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In Mandarin Chinese, abbreviation happens commonly to compound words across different syntactic categories. What is the motivation behind this shortening of words? This paper presents an investigation of this phenomenon from an information-theoretic point of view. A corpus study was con- ducted to measure the average amount of information contained in the full (long) form and the abbreviated (short) form of words given certain contexts. The amount of information was then compared between the long and short forms of a word, revealing that the short one usually contains less information, and therefore is more likely to be used in more predictive contexts. This result indicates that speakers of Chinese can choose to use shorter words when the context is more predictive, in accordance with considerations of efficiency.

CogSci 2023

Chinese words shorten in more predictive contexts

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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.<br>
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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<br>[**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.

A long tradition of studies in psycholinguistics has examined the formation and generalization of ad hoc conventions in reference games, showing how newly acquired conventions for a given target transfer to new referential contexts. However, another axis of generalization remains understudied: How do conventions formed for one target transfer to completely distinct targets, when specific lexical choices are unlikely to repeat? This paper presents two dyadic studies $(N=240$) that address this axis of generalization, focusing on the role of nameability --- the a priori likelihood that two individuals will share the same label. We leverage the recently-released \kilogram dataset, a collection of abstract tangram images that is orders of magnitude larger than previously available, exhibiting high diversity of properties like nameability. Our first study asks how nameability shapes convention formation, while the second asks how new conventions generalize to entirely new targets of reference.

Semantic uncertainty guides the extension of conventions to new referents

How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources? One reason is its ability to know how to use its limited computational resources to make clever choices. 
We postulate that people learn this ability from trial and error (metacognitive reinforcement learning). Here, we systematize models of the underlying learning mechanisms and enhance them with more sophisticated additional mechanisms. We fit the resulting 86 models to human data collected in previous experiments where different phenomena of metacognitive learning were demonstrated and performed Bayesian model selection. Our results suggest that a gradient ascent through the space of cognitive strategies can explain most of the observed qualitative phenomena, and is therefore a promising candidate for explaining the mechanism underlying metacognitive learning.

What are the mechanisms underlying metacognitive learning?

Whether timing affects contingency learning is currently poorly understood. Specifically, does temporal predictability aid the acquisition of contingencies? We asked participants in a music contingency learning task to respond to the target (the name of a note) while ignoring tones. To assess whether temporal predictability influences the acquisition of these contingencies, we manipulated the temporal context of tone presentation in two experiments. In Experiment 1, one group of participants listened to tones presented with a regular temporal interval (1000 ms between each trial), while the other group listened to tones presented in a random manner (random intervals selected between 600 and 1400 ms). In Experiment 2 we manipulated the temporal presentation of the tones within trials: one group listened to tones with a 300 ms fixed interval between the cue and the target, and the other group with an interval randomly selected between 0 and 600 ms. Although participants learned to associate pitch labels in both experiments this occurred independently of the timing manipulations. These results confirm prior evidence on contingency learning but do not show the effects of temporal predictability in this learning context.

The role of temporal predictability in contingency learning

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.

Bilingual students’ test-taking strategies in content subject assessments

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.

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Semantic uncertainty guides the extension of conventions to new referents

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