After the learning (or familiarization) phase, a test phase is administered. Unbeknown to them, some of the stimuli occur in a certain temporal regularity, and those regularities repeat within blocks of trials. A visual SL task commonly consists of two parts First, participants view a stream of complex stimuli, usually composed of arbitrary shapes. One such form of implicit visual learning is visual Statistical Learning (SL) 2, 10, 11, 12. That is, while explicit visual memory relies on limited capacity and available resources and is constrained by divided attention 4, 5, 6, 7, 8, 9, implicit learning of those visual regularities should, by definition, only be minimally affected, if at all, by other concurrent cognitive demands. Indeed, such extraction of regularities is widely accepted as a fundamental cognitive process 1, 2 that occurs implicitly, without direct intention or awareness, effortlessly, and without perturbing other concurrent processes 3. Extracting and utilizing these regularities in the environment is an excellent tool to reduce the amount of information needed to be processed and allow a more efficient allocation of resources. Fortunately, objects in the environment often appear not in a random fashion but rather in certain repeated contexts. Given the vast amount of visual input, one of the challenges is to cope with the stimuli load and prepare for the expected stimuli.
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Our visual world is full of dynamic and ever-changing incoming stimuli.
We further discuss the practical and methodological implications of these findings. Taken together, we conclude that spatial SL requires resources, a finding that challenges the view that the extraction of spatial regularities is automatic and implicit and suggests that this fundamental learning process is not as effortless as was typically assumed. The results showed, once again, that any concurrent task during the familiarization phase largely impaired spatial SL. Finally, we compared a no-load condition with a very low-load, infrequent dot-probe condition that posed minimal demands while still requiring attention to the display (Experiment 4).
We found robust learning in the no-load condition that was dramatically reduced in the low-load condition. Experiment 3 compared SL under spatial low-load and no-load. We found that any type of high-load demands during the familiarization abolished learning. To examine this issue, we tested spatial SL using the standard lab experiment under concurrent demands: high- and low-cognitive load (Experiment 1) and, spatial memory load (Experiment 2) during the familiarization phase. However, whether spatial SL requires resources, or it can operate in parallel to other demands, is still not clear. Statistical learning (SL), the extraction of regularities embedded in the environment, is often viewed as a fundamental and effortless process.
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