In Klein and MacInnes[1], observers searched a complex scene for a camouflaged target. Reflecting Inhibition of Return (IOR) observers were slower to detect and saccade to uncamouflaged probes that interrupted active search when these were placed in the vicinity of a recent fixation. To explore the generality of this finding of IOR during search, we changed the mental state of the observer at the time of the probes by instructing observers to inspect the scene until they found something interesting and stop there. After this voluntary cessation of search, we presented an uncamouflaged probe that observers were required to foveate. Extending our previous demonstration, we observed a relative increase in the time required to locate these probes when they were in the general region of a previous fixation so long as the scene was maintained. When the scene was removed, probe reaction time was unaffected by distance from the last fixation. The pattern of results supports the proposal that IOR biases overt orienting during search.

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model—so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.

Humans use intuition and experience to classify everything they perceive, but only if the distinguishing patterns are visible. Machine-learning algorithms can learn class information from data sets, but the created classes' meaning isn't always clear. A proposed mixed-initiative approach combines intuitive visualizations with machine learning to tap into the strengths of human and machine classification. The use of visualizations in an expert-guided clustering technique allows the display of complex data sets in a way that allows human input into machine clustering. Test participants successfully employed this technique to classify analytic activities using behavioral observations of a creative-analysis task. The results demonstrate how visualization of the machine-learned classification can help users create more robust and intuitive categories.