United States

We examined bilingual students’ test-taking strategies through an eye-tracking experiment. 71 university students with different levels of English (L2) proficiency attempted carefully selected multiple-choice and structured questions on their Biology knowledge. Their eye movement behavior was captured during the process and analyzed using both summary statistics of eye movement measures and gaze transition consistency as measured in entropy using Eye Movement analysis with Hidden Markov Models (EMHMM) to infer their test-taking strategies, which were then correlated with test performance measured in accuracy and reaction time. Results generally showed that eye movement behavior during test taking was associated with performance. In particular, gaze transition consistency predicted performance beyond summary statistics of eye movement measures. However, language proficiency was not associated with eye movement behavior or performance. These results may imply that participants’ moment-by-moment, flexible problem-solving strategies when taking the test would better predict their performance than their underlying language proficiency.

CogSci 2023

Bilingual students’ test-taking strategies in content subject assessments

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

poster

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
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#### **CogSci 2023 Program Book**
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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

People need to generate and test hypotheses in order to create accurate representations of their environments. But how do they know which hypotheses to consider when there are often infinitely many possibilities? Here we explore the idea that evolutionary mental representation generation and selection processes – responsible for the generation of both local (i.e., within a task) and global (i.e., about a task) representations – enable people to address this problem. We investigated this through an active learning experiment, where participants’ task was to discover a hidden rule determining the behavior of a simple physical system. Specifically, we aimed to manipulate factors that constrain this process, particularly through experimental instructions and feedback. We found that providing more opportunities for participants to recognize when their initial task conceptualization was wrong and adjust it helped them create more accurate representations about the task, which in turn led to better accuracy within the task.

How do instructions, examples, and testing shape task representations?

In this paper we study human sequential behavior by integrating cognitive, evolutionary, and computational approaches. Our work centers around the emergence of shared vocabularies in the Embodied Communication Game (ECG). In this game, participant pairs solve a shared task without access to conventional means of communication, enforcing the emergence of a new communication system. This problem is solved typically by negotiating a shared set of sequential signals that acquire meaning through interactions. Individual differences in Personal Need for Structure (PNS) have been found to influence how this process develops. We trained deep learning models to mimic the emergence of these systems and used hyperparameter optimization to approximate latent human cognitive variables to explain human behavior. We demonstrate that models based on bidirectional LSTM networks are better at capturing human behavior than unidirectional LSTM networks. This suggests that human sequence processing in the ECG is influenced by expected future states. The approximated variables cannot explain the effects of PNS, but we do provide evidence suggesting that random and uncertainty-directed exploration strategies are combined to develop optimal behavior.

Modeling Human Sequential Behavior with Deep Neural Networks in Emergent Communication

There is an apparent discrepancy between visual perception, which is colorful, complete, and in high resolution, and the saccadic, and spatially heterogeneous retinal input data. In this work, we computationally emulated foveated color maps and intensity channels as well as intra-saccadic motion data using a neuromorphic event camera. We used a convolutional neural network, U-Net, and adversarial optimization to demonstrate how retinal inputs can be used for the reconstruction of colorful images in high resolution. Our model may set the groundwork toward the development of biologically plausible neural networks for computational vision perception.

Perceptual colorization of the peripheral retinotopic visual field using adversarially-optimized neural networks

Deep convolutional neural networks (DCNNs) have been found to demonstrate hierarchical mapping to human brain regions on tasks such as object recognition. However, it remains unclear if such hierarchical mapping also applies to action recognition, which involves dynamic visual information processing. Here, we compared action representations of two-stream DCNNs to the human visual system. Five visual areas that are associated with object and action processing were selected. Nine human action categories were adopted from three semantic classes to examine the action representations of both DCNNs and human visual areas. In two fMRI experiments, actions were presented in the forms of Maya-rendered videos and point-light biological motion videos. Results showed that although two-stream DCNNs demonstrated hierarchical representations of actions as layers grow deeper, DCNNs lack a hierarchical mapping to human visual areas. Consistently across different video displays and DCNN pathways, only the top DCNN layers demonstrated high similarity to representations in the human visual system. The results suggest that the dynamic representations of human actions may be different in DCNNs compared to the human visual system, even after big-data training.

