The reverse correlation technique is a data driven study method used primarily in psychological and neurophysiological research.
[1][2][3] The term has since been adopted in psychological experiments that usually do not analyze the temporal dimension, but also present noise to human participants.
In contrast to the original meaning, the term is here thought to reflect that the standard psychological practice of presenting stimuli of defined categories to the participants is "reversed": Instead, the participant's mental representations of categories are estimated from interactions of the presented noise and the behavioral responses.
[10] This technique utilizes spike-triggered average to explain what areas of signal and noise in an image are valuable for the given research question.
To determine the areas of importance using reverse correlation, noise is applied to a base image and then evaluated by observers.
A base image that has noise superimposed on top is the stimuli that is presented to and evaluated by participants.
After a participant has responded to hundreds to thousands of trials, a researcher is ready to create a classification image.
As a whole, the reverse correlation method is a process that results in a composite image (from an individual or group) that can be used to estimate and interpret mental representations.
The reverse correlation method is typically executed as an in-lab computer experiment.
[4] After the stimuli have been prepared, a researcher should (2) collect data from participants who will see and respond to approximately 300 -1,000 trials.
[4] When designing the stimuli for a reverse correlation study, the two primary factors that one should consider are (1) the base image and (2) the noise that will be used.
When there is no base image, the number of trials that are required increases dramatically, thus making it harder to collect data.
[15] In order to identify these areas of value, researchers start by minimizing the space a participant can pull information from.
[15] Then, if/when participants are able to correctly identify an image with a trait repeatedly, we can draw conclusions about what areas have diagnostic value.
[16] Beyond face and auditory perception, research utilizing the reverse correlation method has expanded to investigate how individuals see three-dimensional objects in images with noise (but no signal).
[19] These researchers have found the optimal experimental parameters for different study designs that will result in high SNR.
[20] Additionally, researchers have investigated how the decision-making process impacts and is reflected in the reverse correlation method and have found there is a significant relationship between them.
Therefore, when interpreting results using the reverse correlation method, researchers must use caution to not ignore how the decision-making process may influence the data.
When attempting to interpret signal, researchers suggest that the best practice is to use a recently developed metric referred to as “infoVal”.