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Recurrent neural network (RNN) language models that are trained on large text corpora have shown a remarkable ability to capture properties of human syntactic processing (Linzen &amp; Baroni, 2021). For example, the fact that these models display human-like structural priming effects (Prasad, Van Schijndel, &amp; Linzen, 2019; van Schijndel &amp; Linzen, 2018) suggests that they develop implicit syntactic representations that may not be unlike those of the human language system. A rarely explored question is whether RNNs are also able to simulate aspects of human multilingual sentence processing (Frank, 2021) even though training RNNs on two or more languages simultaneously is technically unproblematic.

CogSci 2021

Cross-language structural priming in recurrent neural network language models

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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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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.

Recurrent neural network (RNN) language models that are trained on large text corpora have shown a remarkable ability to capture properties of human syntactic processing (Linzen & Baroni, 2021). For example, the fact that these models display human-like structural priming effects (Prasad, Van Schijndel, & Linzen, 2019; van Schijndel & Linzen, 2018) suggests that they develop implicit syntactic representations that may not be unlike those of the human language system. A rarely explored question is whether RNNs are also able to simulate aspects of human multilingual sentence processing (Frank, 2021) even though training RNNs on two or more languages simultaneously is technically unproblematic.

technical paper

People watching is a ubiquitous component of human activities. An important aspect of such activities is the aesthetic experience that arises naturally from seeing how elegant people move their bodies in performing different actions. What makes some body movements look better than others? We examined how visual processing contributes to the aesthetics experience from seeing actions, using point-light “creatures” generated by spatially scrambling locations of a point-light walker’s joints. Observers rated how aesthetically pleasing and lifelike each creature looked in a video of the creature moving from left to right. They viewed four kinds of creatures: The joints’ trajectories were either from an upright walker (thus exhibiting gravitational acceleration) or an inverted walker (thus defying gravity) and were either congruent to the direction of global body displacements or incongruent (as in the moonwalk). Observers gave both higher aesthetic and animacy ratings for creatures with upright versus inverted trajectories, and congruent versus incongruent movements. Moreover, after regressing out the influence of animacy, the creatures that move in a natural causal manner (in accordance with gravity and their body displacements) were still preferred. The subtle differences between different kinds of creatures suggest a role of automatic perceptual mechanisms in these preferences. Thus, while our thinking minds may enjoy watching the magical moonwalk, our automatic minds, with a taste for causality, may curtail the impression of its visual beauty.

Aesthetic experience is influenced by causality in biological movements

The block design test (BDT), in which a person has to recreate a visual design using colored blocks, is notable among cognitive assessments because it makes so much of a person's problem-solving strategy ``visible'' through their ongoing manual actions. While, for decades, numerous pockets of research on the BDT have identified certain behavioral variables as being important for certain cognitive or neurological hypotheses, there is no unifying framework for bringing together this spread of variables and hypotheses. In this paper, we identify 25 independent and dependent variables that have been examined as part of published BDT studies across many areas of cognitive science and present a sample of the research on each one. We also suggest variables of interest for future BDT research, especially as made possible with the advent of advanced recording technologies like wearable eye trackers.

Measuring More to Learn More From the Block Design Test: A Literature Review

Three leading analogical reasoning paradigms: scene analogies and pictorial A:B::C:D analogies (both semantically-rich) and geometric analogies (semantically-lean) were solved by 251 participants. Pictorial analogies included four types of lures among the response options (perceptual, categorical, semantic, relational). Moreover, distractors related to both B and C were introduced. Additionally, a fluid intelligence test was applied to examine the relationship between the paradigms and general reasoning ability. Results indicated that: (a) objects semantically related to C yielded the strongest distraction in four-term analogies, categorical and relational distractors yielded moderate effects, perceptual distraction was negligible; (b) distractors related to B were relatively easy to ignore, suggesting that C is the primary object of reference during the response selection; (c) whilst the three tasks correlated significantly without control for fluid intelligence, only the semantically-rich tasks did after fluid intelligence was accounted for, suggesting a certain common mechanism, independent of fluid intelligence, underlying the two.

Distraction in Semantic Analogies and Their Relationship with Abstract Reasoning

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov vio- lations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a com- putational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models peo- ple as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning ex- tend across a range of settings, and do so in line with the pre- dictions of a model that describes the process by which causal judgments are drawn.

Testing a Process Model of Causal Reasoning With Inhibitory Causal Links

One of the major debates in the field of word production is whether lexical selection is competitive or not. For nearly half a century, semantic interference effects in picture naming latencies have been claimed as evidence for competitive (relative threshold) models of lexical selection, while semantic facilitation effects have been claimed as evidence for non-competitive (simple threshold) models instead. In this paper, we use a computational modeling approach to compare the consequences of competitive and noncompetitive selection algorithms for blocked cyclic picture naming latencies, combined with two approaches to representing taxonomic and thematic semantic features. We show that although our simple model can capture both semantic interference and facilitation, the presence or absence of competition in the selection mechanism is unrelated to the polarity of these semantic effects. These results question the validity of prior assumptions and offer new perspectives on the origins of interference and facilitation in language production.

