While superficially similiar, attending to features (color, orientation, etc.) is associated with distinct behavioural effects, as compared to attending to a location. In Jabar & Anderson (2017), we showed that a similiar distinction exists for probability. While orientation probability affected perceptual precision, spatial probabilty did not.
We wanted to extend that finding to a different task. 2-alternative forced choice (2AFC) tasks are much more common in the literature than estimation tasks, but they typically introduce possible alternate accounts, such as response biases. In a previous post, we outlined a novel method by which response preparation can be measured in a 2AFC task.
That procedure was implemented in a paper that was recently accepted at Attention, Perception, & Psychophysics. Collaborating our previous work, feature probability was indeed found to be distinct from spatial probability. Chiefly, the two probability types operate under different circumstances, and can affect RT and accuracy in different ways.
As an undergraduate student, no one really tells you how to transition from “ambitious, yet confused” to “feeling accomplished, but still slightly confused”. You want to feel prepared for the next step, whether that’s graduate school or your first “real job”, but there’s still that undeniable grey area in between that must be navigated. Speaking from personal experience, I can say with confidence that the most important step in this transition is to define your comfort zone, and then take one step outside of that. It was in this zone of slightly-uncomfortable that I made connections, explored my interests, and gained valuable skills that helped me come out of my undergraduate degree feeling like I had gained more than just a fancy piece of paper.
I consider obtaining a NSERC Undergraduate Student Research Award for the Spring term of my 3rd year the major turning point of my career as an undergraduate student. I knew research was an interest of mine, but was unsure how to explore it at the undergraduate level. I had been a research assistant for 1 year, but was craving more - what happened with all of the data once all was said and done? The process behind receiving this award involved going to Meet the Profs lunches, emailing TAs for classes that I was enjoying, and preparing for interviews with potential award supervisors. But the hardest part, and I can only speak from personal experience, was narrowing down my interests into specific ideas. Truth be told, obtaining the award took two attempts. I applied at the end of my second year, and again at the end of my third year. Between those two time points, I took the time to try and narrow my interests, as well as take a computer science course (CS115). At the time, I wasn’t able to comprehend the importance of this course. Being a BSc in Psychology (Minor in Biology) student, this course was at least one giant step outside of comfortable. To be honest, I struggled through it. After the final exam, all I could think of was “Phew, I’m so glad that’s over”. Little did I know that the skills I had been obtaining in that class were going to help me achieve more than I thought I could as an undergraduate student.
Receiving the award meant that I was granted the opportunity to work hand-in-hand with a professor of cognitive neuroscience and design, as well as conduct, a research project. I quickly learned that the “conduct a research project” part is by far the easiest. The “design a research project”…not so much. The most challenging part by far was the behind-the-scenes developing that involved learning Python. Being a programming novice meant this was going to take me longer than I cared to admit. Long story (and I mean really long story) short, programming and I eventually settled into a place where I felt it was a helpful skill and not just a necessary burden. After months of research and sifting through error and warning messages, the final research project was a go! It resulted from a collaborative effort between Dr. Britt Anderson, Dr. James Danckert, as well as a second USRA recipient, and involved using inhibitory transcranial direct current stimulation (tDCS) to investigate the involvement of the frontal cortex in probabilistic learning.
After we finished collecting participants, I was finally going to experience the part I had been most excited for: data analysis. Seeing as I had already gone through the process of teaching myself Python, I figured I might as well learn another language. Learning R as a statistical tool was, and is, a skill that I consider very valuable. Throughout the USRA, my honours thesis project, and again this summer term as a data analyst for Dr. Anderson and Dr. Danckert, I continue to discover new and efficient means of finding the stories that the data is trying to tell. I feel that I’ve only began to graze the surface of the R knowledge that is available to me, and I look forward to expanding this skill throughout graduate school.
Being involved in the Anderson Lab, obtaining a USRA, and conducting my honours thesis project with Dr. Anderson and Dr. Danckert has allowed me to become not only a competitive graduate school applicant, but a more well-rounded, and inquisitive student. I will be continuing my studies as a Masters student in the Advanced Cognitive Engineering Lab at Carleton University this September, where I will be conducting research on the cognitive and behavioural aspects of virtual and augmented reality technology. In the moment, most of these processes have felt like a struggle. But the important part is that we can look back and reflect upon those struggles, and see them as learning opportunities. The grey zone is a scary one, but with the right mindset and the will to be a little uncomfortable, the journey becomes that much more rewarding.
In Jabar & Anderson (2015), we saw that orientation probability affected perceptual precision, and based on the data, we hypothesized that this was due to V1 tuning changes. We thought of a way to test that hypothesis by looking at the C1 ERP potential, which is thought to index early V1 activity. As it turns out, the electrophysiological data is concordant with our specific hypothesis.
The C1 ERP dampens for high-probability orientations, and this happens within about 15 minutes into the task. This matches our prediction that neurons preferring these orientations benefit from a 'sharpened' V1 tuning, which should decrease overall activity, while increasing perceptual precision. We are currently attempting to computationally model how this sharpening occurs over the stimulus exposures. [Stay tuned for updates on this]
While probability has been thought of as the product of attentional mechanisms or of shifts in decisional criteria, we demonstrate that probability effects can affect low-level perception. Still, an increased P300 (a later fronto-parietal ERP component) is commonly associated with rare targets, and we do see that trend with our low-probability orientations as well. Of interest though is that there appears to be a correlation between C1 and P300 amplitudes. Speculatively, it might be that differences that arise in early perceptual loci drives later decisional processes.
The article is available online at Vision Research. Update: Here is the link to the article. Feel free to contact us if there are any queries.
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