Description
Data underpinning the pre-print:
- All data organised in line with each figure including aggregated code.
- Figure 1: behavioural count, frequency, correlation, and statistical output data (.csv)
- Figure 2: all N-gram and VO-M outputs from Python
- Figure 3: all stationarity metrics and descriptive outcomes
- Figure 4: kernel outputs from each logistical regression model and windowed memory gain in the since-last-state-memory
- Python Code: contains jupyter notebooks for all analysis and simulation pipelines with comments to guide user throughout
- "Temporal Sequence Data.csv" is the core sequence data from fictive preparations organised by classification, temporal order, peak times, amplitudes - all usable directly within the code.
- All data organised in line with each figure including aggregated code.
- Figure 1: behavioural count, frequency, correlation, and statistical output data (.csv)
- Figure 2: all N-gram and VO-M outputs from Python
- Figure 3: all stationarity metrics and descriptive outcomes
- Figure 4: kernel outputs from each logistical regression model and windowed memory gain in the since-last-state-memory
- Python Code: contains jupyter notebooks for all analysis and simulation pipelines with comments to guide user throughout
- "Temporal Sequence Data.csv" is the core sequence data from fictive preparations organised by classification, temporal order, peak times, amplitudes - all usable directly within the code.
| Date made available | 1 Sept 2026 |
|---|---|
| Publisher | University of St Andrews |