A Computational Model to Study the Dynamics of Updating of Remembered Visual Targets During Rapid and Slow Eye Movements
Bibliographic record
Abstract
After an intervening eye movement, or saccade, humans and animals are able to localize previously perceived visual targets (spatial updating). Although efforts have been made to discover the mechanism underlying spatial updating, there are still many unanswered questions about the neuronal mechanism of this phenomenon. State space model is an effective method for modeling dynamical systems and it can represent the internal behaviour of these systems. Therefore, we developed a state space model for updating target-related spatial information in gaze-centered coordinates. We considered three types of input in our proposed model: 1) an efference copy signal, inspired by motor burst in SC, 2) an eye position signal, found in LIP, VIP, MT and MST areas and 3) visual topographic maps of visual stimuli, located in SC. To model the internal neuronal behaviour of the system, we developed a radial basis function neural network (RBFNN) which can be trained with an Extended Kalman filter method. This RBFNN represents the state space and we can obtain a topographic map of the remembered target in its hidden layer. From our proposed model, the output obtained is the decoded location of the remembered target. To explore the internal mechanism underlying the updating process, we trained this model on a double-step saccade-saccade or pursuit-saccade task. After training, the receptive fields of state-space units replicated both predictive remapping during saccades (Duhamel et al. Science 1992) and continuous eye-centered updating during smooth pursuit (Dash et al. Current Biology, in press). In addition, during trans-saccadic remapping, receptive fields also expanded (to our knowledge, this predicted expansion has not yet been reported in the published literature). In the future, we plan to incorporate this framework within a more comprehensive model of trans-saccadic integration of both spatial and feature information, and use this framework to construct a physiologically plausible model. Meeting abstract presented at VSS 2015
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".