Making the brain-activity-to-information leap using a novel framework: Stimulus Information Representation (SIR)
Schyns, P. G.; Ince, R. A. A.
Show abstract
A fundamental challenge in neuroscience is to understand how the brain processes information. Neuroscientists have approached this question partly by measuring brain activity in space, time and at different levels of granularity. However, our aim is not to discover brain activity per se, but to understand the processing of information that this activity reflects. To make this brain-activity-to-information leap, we believe that we should reconsider brain imaging from the methodological foundations of psychology. With this goal in mind, we have developed a new data-driven framework, called Stimulus Information Representation (SIR), that enables us to better understand how the brain processes information from measures of brain activity and behavioral responses. In this article, we explain this approach, its strengths and limitations, and how it can be applied to understand how the brain processes information to perform behavior in a task.\n\n\"It is no good poking around in the brain without some idea of what one is looking for. That would be like trying to find a needle in a haystack without having any idea what needles look like. The theorist is the [person] who might reasonably be asked for [their] opinion about the appearance of needles.\" HC Longuet-Higgins, 1969.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Motion direction is represented as a bimodal probability distribution in the human visual cortex 96%
- Confirmation Bias through Selective Readout of Information Encoded in Human Parietal Cortex 96%
- Object representations in the human brain reflect the co-occurrence statistics of vision and language 96%
Similar papers in this journal
- Different computations over the same inputs produce selective behavior in algorithmic brain networks 96%
- THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior 96%
- Factorized visual representations in the primate visual system and deep neural networks 96%
Similar papers in this journal
Similar papers in this journal
- The connectome spectrum as a canonical basis for a sparse representation of fast brain activity 95%
- Beyond dimension reduction: Stable electric fields emerge from and allow representational drift 95%
- Temporal evolution of Neural Codes: The Added Value of a Geometric Approach to Linear Coefficients 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.