Title: Neural Correlates of Serial Dependence: Synaptic Short-term Plasticity Orchestrates Repulsion and Attraction
Venue: NeurIPS 2025
Accepted: yes
Keywords: serial dependence

short-term plasticity

continuous attractor neural networks

sensitivity and stability
Abstract: Serial dependence reflects how recent sensory history shapes current perception, producing two opposing biases: repulsion, where perception is repelled from recent stimuli, and attraction, where perception is drawn toward them. Repulsion typically occurs at the sensory perception stage, while attraction arises at the post-perception stage. To uncover the neural basis of these effects, we developed a two-layer continuous attractor neural network model incorporating synaptic short-term plasticity (STP). The lower layer, dominated by synaptic depression, models sensory processing and drives repulsion due to sustained neurotransmitter depletion. The higher layer, dominated by synaptic facilitation, models post-perception processing and drives attraction by sustained high neurotransmitter release probability. Our model successfully explains the serial dependence phenomena observed in the visual orientation judgment experiments, highlighting STP as the critical mechanism, with its time constants defining the temporal windows of repulsion and attraction. Furthermore, the model provides a neural foundation for the Bayesian interpretation of serial dependence. This study advances our understanding of how the neural system leverages STP to balance sensitivity in sensory perception with stability in post-perceptual cognition.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this study, the authors present a two-stage continuous attractor network model endowed with short term facilitation and depression, capable of explaining psychophysical results regarding the sequential dependence upon previous stimuli of perceptual judgments in task participants.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well-written and clear, with an appropriate amount of time spent constructing the two-stage model and analyzing its properties. The results are interpretable, intuitive, and thorough; the model is also reasonably well substantiated by a literature review and appears (as far as I can tell) original. I have some concerns about the authors’ attempt to relate their model to a Bayesian interpretation of serial dependence, explained below. While I do think that the model is quite well designed to explain the specific set of experimental results that the authors have selected, I have a hard time seeing how a model of this kind could be adapted to explain or provide predictions for neural responses or human reports in response to more complex, naturalistic stimuli, i.e. I view it as fundamentally a ‘toy’ model, that leaves little clue for how it could be extended to more complex situations.\n\nQUESTIONS:\nMajor concerns:\n\n1.\tIf the present model were really compatible with Bayesian accounts of sensory representation, shouldn’t its properties be in some way dependent on the statistics of the stimuli presented to the network? As it is, the short-term plasticity timescales and parameters are fixed by the modeler, whereas a serially dependent Bayesian prior over network activity states should theoretically be related to the timescale statistics of presented stimuli. Is there any room for dynamic adaptation of these parameters within the model, or any experimental evidence in favor of such a thing? As it stands, the present model seems to me to be a mechanistic alternative account for empirical data that is likely not compatible with Bayesian approaches; there ought to be experiments that could be used to disambiguate these two classes of model (or model modifications that could produce more synchrony), and a discussion of these points could be very beneficial for the community.\n\n2.\tRelatedly, does the present model make any testable predictions that could be used to validate it? Figure 5 seems to provide some traction on this question, but a more detailed discussion would be beneficial. In particular, how dependent are these results on the (as far as I can tell) arbitrarily selected timescales of STF and STD? More importantly, if this model were really underlying the psychophysical results observed, shouldn’t there be a near-exact relationship between the timescales of STF and STD in early sensory areas and PFC and the timescales of within-trial repulsion/ across-trial attraction?\n\nMinor concerns:\n\nLine 151: the \\Delta S change reported must be highly parameter-dependent, correct? The parameter-sensitivity of this result should be discussed (as well as its implications for testable model predictions as mentioned above).\n\nLine 226: citations need for the gain and two-process models.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn psychophysics and behavioral science, there is by now a large literature documenting the biases in perceptual decision related to the stimulus history. This paper proposed a neural network model to explain these serial dependence effects. Their main model is a two-layer coupled continuous attractor network (CANN), with plasticity in the connectivity. The first layer of the network was assumed to exhibit synaptic depression (STD), while the second layer was assumed to exhibit synaptic facilitation (STF). The network was shown to generate both attractive biases and repulsive biases. Both types of biases have been reported in the previous experimental studies.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nOverall, this is a solid paper that makes an interesting contribution.