eye-blinking has emerged as a promising means of measuring viewer engagement with visual content. this method capitalizes on the fact that although we remain largely unaware of our eyeblinking in everyday situations, eye-blinks are inhibited at precise moments in time so as to minimize the loss of visual information that occurs during a blink. Probabilistically, the more important the visual information is to the viewer, the more likely he or she will be to inhibit blinking. In the present study, viewer engagement was experimentally manipulated in order to: (1) replicate past studies suggesting that a group of viewers will blink less often when watching content that they perceive as more important or relevant; (2) test the reliability of the measure by investigating constraints on the timescale over which blink rate patterns can be used to accurately quantify viewer engagement; and (3) examine whether blink rate patterns can be used to quantify what an individual-as opposed to a group of viewers-perceives as engaging. Results demonstrate that blink rate patterns can be used to measure changes in individual and group engagement that unfold over relatively short (1 second) and long (60 second) timescales. However, for individuals with lower blink rates, blink rate patterns may provide less optimal measures when engagement shifts rapidly (at intervals of 1 second or less). Findings support the use of eye-blink measures in future studies investigating a person's subjective perception of how engaging a stimulus is.

Cognitive control allows us to follow abstract rules in order to choose appropriate responses given our desired outcomes. Cognitive control is often conceptualized as a hierarchical decision process, wherein decisions made at higher, more abstract levels of control asymmetrically influence lower-level decisions. These influences could evolve sequentially across multiple levels of a hierarchical decision, consistent with much prior evidence for central bottlenecks and seriality in decision-making processes. However, here, we show that multiple levels of hierarchical cognitive control are processed primarily in parallel. Human participants selected responses to stimuli using a complex, multiply contingent (third order) rule structure. A response deadline procedure allowed assessment of the accuracy and timing of decisions made at each level of the hierarchy. In contrast to a serial decision process, error rates across levels of the decision mostly declined simultaneously and at identical rates, with only a slight tendency to complete the highest level decision first. Simulations with a biologically plausible neural network model demonstrate how such parallel processing could emerge from a previously developed hierarchically nested frontostriatal architecture. Our results support a parallel processing model of cognitive control, in which uncertainty on multiple levels of a decision is reduced simultaneously.