Figure 2. The original image from Figure 1. On revisiting Figure 1, the seemingly random shapes and lines might transform based on the new understanding gleaned from this original image.

 

In Chapter 2, I studied how humans predict incoming sensory information and how these predictions modulate neural processes. I presented participants with pairs of images. Unbeknownst to them, certain images predicted others, meaning seeing Image A would increase the likelihood of Image B appearing next. Using these statistical regularities, I discerned how the brain reacts to expected versus unexpected images. Do correct predictions about Image B alter its processing in the sensory brain? My results suggest so. The sensory brain responds less vigorously and distinctly to correctly predicted images, even without any conscious intention to predict. In other words, our brain seems to anticipate sensory input based on learned statistical regularities.

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In Chapter 3, I probed how automatic these predictions are. Earlier studies, including my Chapter 2 findings, implied that we continuously and automatically predict, even without intending to. Yet, I wanted to determine the role of attention in this process. To test this, I presented participants with image pairs again, employing statistical regularities. Participants were instructed to either focus on the images or on letters displayed directly above the images. Surprisingly, when participants' attention diverted from the image, all predictive effects vanished. The brain now reacted similarly to expected and unexpected images. But when attention refocused on the images, neural reactions to unexpected images were again more pronounced. My results suggest that while our brain seemingly predicts visual input automatically to some degree (probably without explicit intention to do so), attention to the predictable input seems nonetheless crucial.

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In Chapter 4, I further chart the diminished neural response for expected versus unexpected input. One possibility is that the brain minimizes neural responses to correctly predicted images by reducing the response of neurons reacting vigorously to the anticipated image. An expected image is less informative because it is predictable. Hence, it makes sense for the brain to conserve energy by not reacting as vigorously to anticipated sensory input, effectively "dampening" the response. Another theory suggests that while the brain still reacts strongly to correctly predicted image features, the overall response is more specific and contains less noise, resulting in a lowered average neural response. Using computational models and data from Chapters 2 and 3, I conclude that correctly anticipated sensory inputs appear to be 'dampened'. This implies the brain seems to reduce anticipated information, perhaps because such sensory input is less informative. Thus, our attention is directed towards potentially significant unexpected sensory information.

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Chapter 5 delves into the limits of learning statistical regularities. Previous chapters demonstrated that people can effortlessly and incidentally grasp statistical patterns. I then explored whether this learning process transcends various senses. Instead of showing image pairs to participants, I introduced sounds predicting images, requiring participants to use information from two different senses. Astonishingly, participants did not seem to learn these audio-visual regularities: neither behavioral nor neural evidence indicated statistical learning effects. This implies the statistical learning mechanism discussed in Chapters 2-4 might depend on neural mechanisms within a specific modality (e.g., changes in the visual cortex).

 

Chapter 6 synthesizes the findings from preceding chapters into a cohesive narrative. At first, you likely saw random lines in Figure 1. Yet, after viewing Figure 2, your perception of Figure 1 altered. This change demonstrates that our prior knowledge seemingly has a direct impact on our perceptions. My research illustrates how the brain employs prior knowledge to guide perception. It seems exceptionally proficient at detecting and learning sensory world's statistical structures, even without any intent to learn. Once acquired, this knowledge is utilized by the brain to predict sensory input, with discrepancies leading to prediction errors. For example, almost running into someone after turning a corner. In the sensory brain, such a predictive error is evident by a more robust and clearer neural reaction. My findings also suggest that the brain might use predictions to dampen reactions to predicted sensory input, perhaps because correctly predicted sensory information contains less new information than unexpected sensory input. In conclusion, prediction appears to be a fundamental facet of sensory information processing.

 

If you find this work interesting, chapter 1 of my thesis contains a more elaborate introduction and chapter 6 a discussion/summary of the results.

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