Parallel Distributed Processing Model
In 1990, American psychologist David Rumelhart proposed the parallel distributed processing model (PDP) in an effort to study neural processes through computer simulation. According to Rumelhart, the PDP model views information processing as the result of interactions between so-called units, which can be either facilitatory or inhibitory.
These models are often inspired by the structure of the nervous system and mimic the nervous system organization of living organisms. They assume that information is represented in the brain as activation patterns and that information processing is performed using neuron-like units that interact through synapse-like connections. The activation level of each unit is updated based on the connection strength and activation level of other units.
Main components
The PDP model includes eight main aspects:
- Processing units: include abstract elements such as features, shapes, and words, and are divided into input units, output units, and hidden units.
- Activation State: represents the state of the system as a real vector that captures the representation of the system at any time.
- Output function: maps the current activation state to the output signal. Units interact with each other by transmitting signals.
- Connection Mode: Determines how the system reacts to any input.
- Propagation Rules: Produce a net input for each input type and combine the output vectors and connection matrices according to the rules.
- Activation rule: Produces a new activation state for a stimulus unit by combining the net inputs of related units.
- Learning Rules: Use experience to modify connection patterns.
- Environment representation: The environment in the PDP model is represented as a random function that varies over time.
These elements work together to allow the brain to process information more efficiently, but they also have their limitations.
ConclusionThe main limitations to parallel processing include brain capacity limits, momentary distractions, and processing limitations when performing complex tasks.
Parallel processing not only allows us to efficiently receive and understand the stimuli around us, but also involves how we make rapid cognitive responses. However, even so, there are still situations in which the brain cannot process completely in parallel. In this context, it becomes particularly important to understand how our attention affects this process. So, how will future research help us gain a deeper understanding of the brain's parallel processing capabilities and potential applications?
