Parahippocampal cortex is involved in material processing through echolocation in blind echolocation experts
Bibliographic record
Abstract
People, in addition to animals such as bats and dolphins, can utilize echolocation to navigate through their environments. In fact, there are blind people who have learned to navigate by emitting mouth clicks and listening to the returning echoes. Previous echolocation research has shown that blind people can use echoes from their own vocalizations to discriminate between different materials such as velvet or glass (Kellogg, W.N., 1962, Science 137: 399-404). Importantly, apart from providing a sound-reflecting surface, the materials were always silent. Here we present data from an fMRI experiment that investigated the neural activity underlying the processing of materials through echolocation. Three blind echolocation experts (all males) took part in the experiment. First, we made binaural sound recordings in the ears of each participant while he made clicks in the presence of one of three different materials (fleece, foliage or whiteboard), or while he made clicks in an empty room. During fMRI scanning these recordings were played back to participants. Remarkably, based on the recordings alone, participants were able to identify each of the three materials reliably, as well as the empty room. Furthermore, a whole brain analysis, in which we contrasted the brain activity that occurred when participants listened to material recordings versus when they listened to empty-room recordings, revealed a material-related increase in BOLD activation in a region of parahippocampal cortex. This region of parahippocampal cortex has previously been found to be involved in the processing of the material properties of objects signalled by visual or auditory cues (Arnott et al., 2008, NeuroImage 43: 368-378). Thus, our results are consistent with the idea that material processing by means of echolocation relies on a multi-modal material processing area in parahippocampal cortex. Meeting abstract presented at VSS 2012
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".