Research

Guiding Perception by Memories of Multiple Timescales

PhD Student, Brain and Cognition Lab, University of Oxford, 2021

Our brain is extraordinary at matching incoming sensory signals to past experiences, which guides selective attention and allows us to behave adaptively and dynamically based on predictions and expectations. Memories of different timescales are involved in guiding perception and performance. For instance, when cycling to work, you are not only using long-term memories (LTM) of the spatial map and cycling route to guide your direction, but also relying on current working memories (WM) of traffic lights, pedestrians and cars on the road to adjust your speed or re-plan the route.
However, we still lack a clear understanding of how these memories of different timescales work together to guide adaptive behaviour, and neither do we know the underlying neural mechanisms.
This study aims to elucidate how the prospective nature of memory operates in a multi-timescale and interactive way, and what brain processes are involved in doing so.
Part of the funding of this research comes from UKRI (UK Research and Innovation). An overview of this research on UKRI website can be found here.

Research Intern, Visual Attention Lab, Harvard University, 2020

Background: Our visual system copes with limited capacity using two different modes of attention, a distributed attention mode extracting the gist of a scene (i.e., ensemble perception) and a focused attention mode selecting only relevant information (i.e., selective attention). These two modes of processing serve different purposes. Still, it is unclear how they work together, whether they conflict with each other, and how cognitive control might play a role in conciliating their different processing demands.
Designed and programmed experiments, collected data and conducted analyses with MATLAB.
Developed a novel paradigm incorporating the mean orientation discrimination task and target orientation detection task. Introduced a single-task condition (requiring only one mode of processing), a dual-task condition (requiring both modes of processing), and a mixed-task condition (requiring either one or two modes of processing across trials).
Discovered that people’s performance in target selection and ensemble discrimination tasks positively correlated in both single-task and dual-task conditions, indicating some shared neural mechanisms underlying selective attention and ensemble perception. The mixed-task condition significantly impaired ensemble discrimination performance rather than target selection performance, suggesting a cognitive control strategy favoring selective attention when faced with conflicts in processing demands.

Visiting Researcher, Department of Experimental and Applied Psychology, VU Amsterdam, 2020

Background: Research has shown that interference caused by a salient distractor in visual search tasks can be reduced by suppressing the high-probability location (HPL) of the distractor through implicit learning, while the underlying neural mechanisms and the impact of distractor saliency on suppression effects remain unclear.
Designed and programmed experiments, collected and analyzed data with MATLAB, and wrote the paper (published on Attention, Perception, & Psychophysics).
Developed a novel paradigm to manipulate saliency of distractors in additional singleton task and examined how distractors of different saliency were suppressed at the same HPL.
Inferred the neural mechanisms underlying the saliency-specific mechanism: Spatial probability manipulation elicited attentional modulation of V1 cells that cover the HPL with their classical receptive fields. The attentional modulation is tuned in accordance to the firing rates of the group of V1 cells representing the distractor, which is finally reflected on the saliency-specific reduction of interference when the distractor appears at the HPL.

Research Assistant, Perception and Action Lab, Universityof California, Berkeley, 2019

Background: There hasn’t been enough evidence that people can extract summary statistical information about abstract social traits (e.g., trustworthiness, dominance, submissiveness and the like) because the space of faces features that can draw specific judgments is infinitely large.
Employed a data-driven reverse correlation approach to model the ensemble perception of trustworthiness.
Programmed experiments, collected and analyzed data with Reverse-Correlation Image-Classification Toolbox in R.
Participants viewed face crowds of different average trustworthiness levels, and then did a two-images-forced-choice (2IFC) classification task where they selected one of the two faces (with noise patterns superimposed on the base image) more representative of the average trustworthiness of the face crowd previously shown.
The average of all selected noise patterns constitutes the classification image (CI) and then the CIs for different average trustworthiness levels were statistically compared to determine how much they were different from each other.

Project Leader, Cognitive Neuroscience Lab, Tsinghua University, 2018

Background: Previous research found that a salient but task-irrelevant color singleton would increase the response time to the target form singleton, which is known as attentional capture. However, there haven’t been studies systematically examining how different distractor saliency conditions on a continuous spectrum would have an effect on attentional capture. Additionally, there has been evidence showing spatial heterogeneity in the perception of various visual features, but it’s not clear yet whether attentional capture also exhibits spatial heterogeneity across display locations.
Designed and programmed experiments, collected and analyzed data with MATLAB, and presented posters at the Psychonomic Society 2019 Annual Meeting and VSS 2020 Virtual Meeting.
Examined the effects of distractor saliency defined within multiple attention-guiding dimensions, including color, size and orientation, and established the existence of a certain threshold for distractor saliency to elicit attentional capture.
Found that there existed a spatial pattern of attentional capture susceptibility, which was distinctive and stable for each individual.