Probability Matching as a Computational Strategy Used in Perception

By David R. Wozny, Ulrik R Beierholm, Ladan Shams. 2010

244 citations recorded by the source corpus.

The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location—“model selection.” A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.

Subfields: Cognitive Neuroscience, Economics and Econometrics, Experimental and Cognitive Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Economics and Econometrics

In Experimental and Cognitive Psychology

Citations in the corpus

Assessing the Role of the ‘Unity Assumption’ on Multisensory Integration: A Review (2017, methodology): “The Bayesian causal inference model has been used to study human behavioral responses (Wozny et al., 2010; Rohe and Noppeney, 2015b) and the underlying neural networks (Rohe and Noppeney, 2015a, 2016) when perfoming a spatial ventriloquism task.”
Sensory reliability shapes perceptual inference via two mechanisms. (2015, methodology): “In this study, we consider three decision functions for the implicit CI involved in the spatial localization task (for details see Wozny et al. (2010): According to the ‘‘model averaging strategy, the brain combines the two auditory location estimates weighted in proportion to the posterior…”
Biases in Visual, Auditory, and Audiovisual Perception of Space (2015, methodology): “In the winning hybrid model (8 parameters), the optimized value of the mean for the general meta-modal prior was almost zero, reaffirming the central bias that had been assumed in the original 4-parameter model [30,31].”
Bayesian just-so stories in psychology and neuroscience. (2012, background): “For instance, Wozny, Beierholm, and Shams (2010) took what we referred to above as a Type 2 approach to answer the why question.”
Recalibration of auditory space following milliseconds of crossmodal discrepancy (2011, methodology): “The raw data from these spatial localization judgments have been used in another study (Wozny et al., 2010).”
The Brain’s Tendency to Bind Audiovisual Signals Is Stable but Not General (2016, unknown): “On a given day, each subject participated in two tasks: a temporal-numerosity judgment task (Shams et al., 2000, 2005) and a spatial localization task (Körding et al., 2007; Wozny et al., 2010).”
If MaxEnt RL is the Answer, What is the Question? (2019, background): “While prior work has offered a number of explanations of probability matching [10, 42, 55, 59], its root cause remains an open problem.”
Multisensory Causal Inference in the Brain (2015, background): “averaging strategy, which seems to be the case in many observers [19].”