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Richard Murray, Lisa Pritchett; A classification-image-like method reveals strategies in 2afc tasks. Journal of Vision 2014;14(10):389. doi: https://doi.org/10.1167/14.10.389.
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© ARVO (1962-2015); The Authors (2016-present)
Despite decades of research, there is still uncertainty about how observers make even the simplest visual judgements, such as 2AFC decisions. Here we demonstrate a new method of using classification images to calculate "proxy decision variables" that estimate an observer's decision variables on individual trials. This provides a new way of investigating decision strategies. In Experiment 1, nine observers viewed two disks in Gaussian noise, to the left and right of fixation, and judged which had a contrast increment. The contrast increment was set to each observer's 70% threshold. On each trial we calculated the cross-correlation of the observer's classification image with the two disks, providing proxy decision variables. Using 10,000 such trials per observer we mapped the observer's decision space: we plotted the probability of the observer choosing the right-hand disk as a function of the values of the two decision variables. We tested the hypotheses that observers base their 2AFC decisions on (a) the difference between the two decision variables, (b) independent yes-no decisions on the two decision variables, or (c) just one of the decision variables. We found that all observers' decision spaces had a triangular guessing region, which is not predicted by any of the above models. However, this finding is consistent with model (a) plus intrinsic uncertainty. We conclude that the classic difference model favoured by detection theory is a valid model of 2AFC decisions. In Experiment 2, four observers discriminated between black and white Gaussian disks at fixation, and the two stimulus intervals were separated in time (1000 ms) rather than space. Again observers' decision spaces supported the difference model. We discuss how proxy decision variables can be used to test a wide range of additional signal detection models in domains such as cue combination and visual search.
Meeting abstract presented at VSS 2014
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