Data Café

A discussion of Dijkstra, N., Kok, P., & Fleming, S. M. (2022). Imagery adds stimulus-specific sensory evidence to perceptual detection. Journal of Vision, 22(2), 11. https://doi.org/10.1167/jov.22.2.11

Andrew Ellis

Summary

Question

In order to understand how the brain is able to keep imagined and perceived signals separate, it is necessary to gain insight into how internally and externally generated sensory signals interact to determine visual experience.

Design and Hypotheses

Figure 1

Methods

Psychometric function

  • \(\mu\) is the mean of the normal distribution, reflecting the horizontal offset of the psychometric curve/how much signal is needed to achieve 50% presence responses

  • \(\sigma\) is the standard deviation, reflecting the slope or sensitivity of presence responses to increases in signal

  • g is the guess rate, reflecting the vertical offset at the zero point, or how likely presence responses are in the complete absence of signal.

Results

Figure 2

  • Congruent imagery was associated with a leftward shift of the psychometric function compared to both no imagery and incongruent imagery.
  • In contrast, imagery did not have an effect on either the slope or the guess rate of the psychometric function.
  • These results suggest that imagery adds sensory evidence to perceptual signals, thereby increasing the visibility of perceived stimuli.

Discussion

My opinion

Things I like

  • psychomtric curve fitting
  • attempt to distinguish between response and perceptual bias

Things I would like to do differently:

  • make data available
  • multilevel GLM
  • fit mixture model

Topics

  • Reality monitoring: imagination vs reality
  • Overlapping sensory processes
  • psychometric functions
  • guessing

References

Dijkstra, Nadine, Peter Kok, and Stephen M. Fleming. 2022. “Imagery Adds Stimulus-Specific Sensory Evidence to Perceptual Detection.” Journal of Vision 22 (2): 11. https://doi.org/10.1167/jov.22.2.11.
García-Pérez, Miguel A., and Rocío Alcalá-Quintana. 2013. “Shifts of the Psychometric Function: Distinguishing Bias from Perceptual Effects.” Quarterly Journal of Experimental Psychology 66 (2): 319–37. https://doi.org/10.1080/17470218.2012.708761.
Kuss, Malte, Frank Jäkel, and Felix A. Wichmann. 2005. “Bayesian Inference for Psychometric Functions.” Journal of Vision 5 (5): 8–8. https://doi.org/10.1167/5.5.8.
Morgan, Michael, Barbara Dillenburger, Sabine Raphael, and Joshua A. Solomon. 2012. “Observers Can Voluntarily Shift Their Psychometric Functions Without Losing Sensitivity.” Attention, Perception, & Psychophysics 74 (1): 185–93. https://doi.org/10.3758/s13414-011-0222-7.
Wichmann, Felix A., and N. Jeremy Hill. 2001. “The Psychometric Function: I. Fitting, Sampling, and Goodness of Fit.” Perception & Psychophysics 63 (8): 1293–313. https://doi.org/10.3758/BF03194544.