I'm Long Ni,a 5th year Ph.D student from the Dept of Psychology of Upenn and co-supervised by Drs. Alan Stocker and Johannes Burge. I am studying visual perception and working memory, using psychophysics and computational models.
[1]. Ni, L., & Burge J (2024).Encoding fidelity of binocular disparity from natural stereo-images: impact of internal noise and external stimulus variability. In prep
I have been working with Dr. Johannes Burge on characterizing the properties of simple-cell like receptive fields that are involved in encoding behaviorally-relevant latent variables, such as binocular disparity. Of particular interest to us is the question of how external stimulus variability and internal encoding noise jointly determine the binocular receptive field properties that are most useful for encoding binocular disparity in natural scenes. We addressed this question using computational modeling. Specifically, we used nonlinear-linear subunit modeling framework to simulate the responses of binocular receptive fields to binocular disparity from natural stereo-images with groundtruth disparity at each pixel. We then computed encoding fidelilty, measured by Fisher information, of different levels of binocular disparity by receptived fields with different preferred properties (e.g., spatial frequency, phase shift). We find that both the task-relevant latent variable (i.e., binocular disparity), and two sources of task-irrelevant stimulus variability—luminance contrast variability and local depth variability (i.e., disparity contrast)—place fundamental limits on the precision of disparity estimation. They also determine the usefulness of different encoding sub-spaces, as defined by the preferred spatial frequency of binocular receptive field pairs. Within each encoding subspace, internal noise determines the usefulness of different basis elements spanning the subspace, as defined by the binocular phase shifts of the receptive field pairs. We presented our major findings at VSS2022 (see our Poster here). The preprint will come out soon. Stay tuned~
[2]. Ni, L., & Stocker, A. A. (2023). Efficient sensory encoding predicts robust averaging. Cognition, 232, 105334.
I did this work with Dr. Alan Stocker. This work started with a behevioral phenomenon in ensemble perception: observers tend to assign higher weights to stimuli with features close to the set mean and lower weights to those with features away from the set mean. This phenomenon, termed robust averaging, has been reported in ensemble perception of both low-level visual features (e.g., color, orientation, and shape) and high-level visual stimuli (e.g., faces). Robust averaging has been taken as evidence for the non-optimal integration of sensory information. In this work, however, we show that robust averaging naturally emerges from an optimal integration process when sensory encoding is efficiently adapted to the overall ensemble statistics in the experiment relative to a reference. The core idea of efficient coding is that the limited sensory bandwidth of a perceptual system is optimally allocated according to the statistical regularities of the sensory input such that stimuli that occur frequently are more accurately represented than those occurring rarely. Prior work from the lab has shown that efficient representations according to the natural, long-term stimulus statistics can account for various aspects of perceptual bias and variability that traditional Bayesian observer models are not able to explain (Wei and Stocker, 2012, 2015). Our work suggests that efficient sensory encoding can operate on short time-scales to improve overall decision performance. (see our paper here.) We recently extended this exciting line of work by providing compelling empirical and computational evidence that our visual system can rapidly form efficient representation of ensemble stimuli according to their overall statistics relative to a dynamic reference. I will be giving a talk presentation about the lastest development of this work this May at VSS (2024), with the title 'Efficient coding of ensemble stimuli relative to a dynamic reference'.
[3]. Ni, L., & Ma, W.J. (2024). Bayesian models of interference across domains: the continuous 2-back task, delayed estimation, and crowding. Under review
I started this project with Dr. Wei Ji Ma several years ago. Back then, we were interested in dissoicating the sources of interference in the N-back task, a widely used working memory paradigm. Performance in the classic N-back task is known to suffer from interference from the intervening items in the sequence. Unfortunately, the categorical stimuli (e.g., letters and numbers) used in the classic N-back task precluded researchers from measuring interference as a function of probe-distractor similarity and characterizing the impact of different items in the sequence on task performance. So the first thing we did was to create a new variant of the classic N-back task. In the new variant, we used continuous stimuli such as color and orientation, which allowed us to measure similarity-based interference. With the desirable dataset collected from the continuous N-back task, we next created Bayesian models of interference to dissociate two general sources of interference: pooling and substitution. Crucially, each of our interference models differs from the optimal non-interference model in only component only, allowing us to pinpoint the locus of interference. Our modeling results on the continuous N-back task can be found here (preprint). Lately, we also demonstrated that our models can be generalized to other domains and help dissociate the sources of interference beyoung the N-back task.
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[4]. Yu, W., Ni, L., Zhang, J., Zheng, W., & Liu, Y. (2023). No need to integrate action information during coarse semantic processing of man-made tools. Psychonomic Bulletin & Review , pages 1–10, 2023.
[5]. Gong, J., Yu, W., Ni, L., Jiao, Y., Liu, Y., Fu, X., & Xu, Y. (2020, October). " I can't name it, but I can perceive it " Conceptual and Operational Design of” Tactile Accuracy” Assisting Tactile Image Cognition. In The 22nd International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-12).
[6]. Ni, L., Liu, Y., & Yu, W. (2019). The dominant role of functional action representation in object recognition. Experimental Brain Research, 237(2), 363-375.
[7]. Ni, L., Liu, Y., Yu, W., & Fu, X. (2019). The China Image Set (CIS): A new set of 551 colored photos with Chinese norms for 12 psycholinguistic variables. Frontiers in Psychology, 10, 2631.
[8]. Li, L., Ni, L., Lappe, M., Niehorster, D. C., & Sun, Q. (2018). No special treatment of independent object motion for heading perception. Journal of Vision, 18(4), 1-16.
Email: nilong@sas.upenn.edu