Abstract
AbstractObservers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision—in some studies, approaching statistically optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show that such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, “correcting-out” the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.
Publisher
Springer Science and Business Media LLC
Subject
General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology
Reference43 articles.
1. Alais, D., & Burr, D. (2004). Ventriloquist effect results from near-optimal bimodal integration. Current Biology, 14(3), 257–262.
2. Ashourian, P., & Loewenstein, Y. (2011). Bayesian inference underlies the contraction bias in delayed comparison tasks. PLOS ONE, 6(5), e19551.
3. Aston, S., Pattie, C., Beierholm, U., & Nardini, M. (2020). Failure to account for extrinsic visual noise leads to suboptimal multisensory integration. Journal of Vision, 20(11), 880.
4. Bejjanki, V.R., Knill, D.C., & Aslin, R.N. (2016). Learning and inference using complex generative models in a spatial localization task. Journal of Vision, 16(2016), 1–13.
5. Berniker, M., Voss, M., & Kording, K. (2010). Learning priors for Bayesian computations in the nervous system. PLoS ONE, 5(9), 1–9.
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献