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Aims.We have implemented a novel method to create simulated [CII] emission line intensity mapping (LIM) data cubes using COSMOS 2020 galaxy catalogue data. It allows us to provide solid lower limits for previous simulation-based model predictions and the expected signal strength of upcoming surveys. Methods.We applied [CII]158 μm luminosity models to COSMOS 2020 to create LIM cubes covering a 1.2 × 1.2deg²sky area. These models were derived using galaxy bulk property data from the ALPINE-ALMA survey over the redshift range of 4.4 < z < 5.9, while additional models were taken from the literature. The LIM cubes cover 3.42 < z < 3.87, 4.14 < z < 4.76, 5.34 < z < 6.31, and 6.75 < z < 8.27, matched to planned observations from the EoR-Spec module of the Prime-Cam instrument in the Fred Young Submillimeter Telescope (FYST). We also created predictions including additional galaxies below current detection limits by ‘extrapolating’ from the faint end of the COSMOS 2020 luminosity function, comparing these to predictions from the literature. In addition, we computed the signal-to-noise (S/N) ratios for the power spectra, using parameters from the planned FYST survey with predicted instrumental noise levels. Results.We find lower limits for the expected power spectrum using the likely incomplete empirical data: when normalised by 2π², the amplitudes atk = 1 Mpc⁻¹are 3.06 × 10⁷, 1.43 × 10⁷, 9.80 × 10⁵, 2.77 × 10⁵ (Jy sr⁻¹)²for the aforementioned redshift ranges. For the extrapolated sample, the power spectra are consistent with prior predictions, indicating that extrapolation is a viable method for creating mock LIM cubes. In this case, we expect a result of S/N> 1 when using FYST parameters. However, our high-redshift results remain inconclusive because of the poor completeness of COSMOS 2020 atz > 6.3. These predictions will be improved on the basis of future JWST data. 

Attention filters sensory inputs to enhance task-relevant information. It is guided by an “attentional template” that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.