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Inferences of other’s emotion states are influenced by multiple sources including target cues such as facial movements and the situational context. Our understanding of how information from these cues is integrated is limited, however. We examined whether people integrate information from faces and situations to infer emotions as predicted by an existing model of affective cognition. We applied a Bayesian cue-integration model to a dataset that includes a variety of complex social situations that reflect the heterogeneity of emotion contexts in social lives. Results indicate that when viewing both faces and situations, situation information alone predicted people’s inference about emotions better than Bayesian cue-integration model. However, there was some variability in this pattern across emotion categories as the Bayesian cue-integration model best predicted inferences for emotion categories of amusement and happiness. These findings better our understanding of the interplay between facial and situational cues in informing emotion inferences.

CogSci 2022

Modeling Cue-integration in Emotion Inferences

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

technical paper

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





An examination of emotion recognition and response to music can isolate perception and experience of emotion from the potentially confounding effects of other social cues (e.g., faces). Participants aged 5-6-years-old listened to clips of calm, scary, and sad music and either identified the emotional content of the music or reported on the feelings elicited by the music clip. Children correctly identified the emotions and reported feeling the emotions conveyed in music above chance. Accurately recognizing and resonating with the emotion conveyed were correlated, although the relationship varied as a function of child characteristics. Specifically, children whose parents reported them as showing more prosocial behavior had significantly greater alignment between emotion recognition and resonation. Results provide new insights into emotion perception in the absence of direct social signals and provide evidence that children’s ability to perceive and resonate with the emotion conveyed through music differs depending on key socioemotional characteristics.

Alignment of Knowing Versus Feeling the Emotion in Music During Middle-Childhood

Children increasingly rely on the internet for information. In this study, children ages 7-10 (n=80) indicated whether a human source or Google could answer questions involving past, present, or future events, and which informant would be better able to do so. Children indicated that Google could accurately answer questions more frequently than the human could, and they were least likely to indicate that either informant could answer questions about the future. Children selected Google as the better informant across all question types, but they did so most frequently in the future condition. Children’s responses also varied such that as the age of the participants increased, they judged the person as less able to answer questions about current events and Google as better able to do so. Children believe that search engines can accurately answer questions more often than a person can, perhaps reflecting their exposure to digital learning environments.

Children’s Judgments About Asking for Past, Present, and Future Information from Google and a Person

What information guides individuals to trust an algorithm? We examine this question across three experiments that consistently found explanations and relative performance information increased trust in an algorithm relative to a human expert. Strikingly however, in only 23% of responses (414/1800) did an individual’s preferred agent for a task (e.g., driving a car) change from human to algorithm. Thus, initial preferences were ‘sticky’ and largely resistant to large shifts in trust. We discuss theoretical and practical implications of this work and identify important contributions to our understanding of how summaries of information can improve people’s willingness to trust decision aid algorithms.

Trusting algorithms: performance, explanations, and sticky preferences

How do people build up trust with artificial agents? Here, we study a key component of interpersonal trust: people's ability to evaluate the competence of another agent across repeated interactions. Prior work has largely focused on appraisal of simple, static skills; in contrast, we probe competence evaluations in a rich setting with agents that learn over time. Participants played a video game involving physical reasoning paired with one of four artificial agents that suggested moves each round. We measure participants' decisions to accept or revise their partner's suggestions to understand how people evaluated their partner's ability. Overall, participants collaborated successfully with their agent partners; however, when revising their partner's suggestions, people made sophisticated inferences about the competence of their partner from prior behavior. Results provide a quantitative measure of how people integrate a partner's competence into their own decisions and may help facilitate better coordination between humans and artificial agents.

How do people incorporate advice from artificial agents when making physical judgments?

Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems. It was shown that students who know how and when to use each strategy (StrTime) outperformed those who know neither and stick to the default strategy (Default). In this work, students were trained on a logic tutor that supports a default forward-chaining and a backward-chaining (BC) strategy, then a probability tutor that only supports BC. We investigated three types of interventions on teaching the Default students how and when to use which strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime students received no interventions. Overall, our results show that Nudge outperformed their Default peers and caught up with StrTime on both tutors.

