Serial attentional resource allocation during parallel feature value tracking.

The visual system has evolved the ability to track features like color and orientation in parallel. This property aligns with the specialization of processing these feature dimensions in the visual cortex. But what if we ask to track changing feature-values within the same feature dimension? Parallel tracking would then have to share the same cortical representation, which would set strong limitations on tracking performance. We address this question by measuring the precision of color representations when human observers track the color of two superimposed dot clouds that simultaneously change color along independent trajectories in color-space. We find that tracking precision is highly imbalanced between streams and that tracking precision changes over time by alternating between streams at a rate of ~1 Hz. These observations suggest that, while parallel color tracking is possible, it is highly limited, essentially allowing for only one color-stream to be tracked with precision at a given time.

High Working Memory Capacity at the Cost of Precision?

Working memory capacity (WMC) varies tremendously among individuals. Here, we investigate the possibility that subjects with high WMC use this limited resource more efficiently by reducing the precision with which they store information in demanding tasks. Task difficulty was increased by (a) presenting more objects to be memorized, (b) informing subjects only after the encoding phase about the relevant objects, and (c) delivering distracting features at retrieval. Precision was assessed by means of a continuous delayed-estimation task, in which object features had to be estimated from memory. High WMC subjects did not show a stronger drop in precision in difficult tasks. Instead, a positive correlation between precision and general WMC emerged. These findings suggest that high WMC subjects do not necessarily trade in quantity for quality when forming working memory (WM) representations under increasing demand. Instead, they seem to be able to devote more cognitive resources to support WM storage.