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This lack of efficiency improvement was evident with both algorithmic and cognitive fusion.
Across the two experiments, we found strong evidence of limited capacity for both algorithmic and cognitive fusion.
Even with many experimental changes, we consistently found limited workload capacity with both algorithmic and cognitive fusion.
We found strong evidence for limited capacity processing of both algorithmic and cognitive fusion of multi-sensor imagery.
We hypothesized that individuals' efficiency for both algorithmic and cognitive fusion was at least as high as respective UCIP predictions (i.e., unlimited capacity) for the facing discrimination stimuli.
Performance on both single- and multi-sensor imagery improved with training; however, the capacity coefficient consistently indicated inefficient performance with both algorithmic and cognitive fusion, lower capacity results for algorithmic fusion than cognitive fusion, and no efficiency improvements with training.
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Separate analyses of SFT were conducted for algorithmic and cognitive fusion across both pointing and facing stimuli for those who satisfy selective influence.
Here we discuss what particular processing mechanisms suggest on a more conceptual level about visual cognition for each presentation type: algorithmic and cognitive fusion.
The sessions alternated between algorithmic and cognitive fusion (e.g., Day 2: cognitive fusion, Day 3: algorithmic fusion, Day 4: cognitive fusion, Day 5: algorithmic fusion).
Follow-up studies should consider performance comparisons across multi-sensor information presented with algorithmic and cognitive fusion when the individual sensors each supply unique useful information to the observer.
The stimulus presentation duration was extended to 2 seconds across all conditions (algorithmic and cognitive, single- and multi-sensor) to allow the operator to sample all the information from each image and allow strategies of processing the information potentially to improve with time.
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