Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes, and/or data that has been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. _Black box_ convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was re-trained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data.
Inhibition of return (IOR) is a phenomenon of perceptual attention characterized by delayed shifts in attention toward previously cued target locations. In reflective (internally directed) attention studies, response times (RTs) to cued items are sometimes facilitated, but other times IOR-like effects are observed wherein RTs to probed items are slower when the items had been mentally attended (refreshed) earlier in the trial. Perceptual IOR is known to be modulated by the probability that target and cued locations match. If the same is true for reflective attention, it could account for why sometimes reflective attention can lead to facilitation and other times inhibition. In the current study, four experiments examined the potential facilitative or inhibitory influence of probe predictability in reflective attention. We first replicated the design and IOR like pattern of results originally reported by Johnson et al. (2013). In subsequent experiments, when the proportion of unrefreshed probes was increased, the IOR-like effect increased in magnitude. When the proportion of refreshed probes was increased, the IOR-like effect was eliminated, but there was no evidence for facilitation. Altogether, these results are consistent with perceptual IOR literature implicating underlying inhibitory and facilitative attentional processes that can either interact synergistically or nullify each other. Further work will be needed to fully understand the paradoxical effects of why reflective attention is sometimes inhibitory and other times facilitative, but the current results demonstrate that expectation can play a significant role in the size of the effect.
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