Mapping between the Human Visual System and Two-stream DCNNs in Action Representation

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that iterated learning alone, and communication alone, do not yield the same outcome as clearly.

Iterated learning explains aspects of color naming beyond those explained by efficiency

How do humans get better at planning? Previous work postulated that the improvement of cognitive strategies occurs through feedback-based metacognitive reinforcement learning (MCRL). However, it is not clear whether and, if so, how people can learn planning strategies without reinforcement. To answer these questions, we experimentally investigated the effect of frequency of feedback on people's ability to learn adaptive planning strategies. We found that participants receiving feedback only 25\% of the time nonetheless learned about as well as participants receiving constant feedback. Quantitative modelling of the data revealed that state-of-the-art MCRL models cannot explain this finding. However, extending these models by a mechanism generating an additional learning signal through self-evaluation of plan quality can account for people's ability to learn planning strategies without feedback. The findings of this research have implications for the design of learning environments and enabling people and machines to self-sufficiently improve their strategies in naturalistic settings.

Learning planning strategies without feedback

In psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, there are a number of cognitive mechanisms interacting to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper seeks to extend the GEMS methodology, a system that generates models of cognition using genetic programming, for differing DMTS experimental conditions. This allows us to investigate the strategies that participants might be using, while looking at differences in strategy depending on task variations. We also discuss possible weaknesses with the methodology.

Genetic Programming for Developing Simple Cognitive Models

Neural Probabilistic Learner and Sampler (NPLS) is an algorithm
that has simulated children’s non-symbolic probability
learning from visual stimuli such as collections of different
colors of marbles. Although NPLS closely simulates the
cognitive process of probability learning, the training of such
learning algorithms often uses binary encoding of inputs that
represent the perceived visual stimuli, avoiding simulation of
the visual perception of the stimuli. Here, the computer vision
technique You Only Look Once (YOLO) (Jocher et al.,
2021; Redmon et al., 2016), is integrated into the workflow
of an NPLS simulation probability learning experiments with
children. YOLO is a convolutional neural network (CNN) designed
to detect objects. The model’s performance on marble
datasets is tested through an analysis of precision and recall.
Results indicate that the YOLO model, when trained sufficiently,
outputs predictions on marble image datasets with high
accuracy and precision. We also analyze YOLO’s suitability as
a biologically plausible model of visual processing, interfering
with YOLO’s training process by shortening the training time
to examine the effects of perceptual errors on simulated probabilistic
reasoning.


A Perceptual Front-End for Probability Learning: Object Detection with YOLO

Autonomy support is an interpersonal tone of support and understanding. Teachers' instructional behaviors with autonomy support facilitate students' inner motivational resources and enhance their engagement in learning activities. Identifying these behaviors is vital in terms of teaching effects on students' academic performance and behaviors. However, existing methods heavily rely on psychological questionnaires or observers, which are time consuming and difficult to apply in large scale teaching scenarios. In this work, we combine deep neural networks with psychology to achieve automatically identifying the behaviors. To this end, we create the first Chinese instructional behavior dataset for deep learning, and then construct a BERT-based network for modeling psychological features and identifying behaviors. Since frequencies of different behaviors are highly imbalanced, we further design a novel loss function to adaptively optimize training weights for each behavior. Extensive experiments verify the effectiveness of our method and explain how the behaviors are identified.

Automatically Identifying Teachers' Autonomy Support using Text Classification on Imbalanced Data

We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. Specifically, we design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit, integrating learnable lateral competition and elements of predictive coding, an emerging and viable neurobiological process theory of cortical function, with the forward-forward (FF) adaptation scheme. Furthermore, PFF efficiently learns to propagate learning signals and updates synapses with forward passes only, eliminating key structural and computational constraints imposed by a backprop-based scheme. Besides computational advantages, the PFF process could prove useful for understanding the learning mechanisms behind biological neurons that use local signals despite missing feedback connections. We run experiments on image data and demonstrate that the PFF procedure works as well as backprop, offering a promising brain-inspired learning algorithm for classifying, reconstructing, and synthesizing data patterns.

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How do instructions, examples, and testing shape task representations?

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