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Virtual CogSci Commencement Reception

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May 22, 2020 @ 11:00 am 12:00 pm

Cognitive Science graduating students, family, and friends – please join us at our virtual department reception to celebrate cognitive science BA, MA, and PhD degree recipients of Summer 2019, Fall 2019, and Spring 2020. RSVP using the form below; we will email you the event link.  [formidable id=”54″ minimize=”1″] Participation: Here are ways you can help make this a virtual occasion to remember!
  • Bring a beverage for the toast.
  • Wear Hopkins gear or blue/white clothes.
  • Download and use a Johns Hopkins virtual background in Zoom. (instructions)
  • Make a poster to hold up to the camera. (“Congrats, Jane!”, “We’re proud of you, John!”) Prepare a ‘shout-out’ for someone who is graduating and share it in the chat room.
We have a program planned for our graduates. Here are a couple highlights… Make-your-own Cap & Gown Challenge: Graduates are challenged to use the materials around them to design their own cap and gown. Get creative! Be silly! Get your pets in on the fun! Model the ensemble during the virtual event. Video participants will vote and the winner will receive an e-gift card to Amazon.com. Participation rules will be emailed directly to graduates. Faculty Chats: Graduates and their guests will have an opportunity to join small breakout groups to chat with faculty. So stick around! Can’t wait for the big day? We have a puzzler for you to help fill the time: https://thankyou.jhu.edu/ (by Cognitive Science PhD student Tom McCoy).
Technical Help: How to join a zoom meeting (video). If you are new to using Zoom, we suggest you watch this video in advance. The webpage includes a “test meeting” and detailed written instructions on how to use Zoom with different devices, operating systems, web browsers, etc.
Date:
May 22, 2020
Time:
11:00 am – 12:00 pm

Organizer

Sarah Ciotola
Email:
sciotol3@jhu.edu

Dissertation Presentation: Pang Chaisilprungraung

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Pang Chaisilprungraung will present an open talk on her dissertation on “Axes in Object-Centered Shape Representation: Insights from Mirror-Image Reflection Errors.” The event link has been circulated via email only for security purposes. You may request the link via email to sciotol3 @ jhu.edu.

Abstract: Successful recognition and interaction with objects require the ability to perceive and represent how object parts are internally related (e.g., a teapot’s handle is attached to its body, at a location opposite to the spout).  According to many theorists, axes defined on the basis of object geometry provide a coordinate system for representing the locations and orientations of object parts.  Understanding the function and the nature of object axes can produce key insights into theories of shape representation.  This thesis examines two poorly understood problems: 1) what precisely is the mechanism by which coordinate axes accommodate shape representation and 2) what aspects of object geometry determine how axes are assigned to shapes?  I address these problems in light of previous research on object orientation representation. The coordinate orientation representation (COR) theory (e.g., McCloskey, 2009) posits that the brain represents the orientation of a whole object (e.g., orientation of a pen on a floor) by encoding relations between sets of coordinate axes separately defined for the object (the pen) and for an extrinsic reference frame (the floor). The first portion of this thesis demonstrates that the COR mechanism can be adapted to explain how relationships of parts within an object are represented.  The second portion uses a novel paradigm motivated by the COR theory (Chaisilprungraung et al., 2019) to investigate whether the origin of coordinate axes (i.e., the point where the axes intersect) corresponds to an individual elongated part (e.g., the handle of a hatchet) or the overall object’s center (e.g., the hatchet’s center of mass).  The latter portion’s result deepens the understanding about the mechanism for representing shapes under the adapted COR theory.  These novel findings are important not only for psychological research on visual shape processing, but also for comparing the coordinate-axis view with prominent views in computer vision research (e.g., Medial Axes theory, image recognition based on neural network theory).

Department Welcome

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September 3, 2020 @ 3:30 pm 4:30 pm

At this annual event, we welcome back familiar faces and greet our newest department members. By invitation only.
Date:
September 3, 2020
Time:
3:30 pm – 4:30 pm

Organizer

Sarah Ciotola
Email:
sciotol3@jhu.edu

Location: Online/Zoom

Brown Bag: Junghyun Min

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MA student Hyun Min will present on “Heuristics in language models and syntactic augmentation to mitigate them.”  Discussion to follow.

