Asymmetric Serial Dependencies between Form and Motion Feature Perception Revealed by Changing Stimulus Internal and External Noise

You, Fan-Huan; Sun, Qi; Gong, Xiu-Mei

doi:10.2139/ssrn.4347505

Cited by 2 publications

(13 citation statements)

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“…As mentioned, within the framework of the ideal observer, the bias towards prior stimuli depends exclusively on the relative uncertainty between the current and previous stimuli 10 . However, this model falls short in explaining the results of Experiments 1a and 1b (Figure 2), as well as similar patterns that are qualitatively evident in other studies [22][23][24]30 .…”

Section: State-dependent Serial Dependencesupporting

confidence: 78%

“…These findings suggest that ideal observer models offer only limited and incomplete explanations of serial dependence. In line with this, prior work has used the term 'partial Bayesian inference' when referring to the computations behind serial dependence 24 , to explain cases in which, for instance, the effects depended only on the uncertainty in the present, but not in the past [22][23][24] . It must be noted that even in such cases, existing data contains trends compatible with our results, with peaks of serial dependence when both the past and current stimuli are uncertain, despite the use of diverse stimuli and paradigms 22,23,30,34 .…”

Section: Discussionmentioning

confidence: 99%

“…Recent work on serial dependence, a phenomenon where perceptual decisions are biased towards previous stimuli 20,21 , has revealed clear deviations from ideal observer strategies. In particular, when integrating current and prior stimuli, humans do not take the relative uncertainty between stimuli into account in the same way as ideal observers [22][23][24] . This suggests that classic models, which typically assume fixed computations based solely on stimulus uncertainty, may not fully account for other factors.…”

Section: Introductionmentioning

confidence: 99%

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It’s not the spoon that bends: Internal states of the observer determine serial dependence

Ozkirli,

Pascucci

2023

Preprint

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According to traditional views, perception deals with uncertainty using optimal strategies. For instance, when prior stimuli are more reliable than current ones, perception tends to give greater weight to the past, resulting in stronger serial dependence in perceptual decisions. Here, we challenge this view and reveal a systematic deviation from this strategy. We asked participants to reproduce the average orientation of an ensemble of stimuli, with the level of uncertainty manipulated by varying the ensemble variability. Surprisingly, we found that observers exhibited stronger serial dependence when prior stimuli were uncertain rather than reliable. To explain this pattern, we present an extension of the ideal observer model. In this model, uncertain events trigger internal states of the observer, such as expectations about moments of degraded sensory input, thereby modulating the tendency to integrate prior and current stimuli. The striking finding is that manipulation of these internal states through external feedback can lead to clear effects on serial dependence, even when the level of uncertainty in the stimulus remains constant. We propose that serial dependence reflects a 'state-dependent' mechanism that the brain amplifies to cope with moments of expected uncertainty.

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Section: State-dependent Serial Dependencesupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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It’s not the spoon that bends: Internal states of the observer determine serial dependence

Ozkirli,

Pascucci

2023

Preprint

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“…Another important contribution of the current study is that our Bayesian inference model well predicts the effects of the attentional load on serial dependence, suggesting that the attentional effect is consistent with a Bayesian inference process from the computational perspective. Previous studies only proposed that the serial dependence effect between form and flow information was consistent with the Bayesian inference process based on the trend of the data but did not examine it by developing a computational model 10,11 . In addition, our model proposes that the attentional load modulates the standard deviation of likelihood distributions that are positively correlated with the width of sensory neurons' tuning curves [41][42][43] .…”

Section: Discussionmentioning

confidence: 99%

“…That is, the effect of the form feature on the perception of motion feature was not equal to the effect of the flow feature on the perception of form feature 7,8 . You et al (2023) 11 alternatively presented form and flow displays and found the asymmetric serial dependences between form and flow displays, suggesting that the across-temporal integration between the two features is also asymmetric.…”

Section: Introductionmentioning

confidence: 99%

Effects of attention on the asymmetric serial dependences between form and motion patterns and their computational processes

Sun,

Wang,

Sun

et al. 2024

Preprint

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Recent studies have revealed that serial dependences are asymmetric in the estimation of the focus of expansion (FoE) in the global static form and dynamic optic flow displays. Here, we conducted two experiments to examine whether and how attention affected the serial dependences between the two displays. We found that when all attentional resources are allocated to the FoE estimation task, the serial dependence of the form FoE estimation on the previous flow FoE (SDEflow_form) still existed even as the flow FoE was 40°, while the serial dependence of the flow FoE estimation on the previous form FoE (SDEform_flow) disappeared as the form FoE was beyond 30°. When attentional resources are distributed by other tasks, the SDEflow_form tended to be stronger than the SDEform_flow. Therefore, the SDEflow_form and SDEform_flow are asymmetric regardless of observers’ attentional states. Lastly, we developed two Bayesian models based on the proposal that attention modulated the certainty of sensory representations of currently presented features. Besides, one model took the effects of working memory on previously presented features in consideration. We found that the model with working memory predicted participants’ performances better than the model without considering working memory. In summary, the current study demonstrated attention and working memory affected the serial dependences between form and flow displays and the effects could be quantitatively predicted by Bayesian inference models.

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Asymmetric Serial Dependencies between Form and Motion Feature Perception Revealed by Changing Stimulus Internal and External Noise

Cited by 2 publications

References 0 publications

It’s not the spoon that bends: Internal states of the observer determine serial dependence

It’s not the spoon that bends: Internal states of the observer determine serial dependence

Effects of attention on the asymmetric serial dependences between form and motion patterns and their computational processes

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