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CBB talk series

The Cognitive Brown Bag (CBB) is a graduate student organized talk series, primarily attended by the faculty, graduate students, and staff from the cognitive labs at Dartmouth College. A list of past CBB talks can be found here.

Virtual CBB : Fall 2020 -  Spring 2021

Talks will be held from 12:15-1:00pm, unless otherwise noted.







FALL 2020




Sep 24

Graduate student data blitz



Oct 8

Doug Addleman (Dartmouth College)

Characterizing conscious and unconscious guidance of spatial attention

Past research has identified unconscious, experience-driven effects on selective attention. In particular, what and where an observer has attended in the past affects future attentional selection. For instance, attention while searching for an item is biased towards locations which contained recent targets—an effect called inter-trial location priming—as well as towards locations which contain targets more often than other regions over a span of time—an effect called location probability learning. I will present research investigating effects of experience-driven attention and how they differ from the better-understood goal-driven form of attention. In line with a multiple-levels framework of attention that emphasizes the variety of mechanisms involved in attentional control and attentional guidance, this research provides evidence for differences between experience-driven and goal-driven attention both at the level of acquiring attentional control setting and at the level of guiding attentional shifts, challenging dominant models that posit attentional guidance as acting via a single system of priority maps. 

Oct 22 @ 2:00pm

Mark Lescroart (University of Nevada, Reno)

Body representations in the brain and bodies in natural visual experience

Body pose information is critical for social communication and interaction, and information about bodies is known to be represented in the lateral occipitotemporal cortex (LOTC) in humans. In this talk, I will describe our fMRI investigation of the extent and organization of the representation of body parts in human LOTC. To address this question we used a modified version of the OpenPose pose-estimation neural network to quatify the positions of body parts in three diverse naturalistic stimulus sets: static natural images, natural movie clips, and computer-rendered scenes. These stimulus sets encompass substantial visual diversity. We modeled the fMRI responses to these stimuli as a function of body parts to estimate the importance of body part features for each voxel in the brain. Our model produced accurate predictions of BOLD responses in withheld data throughout LO, FFA, OFA, and regions in and around EBA and pSTS. Contrasts between model weights for well-predicted voxels revealed patchy selectivity for hands compared to faces around hMT and selectivity for the contralateral visual field throughout LOTC, particularly near the Lateral Occipital Sulcus. These results argue for an anatomically-circumscribed model of body-selectivity in LOTC and lend support to previous reports positing the existence of multiple sub-regions of EBA.. Variance partitioning analyses suggest that our results cannot be fully explained by motion energy or the mere presence of humans. For the last part of this talk, I will highlight some of the complexities that arise in interpretation of results based on natural stimuli, and I will describe a new project aimed at better understanding natural feature variance and covariance in human visual experience. 

Nov 5

Mehran Moradi (Dartmouth College)

A neural dynamic approach to study the functional organization of the brain and learning

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and the integration of task-relevant signals. By applying our method to neural and behavioral data during multiple decision-making tasks, we found that most neurons exhibited multiple timescales in their response, which consistently increased from posterior to anterior regions of the cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, we found that the learning condition (volatility of environment) and training influence the integration of task-related signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility for learning. 

Nov 19

Jiahui Guo (Dartmouth College)

Predicting individual face-selective topography using naturalistic stimuli

Subject-specific, functionally defined areas are conventionally estimated with functional localizers and a simple contrast analysis between responses to different stimulus categories. Compared with functional localizers, naturalistic stimuli provide several advantages such as stronger and widespread brain activation, greater engagement, and increased subject compliance. In this study we demonstrate that a subject’s idiosyncratic functional topography can be estimated with high fidelity from that subject’s fMRI data obtained while watching a naturalistic movie using hyperalignment to project other subjects’ localizer data into that subject’s idiosyncratic cortical anatomy. These findings lay the foundation for developing an efficient tool for mapping functional topographies for a wide range of perceptual and cognitive functions in new subjects based only on fMRI data collected while watching an engaging, naturalistic stimulus and other subjects’ localizer data from a normative sample. 


Lauren Williams (Dartmouth College)

The invisible breast cancer: Experience does not protect against inattentional blindness to clinically-relevant findings in radiology

Retrospectively obvious events are frequently missed when attention is engaged in another task – a phenomenon known as inattentional blindness. Although the task characteristics that predict inattentional blindness rates are relatively well-understood, the observer characteristics that predict inattentional blindness rates are largely unknown. In previous research, expert radiologists showed a surprising rate of inattentional blindness to a gorilla photoshopped into a CT scan during lung-cancer screening. However, inattentional blindness rates were higher for a group of naïve observers performing the same task, suggesting that perceptual expertise may provide protection against inattentional blindness. Here, we tested whether expertise in radiology predicts inattentional blindness rates for unexpected abnormalities that were clinically-relevant. Fifty radiologists evaluated CT scans for lung-cancer. The final case contained a large (9.1cm) breast mass and lymphadenopathy. 66% of radiologists did not detect breast cancer and 30% did not detect lymphadenopathy when their attention was focused on searching for lung nodules. In contrast, only 3% and 10% of radiologists (N=30), respectively, missed these abnormalities in a follow-up study when searching for a broader range of abnormalities. Neither experience, primary task performance, nor search behavior predicted which radiologists missed the unexpected abnormalities. These findings suggest perceptual expertise does not protect against inattentional blindness, even for unexpected stimuli that are within the domain of expertise.

Dec 3

Ruth Rosenholtz (MIT)







Jan 14

Jason Samaha (UC Santa Cruz)



Mar 11

Yael Niv (Princeton University)







Apr 29

John Serences (UC San Diego)



May 27

Leyla Isik (Johns Hopkins University)