Department members, CogSci/Ling undergraduates, and invited guests may to attend these Brown Bag Lunches. Meeting links will be distributed via email only to those groups.

Upcoming Brown Bag Lunches…

  • 09/25/20 – Eric Rosen
  • 10/16/20 – Suhas Arehalli
  • 10/30/20 – Diana Dima
  • 11/13/20 – An Nguyen
  • 11/20/20 – Alon Hafri
  • 12/04/20 – Kyriaki Neophytou

Colloquium: Jane Chandlee

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Rule Application Modes Revisited

ABSTRACT: Foundational work in rule-based phonology (Chomsky and Halle 1968, Anderson 1969, Johnson 1972, Howard 1972, Lightner 1972, Kenstowicz and Kisseberth 1979) brought about a debate over whether rules should be assumed to apply in a simultaneous manner, an iterative manner, or both. The shift to constraint-based formalisms like Optimality Theory (Prince and Smolensky 1993, 2004), which are inherently iterative, lead to a stronger hypothesis that noniterativity should have no role in phonology at all (e.g., Kaplan 2008). In this talk I address this debate from a computational perspective, by first recasting the simultaneous/iterative distinction as one of input- versus output-based computation. More specifically, in earlier work (Chandlee 2014) I proposed the input strictly local (ISL) functions and output strictly local (OSL) functions as implementations of simultaneous and iterative rule application, respectively. Subsequent investigation, however, has revealed that that correspondence is not as straightforward as it seems: an apparently simultaneous phonological map may still be OSL, while other maps may be necessarily ISL for formal reasons that have nothing to do with iterativity. The conclusion then is that there is a role for input-based computation in a theory of phonology, but that role is not entirely tied to noniterativity. With an increasing number of phenomena being analyzed as ISL (e.g., Oakden and Chandlee 2020, Dolatian and Rawski 2020), this talk clarifies what’s at stake for such classifications in the bigger picture of the (computational) nature of phonology.

Dr. Jane Chandlee is an assistant professor of linguistics at Haverford College.

The Zoom link will not be posted online. If you wish to receive colloquium announcements, please subscribe here.

Brown Bag Lunch: Eric Rosen

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Inflectional paradigms as a dynamic system (and why I decided at the last minute to stop worrying about transformers and switch to GSC instead).

Eric Rosen is a postdoc working with Prof. Paul Smolensky. Discussion to follow.

Department members, CogSci/Ling undergraduates, and invited guests are invited to attend these Brown Bag Lunches.

Upcoming Brown Bag Lunches…

  • 10/16/20 – Suhas Arehalli
  • 10/30/20 – Diana Dima
  • 11/13/20 – An Nguyen
  • 11/20/20 – Alon Hafri
  • 12/04/20 – Kyriaki Neophytou

Colloquium: Nikolaus Kriegeskorte

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Testing deep neural network models of human vision with brain and behavioral data.

ABSTRACT: To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments [1]. Neural network models have enabled major strides in computer vision and other artificial intelligence applications. This brain-inspired technology provides the basis for tomorrow’s computational neuroscience [1, 2]. Deep convolutional neural nets trained for visual object recognition have internal representational spaces remarkably similar to those of the human and monkey ventral visual pathway [3]. Functional imaging and invasive neuronal recording provide rich brain-activity measurements in humans and animals, but a challenge is to leverage such data to gain insight into the brain’s computational mechanisms. I will discuss statistical inference techniques that enable us to adjudicate among deep neural network models on the basis of brain and behavioral data [4-5]. In order to capture the dynamic computations in biological brains, neural network models need to be recurrent. Recurrent networks can recycle their limited neuronal resources to enhance their performance, trading off speed and energy in exchange for higher accuracy [6, 7, 8]. Recurrent convolutional neural networks also provide better accounts of the dynamics of human ventral-stream visual representations, as measured with magnetoencephalography (MEG) [9]. Finally, I will discuss the method of controversial stimuli [10], which enables us to optimize experiments for adjudicating among computational theories that are implemented in neural network models. Controversial stimuli are stimuli that models disagree over, in terms of their representations or classifications. Human psychophysical experiments with controversial stimuli suggest that generative models might be critical for explaining human perception [10]. Current models still fall short of explaining how humans can so rapidly, robustly, and deeply understand the causes and implications of a visual image. However, the existing tools of measurement and modeling and the emerging methods for testing models with measurements are accelerating progress in cognitive computational neuroscience [1, 2].