\n\n- The paper is well written. Although the work is technical, it is relatively easy to follow. The logic of the paper is generally clear. \n\n- The literature on the serial biases in psychophysics is quite messy. Given that, it is particularly nice to see models attempting to unify these experimental findings into a unifying computational framework. \n\n- The proposed network model can simultaneously explain both attractive biases and repulsive biases. \n\n- The model generates direct predictions that can be tested in future experiments (Fig. 5).\n\n\n\nWeaknesses:\n\n- The dependence of the results on the choice of key model parameters (e.g., the time constants of STD/STF) was not discussed. Thus, the robustness of these findings remain unclear. \n\n- While qualitatively comparisons to the experimental results were provided in the paper, there was no direct comparison to the data. \n\n- Line 82-85 motivates the model by citing papers showing STD in sensory areas and LTP in high-level areas. I believe these experimental data are only suggestive. It remains largely unclear what happens regarding STD/STF when the brain is performing some tasks. Can the authors tone this statement down? [minor weakness]\n\nQUESTIONS:\nHow was perceptual decision modeled in the network? Different readout rules may lead to different results as the population activity profile may be asymmetric. It would be interesting to examine this question. \n\nWould it be possible to show some direct comparisons to example experimental results?\n\nAre there any patterns of biases reported in experiments that the current model can not explain? If the answer is yes, it would be useful to discuss these as well. \n\n\nSome more questions about the choice of the models:\n\n(i) Would it be possible for a single-layer network to explain both attraction/repulsion?\n\n(ii) Is the order the the STD and STF important? That is, would a model with STF in the first layer and STD in the second layer lead to different predictions than those shown in the paper?\n\n(iii) Is the attractor dynamics important in the model? If not, one may be able to derive a more general network mechanism.\n\nLIMITATIONS:\nThe  limitations of the work were properly discussed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a two-layer continuous attractor neural network model incorporating synaptic short-term plasticity to elucidate the neural mechanisms underlying repulsive and attractive effect in serial dependence during perception. The lower sensory-processing layer mediates repulsion through neurotransmitter depletion, which drives short-term depression (STD). Conversely, the higher post-perceptionl layer mediates attraction via sustained neurotransmitter release probability, which drives short-term facilitation (STF). The model successfully replicates serial dependence phenomena observed in visual orientation tasks and identifies STP time constants as critical determinants of repulsion/attraction timescales. Furthermore, it offers a biologically implementation for Bayesian interpretations of serial dependence, linking STD to likelihood modulation and STF to prior integration.\n\nSTRENGTHS AND WEAKNESSES:\nQuality：\nStrength: 1)The set up of two-layer CANN model is consistent with biological evidence that sensory areas(e.g. V1) often exhibits short term depression while  higher order cortex area(e.g PFC) exhibits short term facilitation.\n2) The paper systematically implements the temporal-spatial pattern of repulsion and attraction effect by adjust the parameters such as ISI/ITI and $\\Delta$S, which are consistent with classic psychological experiments.\n3) The paper connects the STP to the Bayesian framework, STD to likelihood while STP to prior distribution. \nWeakness:\n1）\tThe paper needs to screening the effects of parameters one the repulsion and attraction in perception and analyze the robustness of parameters such as $\\tau_d$ and $\\tau_f$.\n2）\tThe paper did not compare their neural activity with experimental observations such fMRI or electrophysiological recording data.\n\nStgrength:\n1)The logic flow is fluent, starting from the effect of stp in single layer network to reciprocal interaction between two layer and lastly to the Bayesian framework.\n2) Figures in the paper can intuitively demonstrate the temporal-spatial dynamics of the network, especially, the prediction of ISI on the transition from repulsion to attraction along with change of ISI in figure 5 \nWeakness:\nSome details of the implementation should be clarified such as the noise term during the simulation.\nThe biological basis or the reasons to choose some specific value of parameters also should be clarified such $\\tau_d$ $\\tau_f$ in lower and higher layer of the model.\nProvide some explanation or proof on the continuous attractor guaranteed by rotate-transfer invariant.\n\nSignificance:\nStrength:\nUnified the explanation of repulsion and attraction effect using one single model endowed with STP and bridges the gap between Bayesian computation and neural implementation.\nProvide some testable experiments, such as ISI on the transition from repulsion to attraction.\nWeakness: Lack of the feedback influence from higher order cortex perform attention and decision.\n\nOriginality:\n\nStrength:\n\nClearly proposed that $\\tau_d$ and $\\tau_f$ as the temporal indication of information integration and separation, which is original.\nThe paper provide a biological explanation of likelihood and prior in Bayesian computation in neural system.\n\nWeakness:\nAuthors did not notice the importance of the labor division of STD and STF  in a hierarchical information processing during the  perception.