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Noise Improves Visual Motion Discrimination via a Stochastic Resonance-Like Phenomenon

View Article: PubMed Central - PubMed

ABSTRACT

The stochastic resonance (SR) is a phenomenon in which adding a moderate amount of noise can improve the signal-to-noise ratio and performance of non-linear systems. SR occurs in all sensory modalities including the visual system in which noise can enhance contrast detection sensitivity and the perception of ambiguous figures embedded in static scenes. Here, we explored how adding background white pixel-noise to a random dot motion (RDM) stimulus produced changes in visual motion discrimination in healthy human adults. We found that, although the average reaction times (RTs) remained constant, an intermediate level of noise improved the subjects’ ability to discriminate motion direction in the RDM task. The psychophysical responses followed an inverted U-like function of the input noise, whereas the incorrect responses with short RTs did not exhibit such modulation by external noise. Moreover, by applying stimulus and noisy signals to different eyes, we found that the SR phenomenon occurred presumably in the primary visual cortex, where these two signals first converge. Our results suggest that a SR-like phenomenon mediates the improvement of visual motion perception in the RDM task.

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Background noise enhances motion detection in the RDM task.(A) Cartoon of the RDM task with different background noise average luminance levels [zero noise (ZN): 0% luminance; optimal noise (ON): 5% luminance; high noise (HN): 25% luminance]. The noise source was dynamic; it was refreshed every frame and was added as a background behind the RDM dots. (B) Group-averaged %correct choice index (upper panel; %CCI = %correct choices with noise/% correct choices without noise; paired trials, see Materials and Methods) against the luminance of background noise (coherence of 5%; stimulus luminance of 25%). No modulation in %incorrect choice index (%ICI) for incorrect choices that had RTs lower than the median RT (i.e., median split; gray squares in B). The Fano factor of the %CCI data presents a local minimum with optimal background noise (green dot; middle panel). The increase in %CCI around 5% of noise luminance (green dot in the upper panel) cannot be explained by changes in the averaged RTs of the choices made in the presence (gray circles) vs. the absence (empty circles) of background noise (middle panel). The skeweed RT distributions for correct (C) and incorrect (D) choices had similar shapes, means and medians. Number of subjects in parenthesis.
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Figure 2: Background noise enhances motion detection in the RDM task.(A) Cartoon of the RDM task with different background noise average luminance levels [zero noise (ZN): 0% luminance; optimal noise (ON): 5% luminance; high noise (HN): 25% luminance]. The noise source was dynamic; it was refreshed every frame and was added as a background behind the RDM dots. (B) Group-averaged %correct choice index (upper panel; %CCI = %correct choices with noise/% correct choices without noise; paired trials, see Materials and Methods) against the luminance of background noise (coherence of 5%; stimulus luminance of 25%). No modulation in %incorrect choice index (%ICI) for incorrect choices that had RTs lower than the median RT (i.e., median split; gray squares in B). The Fano factor of the %CCI data presents a local minimum with optimal background noise (green dot; middle panel). The increase in %CCI around 5% of noise luminance (green dot in the upper panel) cannot be explained by changes in the averaged RTs of the choices made in the presence (gray circles) vs. the absence (empty circles) of background noise (middle panel). The skeweed RT distributions for correct (C) and incorrect (D) choices had similar shapes, means and medians. Number of subjects in parenthesis.

Mentions: To explore how external visual noise interacted with visual motion perception, we developed the computational tools that allowed us to combine dynamic background pixel-noise (refreshed every frame) with the standard and widely used RDM task (Newsome and Pare, 1988; Figure 2A; see Materials and Methods). We asked how adding such noise with different luminance levels influenced the choice performance of naïve subjects solving the RDM task. We tested 13 new participants in conditions where the visual stimulus barely produced a perception of global motion (coherence = 5%; luminance = 25%; correct choices: 63.20 ± 2.48%, one way ANOVA test, P = 0.03; Figure 1B; Newsome et al., 1989). After performing the experiments, we calculated the group averaged %correct choice index (%CCI) as a function of the luminance of the additive background noise. We took the %CCI index as the amount of %correct choices obtained in the presence of noise divided by those obtained in its absence. The group averaged %CCI revealed a bell-shaped distribution with a peak sensitivity of 6.5 ± 1.8% at a moderate noise luminance of 5% (paired t-test, P < 0.001; Figure 2B; green dot, upper panel). This response pattern resembled a SR-like phenomenon, and the peak value in the %CCI function was consistent with previous SR observations (Zeng et al., 2000; Ward et al., 2002). Regarding the stimulus presentation sequences, we paired the test trials for each specific noise luminance condition with trials lacking noise (see Materials and Methods). This arrangement ensured that the task provided no temporal information about the stimulus (Pelli, 1985), and allowed us to estimate the %CCI independently of possible variations in choice performance due to attentional shifts (or any other factors; Smith and Ratcliff, 2004; Treviño, 2014). We used 100 repetitions per noise luminance.


