Supporting dynamic change detection: using the right tool for the task

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition

Induced and Evoked Human Electrophysiological Correlates of Visual Working Memory Set-Size Effects at Encoding

The ability to encode, store, and retrieve visually presented objects is referred to as visual working memory (VWM). Although crucial for many cognitive processes, previous research reveals that VWM strictly capacity limited. This capacity limitation is behaviorally observable in the set size effect: the ability to successfully report items in VWM asymptotes at a small number of items. Research into the neural correlates of set size effects and VWM capacity limits in general largely focus on the maintenance period of VWM. However, we previously reported that neural resources allocated to individual items during VWM encoding correspond to successful VWM performance. Here we expand on those findings by investigating neural correlates of set size during VWM encoding. We hypothesized that neural signatures of encoding-related VWM capacity limitations should be differentiable as a function of set size. We tested our hypothesis using High Density Electroencephalography (HD-EEG) to analyze frequency components evoked by flickering target items in VWM displays of set size 2 or 4. We found that set size modulated the amplitude of the 1st and 2nd harmonic frequencies evoked during successful VWM encoding across frontal and occipital-parietal electrodes. Frontal sites exhibited the most robust effects for the 2nd harmonic (set size 2 > set size 4). Additionally, we found a set-size effect on the induced power of delta-band (1-4 Hz) activity (set size 2 > set size 4). These results are consistent with a capacity limited VWM resource at encoding that is distributed across to-be-remembered items in a VWM display. This resource may work in conjunction with a task-specific selection process that determines which items are to be encoded and which are to be ignored. These neural set size effects support the view that VWM capacity limitations begin with encoding related processes