The paper argues that spiking level network, with neuronal details as illustrated in the figure below, can allow ‘vertical’ translation between physiological properties of neural systems and emergent ‘whole system’ performance – enabling psychological results to be simulated from implemented networks, and also inferences to be made from simulations concerning processing at a neural level. These models also emphasise particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and which can be tested empirically. The paper illustrates the argument from neural-level models that select stimuli by biased competition. It shows that a model with biased competition dynamics can simulate data ranging from physiological studies of single cell activity to ‘whole system’ behaviour in human visual search, while also capturing effects at ‘intermediate level’, including performance break down after neural lesion and data from brain imaging. It also shows that, at each level of analysis novel predictions can be derived from the biologically plausible parameters adopted, which the team proceeds to test. The paper argues that, at least for studying the dynamics of visual attention, the approach productively links single cell to psychological data.