Visual Perceptual Learning of Form-Motion Integration: Exploring the Involved Mechanisms with Transfer Effects and the Equivalent Noise Approach

Abstract

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants' performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing.

Keywords: equivalent noise analysis; glass patterns; internal noise; modified random-dot kinematograms; non-directional motion; sampling efficiency; visual perceptual learning.

Figure A1

Illustration of the procedure used to extract the threshold value from a divergent staircase. (a) The solid line represents the behavior of the divergent staircase, while the dotted line shows a standard staircase for reference. (b) Trials are sorted into bins with respect to the scale parameter, with each bin counting the percentage of correct answers in that group of trials. The distribution is then fitted against a sigmoid curve (solid line), and the parameter value corresponding to 70.7% is extrapolated (dashed lines).

Figure 1

(a,b) depict the 2IFC task procedure. (a) illustrates the procedure using dynamic GPs, where the first interval contains a vertically oriented GP, and the second interval contains a random/noisy GP. (b) depicts the procedure using mRDKs, with bidirectional movement along the vertical axis. In both figures, the arrows in the first interval indicate the bidirectional illusory motion along the vertical axis. The interval order shown in the figure is just an example; in the actual experiment, the coherent stimulus could randomly appear in either the first or the second temporal interval. Additionally, for illustrative purposes, the stimuli are shown at the maximum level (100%) of their coherence in this figure.

Figure 2

(a,b) represent the equivalent noise task where participants were required to discriminate the perceived orientation or illusory direction of motion (either clockwise or counterclockwise from vertical) using a two-alternative forced-choice task (2AFC). (a) depicts the procedure with dynamic GPs, while (b) illustrates the mRDKs. The arrows represent the bidirectional illusory motion along the oblique axis and were not displayed during the experiment.

Figure 3

Boxplots depicting coherence thresholds for pre- and post-test sessions, as well as for GPs and mRDKs. Each box in the plot represents the interquartile range (IQR) of the data, with the median indicated by the horizontal line inside the box. The whiskers extend to the minimum and maximum values within 1.5 times the IQR from the first and third quartiles, respectively. Additionally, the black point inside each box denotes the mean of that condition. The colored dots represent individual data points, with blue indicating dynamic GPs and red indicating mRDK.

Figure 4

Boxplots of sampling efficiency (η) for pre- and post-test conditions. For each boxplot, the horizontal black line indicates the median, whereas the dot within each box represents the mean sampling efficiency for each condition. The colored dots represent individual data points, with blue indicating dynamic GPs and red indicating mRDK.

Figure 5

Boxplots of internal noise (${\mathsf{\sigma }}_{int}$) for pre- and post-test conditions. For each boxplot, the horizontal black line indicates the median, whereas the dot within each box represents the mean internal noise (in radians) for each condition. The colored dots represent individual data points, with blue indicating dynamic GPs and red indicating mRDK.

Figure 6

Coherence threshold percentages with GP stimuli observed across ten experimental sessions, including the pre-test, post-test, and eight training sessions. The blue points represent the mean coherence threshold for each session, with error bars indicating ±1SEM. The dashed red line represents the power function fit to the data.