A Distributed Cortical Representation Underlies Crossmodal Object Recognition in Rats
Bibliographic record
Abstract
The mechanisms by which the brain integrates the unimodal sensory features of an object into a comprehensive multimodal object representation are poorly understood. We have recently developed a procedure for assessing crossmodal object recognition (CMOR) and object feature binding in rats using a modification of the spontaneous object recognition (SOR) paradigm. Here we show for the first time that rats are capable of spontaneous crossmodal object recognition when they are asked to recognize a visually presented object having previously only explored the tactile features of that object. Moreover, rats with bilateral perirhinal cortex (PRh) lesions were impaired on the CMOR task and a visual-only, but not a tactile-only, version of SOR. Conversely, rats with bilateral posterior parietal cortex (PPC) lesions were impaired on the CMOR and tactile-only tasks but not the visual-only task. Finally, crossmodal object recognition ability was severely and selectively impaired in rats with unilateral lesions made to PRh and PPC in opposite hemispheres. Thus, spontaneous tactile-to-visual crossmodal object recognition in rats relies on an object representation that requires functional interaction between PRh and PPC, which appear to mediate the visual and tactile information-processing demands of the task, respectively. These results imply that, at least under certain conditions, the separate sensory features of an object are represented in a distributed manner in the cortex. The novel paradigm introduced here should be a valuable tool for further study of the neurobiological bases of crossmodal cognition and object feature binding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".