The Flash-Lag Effect as a Motion-Based Predictive Shift
Fig 3
The diagonal motion-based prediction (dMBP) model accounts for the Flash-lag effect.
(A) We plot the histogram of estimated positions from the dMBP model with a neural delay τ = 100 ms for the moving and the flashed stimuli. These estimated positions are averaged across the five frames centered around the time at which the response to the flash reaches its maximal precision and across 20 trials. Comparing the distribution of estimated positions for the moving (green) and flashed (in red) stimuli shows that, at this particular instant, the (left) moving dot is perceived ahead of the estimated position of the flash. (B) We quantified this spatial lead by plotting the histograms of the inferred horizontal positions during these frames, both for the position-based predictive (PBP) and dMBP models. The red and green dashed vertical lines represent the average positions of the flashed and moving stimuli, respectively. One can observe a significant spatial lead in the dMBP model, but not in the PBP model. The motion component of the dMBP model is thus essential to explain the flash-lag effect. (C) We varied the speed of the dot motion to titrate its role in the amplitude of the spatial lead. The black dashed line illustrates the predicted linear relationship from an extrapolation model with a perfect knowledge about target speed (slope one). One can observe a nearly linear relationship at slow speeds, followed by a saturation for higher speeds. At the fastest extrema of the speed range, ones observes a decrease in the spatial lead of the moving spot, together with an higher variability across trials (error bars: ±1 SD), consistent with the experimental data from [36]. The nonlinear relationship in our model emerges from the decrease of precision in the representation of motion at higher speeds. It highlights the putative role of the dynamic, explicit representation of precision in explaining the flash-lag effect.