The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems

We extend our previous neural dynamic models of visual search and scene memory (Grieben et al. (2020); Grieben and Schöner (2021)) to move beyond classical “laboratory” stimuli. The new model can autonomously explore a natural scene and build a scene memory of recognized objects and their locations. It is also capable of guided visual search for object categories in the scene. This is achieved by learning object templates for object recognition, and feature guidance templates for visual search and associating them to categorical concepts. We address how preattentive shape can be extracted from the visual input and how scene guidance, specifically, scene gramar (Võ, 2021), emerge. For the first time, we embed feature extraction by a headless deep convolutional neural network (CNN) in a neural dynamic (DFT) architecture by learning a mapping from the distributed feature representation of the CNN to the localist representation of a dynamic neural field.

Bridging D and DNNs: A neural dynamic process model of scene representation, guided visual search and scene grammar in natural scenes

Children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al. (2019) examined whether deep neural networks (DNNs) show a shape or texture bias by constructing images with conflicting shape and texture cues. They found that DNNs strongly preferred to classify familiar objects based on texture as opposed to shape. However, there are a number of differences between how the networks were tested in this study versus how children are typically tested. In this work, we re-examine the inductive biases of neural networks by adapting the stimuli and procedure from Geirhos et al. (2019) to more closely follow the developmental paradigm and test on a range of neural networks. We find that DNNs exhibit a preference for shape rather than texture when tested under conditions that more closely replicate the developmental procedure.

A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines

Modern Hopfield networks (HNs) exhibit properties of a Content Addressable Memory (CAM) that can store and retrieve a large number of memories. They also provide a basis for modelling associative memory in humans. However, the implementation of these networks is often not biologically plausible as they assume the strengths of synaptic connections are symmetric, and utilize functions that rely on many-body synapses. More biologically realistic versions of Modern HNs have been proposed, although these implementations often still utilize the softmax function. Computing the softmax for a single node requires the knowledge of all other neurons, and thus still poses a degree of biological implausibility. We present a Modern HN that uses a version of softmax that can be computed in a more bio-realistic way, and hence moves us closer to a model of memory that is biologically sound. We also show that our proposed network can learn the connection weights using a local learning rule, derived from gradient descent on the energy function. Finally, we verify that our proposed biological network behaves like a Modern HN and explore some of its other interesting properties.

Biological Somax: Demonstrated in Modern Hopfield Networks

We present a new cognitive architecture that combines two neurobiologically-plausible computational elements: (1) a variant of predictive processing known as neural generative coding and (2) hyperdimensional / vector-symbolic models of human memory. We draw inspiration from well-known cognitive architectures such as ACT-R, Soar, Leabra, and Spaun/Nengo. Our cognitive architecture, the COGnitive Neural GENerative system (CogNGen), is in broad agreement with these architectures, but provides a level of detail between ACT-R’s high-level, symbolic description of human cognition and Spaun’s low-level neurobiological description. CogNGen creates the groundwork for developing agents that learn from diverse tasks and model human performance at larger scales than what is possible with existent cognitive architectures. We test CogNGen on a set of maze-learning tasks, including mazes that test short-term memory and planning, and find that the synergy between its predictive processing and vector-symbolic components allow it to master the maze tasks.

CogNGen: Building the Kernel for a Hyperdimensional Predictive Processing Cognitive Architecture

Global Workspace Theory (GWT) is a prominent account of cognitive access in humans. In the decades since its proposal, there have been a number of computational models developed to study the hypothetical dynamics of the global workspace, most of which are hand-designed to reflect the expectations of the theory. Here we examine a recently successful general deep learning architecture, the Perceiver, as a potential theoretical candidate for the global workspace. We find that despite being developed in an unrelated context, the Perceiver meets a number of theoretical requirements of the global workspace. More importantly, it demonstrates empirical behavior consistent with that expected by GWT in both attentional control and working memory tasks drawn from the cognitive science literature. Taken together, this evidence suggests that the Perceiver and related models may be a useful tool for studying the global workspace and its potential realization in both artificial and biological agents.

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Alignment of Knowing Versus Feeling the Emotion in Music During Middle-Childhood

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