Noise Improves Visual Motion Discrimination via a Stochastic Resonance-Like Phenomenon
Background noise enhances motion detection in the RDM task.(A) Cartoon of the RDM task with different background noise average luminance levels [zero noise (ZN): 0% luminance; optimal noise (ON): 5% luminance; high noise (HN): 25% luminance]. The noise source was dynamic; it was refreshed every frame and was added as a background behind the RDM dots. (B) Group-averaged %correct choice index (upper panel; %CCI = %correct choices with noise/% correct choices without noise; paired trials, see Materials and Methods) against the luminance of background noise (coherence of 5%; stimulus luminance of 25%). No modulation in %incorrect choice index (%ICI) for incorrect choices that had RTs lower than the median RT (i.e., median split; gray squares in B). The Fano factor of the %CCI data presents a local minimum with optimal background noise (green dot; middle panel). The increase in %CCI around 5% of noise luminance (green dot in the upper panel) cannot be explained by changes in the averaged RTs of the choices made in the presence (gray circles) vs. the absence (empty circles) of background noise (middle panel). The skeweed RT distributions for correct (C) and incorrect (D) choices had similar shapes, means and medians. Number of subjects in parenthesis.
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Figure 2: Background noise enhances motion detection in the RDM task.(A) Cartoon of the RDM task with different background noise average luminance levels [zero noise (ZN): 0% luminance; optimal noise (ON): 5% luminance; high noise (HN): 25% luminance]. The noise source was dynamic; it was refreshed every frame and was added as a background behind the RDM dots. (B) Group-averaged %correct choice index (upper panel; %CCI = %correct choices with noise/% correct choices without noise; paired trials, see Materials and Methods) against the luminance of background noise (coherence of 5%; stimulus luminance of 25%). No modulation in %incorrect choice index (%ICI) for incorrect choices that had RTs lower than the median RT (i.e., median split; gray squares in B). The Fano factor of the %CCI data presents a local minimum with optimal background noise (green dot; middle panel). The increase in %CCI around 5% of noise luminance (green dot in the upper panel) cannot be explained by changes in the averaged RTs of the choices made in the presence (gray circles) vs. the absence (empty circles) of background noise (middle panel). The skeweed RT distributions for correct (C) and incorrect (D) choices had similar shapes, means and medians. Number of subjects in parenthesis.
Mentions: To explore how external visual noise interacted with visual motion perception, we developed the computational tools that allowed us to combine dynamic background pixel-noise (refreshed every frame) with the standard and widely used RDM task (Newsome and Pare, 1988; Figure 2A; see Materials and Methods). We asked how adding such noise with different luminance levels influenced the choice performance of naïve subjects solving the RDM task. We tested 13 new participants in conditions where the visual stimulus barely produced a perception of global motion (coherence = 5%; luminance = 25%; correct choices: 63.20 ± 2.48%, one way ANOVA test, P = 0.03; Figure 1B; Newsome et al., 1989). After performing the experiments, we calculated the group averaged %correct choice index (%CCI) as a function of the luminance of the additive background noise. We took the %CCI index as the amount of %correct choices obtained in the presence of noise divided by those obtained in its absence. The group averaged %CCI revealed a bell-shaped distribution with a peak sensitivity of 6.5 ± 1.8% at a moderate noise luminance of 5% (paired t-test, P < 0.001; Figure 2B; green dot, upper panel). This response pattern resembled a SR-like phenomenon, and the peak value in the %CCI function was consistent with previous SR observations (Zeng et al., 2000; Ward et al., 2002). Regarding the stimulus presentation sequences, we paired the test trials for each specific noise luminance condition with trials lacking noise (see Materials and Methods). This arrangement ensured that the task provided no temporal information about the stimulus (Pelli, 1985), and allowed us to estimate the %CCI independently of possible variations in choice performance due to attentional shifts (or any other factors; Smith and Ratcliff, 2004; Treviño, 2014). We used 100 repetitions per noise luminance.

View Article: PubMed Central - PubMed

ABSTRACT

The stochastic resonance (SR) is a phenomenon in which adding a moderate amount of noise can improve the signal-to-noise ratio and performance of non-linear systems. SR occurs in all sensory modalities including the visual system in which noise can enhance contrast detection sensitivity and the perception of ambiguous figures embedded in static scenes. Here, we explored how adding background white pixel-noise to a random dot motion (RDM) stimulus produced changes in visual motion discrimination in healthy human adults. We found that, although the average reaction times (RTs) remained constant, an intermediate level of noise improved the subjects&rsquo; ability to discriminate motion direction in the RDM task. The psychophysical responses followed an inverted U-like function of the input noise, whereas the incorrect responses with short RTs did not exhibit such modulation by external noise. Moreover, by applying stimulus and noisy signals to different eyes, we found that the SR phenomenon occurred presumably in the primary visual cortex, where these two signals first converge. Our results suggest that a SR-like phenomenon mediates the improvement of visual motion perception in the RDM task.

No MeSH data available.


Related in: MedlinePlus