Dr. Nikolaus Kriegeskorte is a professor of psychology and neuroscience and director of cognitive imaging at Columbia University. He is also the PI of Columbia’s Zuckerman Institute.

The Zoom link will not be posted online. If you wish to receive colloquium announcements, please subscribe here.


References: Open to see References

Brown Bag Lunch: Suhas Arehalli

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Neural Language Models as Models of Agreement Attraction. Discussion to follow.

Suhas Arehalli is a graduate student working with Prof. Tal Linzen.

Department members, CogSci/Ling undergraduates, and invited guests are invited to attend these Brown Bag Lunches.

Upcoming Brown Bag Lunches…

  • 10/30/20 – Diana Dima
  • 11/13/20 – An Nguyen
  • 11/20/20 – Alon Hafri
  • 12/04/20 – Kyriaki Neophytou

Colloquium: Heather Burnett

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Social Signaling and Reasoning under Uncertainty: French “Écriture Inclusive”. 

ABTRACT: Gender inclusive writing (“écriture inclusive” EI) has long been the topic of public debates in France. Examples of EI for the word “students” are shown in (1).

(1) a. étudiant·e·s (point médian)
b. étudiant.e.s (period)
c. étudiants et étudiantes (repetition)
d. étudiant(e)s (parentheses)
e. étudiant-e-s (dash)
f. étudiantEs (capital)
g. étudiant/e/s (slash)
h. étudiant–e–s (double dash)

These debates have amplified since the Macron government prohibited the use of the point médian (1a) in official documents in 2017 (Abbou et al. 2018). In addition to being a point of disagreement between feminists and anti-feminists, EI is also controversial among feminists: it has many variants (1), who often disagree on which variant should be used (Abbou 2017).

In this talk, I argue that the source of many of these disagreements lies in the fact that French écriture inclusive has developed into a rich social signalling system: based on a quantitative study of EI in Parisian university brochures (joint work with Céline Pozniak (Burnett & Pozniak 2020)), I argue that writers use or avoid EI in part in order to communicate aspects of their political orientations. We show that these aspects involve writers’ perspectives on gender, but also stances on issues unrelated to gender, such as (anti)institutionalism and support for the Macron government. I then outline a research programme for studying this signalling system from a formal perspective: following Burnett (2019), I show how we can use probabilistic pragmatics to analyze EI’s contribution to writers’ political identity construction and the consequences that this has for its use as a tool for promoting gender equality and social change.

References

  • Abbou, J., Arnold, A., Candea, M., & Marignier, N. (2018). Qui a peur de l’écriture inclusive? Entre délire eschatologique et peur d’émasculation Entretien. Semen. Revue de sémio-linguistique des textes et discours, (44).
  • Abbou, J. (2017). (Typo) graphies anarchistes. Où le genre révèle l’espace politique de la langue. Mots. Les langages du politique, (1), 53-72.
  • Burnett, H. & C. Pozniak. (2020). Political Dimensions of Écriture Inclusive in Parisian Universities. Manuscript, Université de Paris.
  • Burnett, H. (2019). Signalling Games, Sociolinguistic Variation and the Construction of Style. Linguistics and Philosophy, 42(5), 419-450.

Dr. Heather Burnett is a CNRS (French National Centre for Scientific Research) researcher working in the Laboratoire de Linguistique Formelle at the Université de Paris 7-Denis Diderot

The Zoom link will not be posted online. If you wish to receive colloquium announcements, please subscribe here.

PhD Admissions Info Session

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