10. Predictive Processing Flashcards
Q: What is predictive processing in cognitive science?
A: Predictive processing is a theory suggesting that the brain constantly makes predictions about the world and updates these predictions based on sensory input. It views perception as an active process of prediction and error correction rather than passive sensory data reception.
Q: How does predictive processing use Bayesian inference?
A: Predictive processing relies on Bayesian inference to make predictions. The brain combines prior knowledge with incoming sensory data to generate hypotheses and update beliefs, aiming to minimize prediction errors.
Q: What is a prediction error in the context of predictive processing?
A: A prediction error occurs when there is a mismatch between what the brain expects to perceive and what it actually perceives. The brain aims to minimize these errors to better understand and navigate the world.
Q: How does binocular rivalry provide evidence for predictive processing?
A: In binocular rivalry, when different images are shown to each eye, the brain alternates between seeing the two images rather than blending them. This demonstrates that the brain actively interprets sensory information rather than passively receiving it, supporting the predictive processing model.
Q: What was discovered about perception when each eye sees half of a combined image?
A: Researchers found that when each eye sees half of an image, the brain perceives whole images alternately rather than experiencing rivalry between the halves. This indicates the brain overrides actual input to make sense of the world.
Q: How does the study involving text markers, roses, and the smell of roses support the Bayesian explanation of perception?
A: The study showed that adding the smell of roses increased the likelihood of participants perceiving the rose image during binocular rivalry. This demonstrates that increasing the prior probability of a hypothesis can influence perception, aligning with Bayesian principles.
Q: What is the role of prior probability in perceptual inference?
A: Prior probability reflects how likely a hypothesis is based on past experiences before considering new evidence. The brain combines prior probability with sensory data to choose the most plausible hypothesis, updating its beliefs accordingly.
Q: What is the likelihood principle in perceptual inference?
A: The likelihood principle involves evaluating how well a hypothesis explains the sensory data. The brain generates possible causes for sensory input and assesses their likelihood to determine the best explanation.
Q: Why can’t the brain rely solely on likelihood in perceptual inference?
A: Relying only on likelihood is insufficient because many plausible hypotheses might fit the data. Prior probability must also be considered to refine hypotheses and update beliefs based on a combination of likelihood and prior experiences.
Q: How does the brain use Bayes’ rule to update beliefs about a hypothesis?
A: Bayes’ rule combines the likelihood of a hypothesis with its prior probability to calculate the posterior probability. This updated belief reflects the probability of the hypothesis being true given the current evidence and prior knowledge.
Q: What is active inference in the context of predictive processing?
A: Active inference involves the brain actively changing sensory input to match its model, reducing prediction errors through actions. This process helps the brain verify and refine its predictions, enhancing perception and understanding.
Q: Why is active inference crucial for perception?
A: Active inference is crucial because it allows the brain to test hypotheses and confirm causal relationships through action. This active testing boosts confidence in perceptions and helps the brain navigate and understand the world more effectively.
Q: How does the example of recognizing objects illustrate active inference?
A: If we see something that looks like a bike but are unsure, we might move closer to confirm its identity. This active exploration helps the brain verify its predictions and reduces uncertainty, illustrating the role of active inference in perception.
Q: What is the significance of comparing posterior probabilities in active inference?
A: Comparing posterior probabilities allows the brain to rank hypotheses by their plausibility. Active inference helps the brain focus on hypotheses with high posterior probabilities, minimizing prediction errors more effectively.
Q: How does closing one’s eyes to predict darkness challenge active inference?
A: Closing one’s eyes to minimize prediction error is short-sighted and potentially dangerous. For instance, closing your eyes when a tiger is approaching may prevent immediate errors but won’t stop you from being harmed, highlighting the need for realistic and beneficial predictions.
