Children struggle to derive scalar implicatures. Initially this was thought to relate to a lack of cognitive resources required for the computation. More recently however, there has been a shift towards the alternatives (what a speaker could have said but did not). The argument is that children struggle to make the scalar implicature associated with some because they are unaware of its relationship with the stronger alternative all. We present a priming study that investigates this. We show that children’s implicatures can be primed equally by alternatives in quantifier and ad hoc expressions. This suggests that children are aware of the scalar relationship between some and all, even if they choose not to derive the implicature.

Priming implicatures in young children

Frequency counters are computational mechanisms that track the frequency or probability of speech units. Such counters are idealizations which re-describe frequency effects in early word segmentation, not providing an underlying learning mechanism from which these effects arise. Previous work has shown that Implicit Chunking represents a plausible learning mechanism explaining infants’ sensitivity to statistical cues when segmenting small-scale artificial languages (French et al., 2011). However, no work has examined whether Implicit Chunking allows to segment naturalistic speech in a developmentally plausible way. Here, we show how a novel symbolic model of Implicit Chunking – CLASSIC-Utterance-Boundary - performs better or as well as previous frequency-based models (i.e., transitional probability, chunking) at predicting children’s word age of first production and a range of word-level characteristics of children’s vocabularies (word frequency, word length, neighborhood density, phonotactic probability). We suggest that explicit frequency counters are not necessary to explain infants’ speech segmentation in naturalistic settings.

Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose
an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions.
Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

Attentive Hybrid Feature with Two-Step Fusion forFacial Expression Recognition


**For a full list of posters in this session, click the See All link on the right side of the page.**

Poster Session 2 -  E: Linguistics

poster

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Poster Session 3 -  A: AI, Computer Science and Computer Models

PS T2.4: Human Activity and Behavior Understanding

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Lewis Bott

3

Presentations

Priming implicatures in young children

Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

Attentive Hybrid Feature with Two-Step Fusion forFacial Expression Recognition

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