Behavioral interference or facilitation does not distinguish between competitive and noncompetitive accounts of lexical selection in word production

Humans have the astonishing capacity to quickly adapt to varying environmental demands and reach complex goals in the absence of extrinsic rewards. Part of what underlies this capacity is the ability to flexibly reuse and recombine previous experiences, and to plan future courses of action in a psychological space that is shaped by these experiences. Decades of research have suggested that humans use hierarchical representations for efficient planning and flexibility, but the origin of these representations has remained elusive.
This study investigates how 73 participants learned hierarchical representations through experience, in a task in which they had to perform complex action sequences to obtain rewards. Complex action sequences were composed of simpler action sequences, which were not rewarded, but whose execution led to changes in the environment. 
After participants learned action sequences, they completed a transfer phase. Unbeknownst to them, we manipulated either complex or simple sequences by exchanging individual elements, requiring them to relearn. 
Relearning progressed slower when simple (rather than complex) sequences were changed, in accordance with a hierarchical representations in which lower levels are quickly consolidated, potentially stabilizing exploration, while higher levels remain malleable, with benefits for flexible recombination.

How the Mind Creates Structure: Hierarchical Learning of Action Sequences

In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, as well as their properties. When considering concrete motor actions, this knowledge has been called the object affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where each dimension corresponds to a broad mode of interaction, based on verb selectional preferences in text corpora. This object embedding makes it possible to predict human judgments of verb applicability to objects better than a variety of alternative approaches. Furthermore, we show that the dimensions in this space can be used to predict categorical and functional dimensions in a state- of-the-art mental representation of objects, derived solely from human judgements of object similarity. These results suggest that interaction knowledge accounts for a large part of mental representations of objects.

Mental representations of objects reflect the ways in which we interact with them

Many students fail to develop adequate understanding of mathematical equivalence in early grades, which impacts later algebra learning. Work from McNeil and colleagues proposes that this failure is partly due to the format of traditional instruction and practice with highly similar problems, which encourages students to develop ineffective representations of problem types (McNeil, 2014, McNeil & Alibali, 2005). In the current study, we explore students’ learning trajectories in two matched equivalence interventions. We show that, relative to an active control, the principle-based treatment intervention gives rise to a greater number of successful learners, a designation that, in turn, leads to improved performance on distal transfer assessments. We further demonstrate a predictive relationship between students’ engagement with the intervention, via workbook completion, and likelihood of becoming a successful learner. Our findings have implications for early detection of learning and subsequent scaffolding for low-performing students.

Predicting Learning and Knowledge Transfer in Two Early Mathematical Equivalence Interventions

Understanding cause and effect relationships gives power to produce desired effects and avoid negative outcomes. Despite the power of causal explanations, people often lack full understanding of how causes relate to or produce their effects. In two experiments, we explored how people search for information to enrich their causal explanations of real-world phenomena when given the chance. Participants completed an information search task that provided a causal relationship where they could seek out mechanistic information at different steps between the cause and the effect. We measured where people searched in the causal chain of events that made the explanation. We found that when allowed to search freely (Experiment 1) or when instructed that they must search for information (Experiment 2) participants consistently sought out information closest to the root cause in the explanation. We discuss implications for how to improve the teaching of new explanations to maximize the informational desires of the learner.

Searching for the Cause: Search Behavior in Explanation of Causal Chains

Temporal expectation for future events allows people to prepare more efficiently for the future. In sensorimotor tasks, it has been considered as an important factor that influences the accuracy and speed of responding to specific sensory events. However, there was no consensus whether the temporal expectation functioning in sensorimotor tasks is simply an emergent property of task-specific, low-level circuits, or an abstract representation shared by higher-level cognition. In four experiments, we asked whether two simultaneously processed tasks—one of lower-level and the other of higher-level cognition—would be influenced by the same temporal expectation. One task was speeded response to a target stimulus, where the target was cancelled on 30% of the trials. The other task was a real-time gambling task, where participants needed to predict from time to time whether the current trial would end up with target or cancellation. Both the target and cancellation latencies followed specific distributions, with the distribution of cancellation latencies varied across blocks. Participants’ choices in gambling provided real-time measures of the updating of temporal expectation over time, which suggest imperfect representation of temporal distributions. Importantly, we found that on a trial when participants predicted an ending of cancellation instead of target, their subsequent response to the target was strikingly slower (up to 1/3 increase in response time). It implies temporal expectation is shared across higher-level and lower-level cognitive tasks.

Cross-language structural priming in recurrent neural network language models

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Aesthetic experience is influenced by causality in biological movements

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Selection, Engagement, & Enhancement: A Framework for Modeling Visual Attention

Cross-language structural priming in recurrent neural network language models

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Next from CogSci 2021

Aesthetic experience is influenced by causality in biological movements

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Selection, Engagement, & Enhancement: A Framework for Modeling Visual Attention

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