Storage- and processing limitations manifest clearly in working memory when the number of to-be-remembered items increases: Each item is encoded less precisely and more slowly. Why do humans encode items at a slower rate that are already encoded with lower precision? Here, we propose a resource-rational model of working memory encoding. Increasing precision and speed reduce behavioral cost, but also incur neural costs. Both storage- and processing limitations arise naturally from jointly minimizing behavioral and neural costs. Intuitively, marginal increases in precision or speed have a diminishing return on investment. We show that our model: (1) accounts for classical effects of set size on storage capacity and processing speed, (2) explains recent findings showing faster encoding when briefly presented items are expected, and (3) predicts novel findings suggesting that classical set size effects on processing speed are in fact mediated by the probability that an item will be probed.

Keeping track of time is essential for everyday behavior. Theoretical models have proposed a wide variety of neural processes that could tell time, but it is unclear which ones the brain actually uses. Low-level neural models are specific, but rarely explicate how cognitive processes, such as attention and memory, modulate prospective and retrospective timing. Here we develop a neurocomputational model of prospective and retrospective timing, using a spiking recurrent neural network. The model captures behavior of individual spiking neurons and population dynamics when producing and perceiving time intervals, thus bridging low- and high-level phenomena. When interrupting events are introduced, the model delays responding in a similar way to pigeons and rats. Crucially, the model also explains why attending incoming stimuli decreases prospective estimates and increases retrospective estimates of time. In sum, our model offers a neurocomputational account of prospective and retrospective timing, from low-level neural dynamics to high-level cognition.