\n\nQUESTIONS:\nQ1. How does the repulsion induced by STD cross talk with attraction induced by STF between two layers? Does the neurotransmitter depletion influence the accumulation of calcium in upper layer? Are there biological evidences?\nQ2.Figure 4 shows the relation with Baysian framework, do we have some mathematical equivalence between two frameworks?\nQ3.Figure 5 shows that repulsion or attraction depends on the comparison between time constant $\\tau_d,\\tau_f$ and ISI/ITI. Are these time constants task dependent? For example, in other perceptual task such as face detection or color discrimination?\nQ4.How to distinct the results from STF or top-down cognitive control on the attraction effect?\nQ5.Can we extend this model to sequential sound detection further multisensory situation? If so, what should be changed on the model setup?\n\nLIMITATIONS:\n1. STP is uniform in each layer in the model, which is not the case biologically. Author should discuss this simplification on the results.\n2.The model ignore the topdown feedback, thus the model can not discuss the effect of attention or working memory on the repulsion or attraction effect, and the goal-oriented bias can not be explained in the present model.\n3. There is two-layer continuous attract model on multi sensory integration and Bayesian integration by Wenhao Zhang, the present model should clearly point out the progress of the present model.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a model endowed with short-term synaptic plasticity to address “serial dependence”. Specifically, the authors aim to reconcile “repulsive” effects (when the current percept is pushed away from a recent sensory input) and “attractive” effects (the percept is drawn toward recent history) within a unified framework. They perform experiments to understand both within-trial and across-trial behavior in their model. Finally, they also provide a loose analogy to Bayesian viewpoints on serial dependence.\n\nSTRENGTHS AND WEAKNESSES:\nThis paper presents a concise and incremental contribution to the literature on short-term synaptic plasticity in neural network models. They tackle an interesting question – serial dependence. I do believe that this would be a useful addition to the literature. However, I’m concerned that the current manuscript appears preliminary and requires substantial revision before it is ready for publication. I’ve detailed some of the concerns/comments below.\n\nI like the start of the introduction. It sets up a clear phenomenon. However, the flow is interrupted by the “related works” section before returning to the introduction. My suggestion to the authors is to have a dedicated “Contributions” subsection at the end of the intro (and before the related works section). This will allow them to complete stating the question, their approach, and what they bring to the table.\n\nFigure 2 needs significant improvement, particularly in terms of captioning and legends.\n\n- I don’t understand what the red horizontal lines are in A, D\n\n- I’m guessing that the different saturated lines in C, F are different ISIs? I had initially thought this was the length of the delay period but it was mentioned otherwise somewhere else in the text. What do the shaded lines represent? Are these standard errors across runs?\nWhy are the authors showing $x$ in A and $u$ in B? This wasn't very clear at first. Why not show both $x$ and $u$ for both the panels? Or $x \\times u$\n\nOverall, the magnitude of the effects reported in Fig. 2 appears very marginal. This is not a compelling demonstration. Did the authors perform any statistical tests to support their findings? Also, is the magnitude of effects reported in prior psychophysics literature consistent with what’s being described here (i.e., a maximum of ~2 degrees either way)? This seems very important to clarify.\n\nAdditionally, my understanding is that even this effect completely relies on the choice of $\\tau$s. I would like to see some kind of sensitivity analysis to understand how robust any of these effects are. It’s possible that the authors chose the values for these time constants based on biological (not modeling) literature. If so, that would also be additionally useful to clarify.\n\nOne suggestion to sort of bypass this comment about hyperparameter choices is that the $\\tau$s can be learnable and the model is trained (with gradient descent) to report the perceived orientation. For example: Fig. 4 in Linsley et al. ICLR 2020. Recurrent Neural Circuits For Contour Detection. This is not exactly the same kind of thing the authors are going for, but I’m mentioning this as a motivating example.\n\nIt also seems to me that it’s important to show negative ISIs. The authors mention that “without loss of generality,” they always cue the second stimulus, but it is not clear to me why this is in principle the same as cueing the first one. In one case, the memory of the stimulus seems way more important. Can the authors clarify?\n\nThe parallels to the Bayesian framework, though interesting, are very vague and unclear to me. Are the authors arguing that, depending on the magnitude of instantaneous noise, the network’s readout is non-deterministic? I didn’t find any experiments systematically testing the effect of noise in this context.\n\nL147 “modeling behaviors of different participants”. What does this mean?\n\nSome grammatical errors that I could spot. Please do a thorough proofread.\nL122 “cuing”\nFig. 2 Caption “The detailed simulations, see Appendix A”\nL153 “manupilating”\n\nQUESTIONS:\nPlease refer to my review above.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-16"}