Canada

We examined how rhythmic activities affect children’s perspective-taking in a referential communication task with 69 Chinese 5- to 6-year-old children. The child first played an instrument with a virtual partner in one of three coordination conditions: synchrony, asynchrony, and antiphase synchrony. Eye movements were then monitored with the partner giving instructions to identify a shape referent which included a pre-nominal scalar adjective (e.g., big cubic block). Participants with awareness of their partner’s perspective could, in principle, identify the intended referent before the shape was named when the target contrast (a small cubic block) was in shared ground whereas a competitor contrast was occluded for the partner. Children in the asynchrony and antiphase synchrony conditions, but not the synchrony condition, showed anticipatory looks to the target, suggesting that playing instruments asynchronously or in alternation facilitates perspective-taking, likely by training self-other discrimination and inhibitory control.

CogSci 2022

Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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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. 





poster

Explainable Artificial Intelligence (XAI) provides human understandable explanations into how AI systems make decisions in order to increase transparency. We explore how transparency levels in XAI influence perceptions of fairness, trust and understanding, as well as attitudes towards AI use. The transparency levels – no explanation, opaque, simple and detailed - were varied in two contexts - treatment prioritization and recidivism forecasting. In eight experimental groups, 573 participants judged these explanations. As predicted opaque explanations decreased trust and understanding, but surprisingly simple explanations that were more limited in the information they provided had stronger effects on trust and understanding than detailed explanations. Transparency levels did not have an impact on perceptions of fairness and attitudes towards AI, but context did, with the recidivism AI being perceived as less fair. The findings are discussed in relation to information overload and task subjectivity vs objectivity.

Explanations that backfire: Explainable artificial intelligence can cause information overload

As conversational agents become more human-like, people expect them to be engaging as well. However, developing agents that comprehend human desires and generate appropriate responses, continues to be a challenge. We, therefore, collected 2,300 human open-domain dialogs with self-labeled psychological variables such as empathy, connectedness, respect, and friendliness. We found that participants who talk coherently and disclose self-relevant information were engaging partners. Also, we found that various empathetic responses were critical for sincere interaction: agreement, perspective-taking, referring to someone as adorable, and asking questions. When comparing the most and least engaging dialogs, linguistic cues and length of sentences denoted different extents of perceived empathy and sincerity by the partner. Also, we found that a large language model, GPT-3, makes small talks in one shot, but it cannot generate many empathic expressions or sustain a lengthy conversation. We propose a new approach for enhancing conversational agents' social and engaging characteristics.

“Feels Like I’ve Known You Forever”: Empathy and Self-Awareness in Human Open-Domain Dialogs

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.

Modelling Competitive Human Action using Dynamical Motor Primitives for the Development of Human-Like Artificial Agents

Human communication comprises a complex and dynamic interplay of verbal and nonverbal communication channels. The investigation of these channels therefore represents a major methodological challenge.

Technical developments in interaction platforms using virtual characters provide tools for these investigations. Paradigms in which participants interact with algorithmically controlled agents have already enabled the investigation of individual nonverbal communication channels with the necessary experimental control. However, it is unclear how these results relate to human-human communication.

Here, we present a study with a new system for human-human interactions mediated by avatars. As a proof-of-concept, we tested the generalisability of gaze patterns during turn-taking in avatar-mediated conversations. Results show that given our Bayesian mixed effects model, priors and data, there is compelling evidence that gaze patterns are comparable to natural interactions. Exploratory analyses show that our system is suitable to shed light on variability in individual-specific gaze behaviour, which we plan to investigate further.

The relation between gaze and turn-taking in dyadic avatar-mediated conversations

Dopaminergic reward prediction error neurons in the midbrain are the most prominent type of neurons encoding rewards. To explain the coding properties of these neurons, we apply the efficient coding framework to derive how neurons should encode rewards to maximize efficiency. The optimal populations qualitatively explain two recently made observations about real reward prediction error neurons: First, reward prediction error neurons represent rewards relative to a range of quantiles of the expected reward distribution, not relative to a single value. Second, the tuning of these neurons is asymmetric around their base firing rate and the asymmetry of each neuron is related to its threshold quantile. Furthermore, we achieve a good quantitative agreement with the neuronal recordings that were recently used to establish distributional reinforcement learning as a mechanistic explanation for these observations. Our analyses suggest the new interpretation that reward prediction error neurons might efficiently encode reward. Furthermore, it establishes an interesting theoretical link to the sensory processing literature, where efficient coding principles were developed.

Reward Prediction Error Neurons Implement an Efficient Code for Reward

Hippocampus (HPC) is a key structure in category learning, forming organized concepts from individual episodes to adaptively represent shared and distinct elements. Most of the evidence for the role of HPC in concept formation, however, relies on neural coding measured at the end of learning. Thus, it remains unclear how neural mechanisms early in learning support the formation of flexible knowledge. We explored this by combining human functional MRI with a computational model of learning, SUSTAIN. Participants learned to categorize complex visual objects with multiple features. A multidimensional rule linked features to categories with exception items in each category. We observed distinct HPC engagement along its primary axis for exception learning that also predicted learning outcomes. The model predicted that better learners use exceptions to update their knowledge, which was reflected in HPC activation. These findings suggest that HPC discriminates initial informative experiences during learning to support flexible knowledge formation.

Hippocampus Engagement Early in Learning Supports Concept Formation

Inhibition-of-return (IOR) is demonstrated by longer response times (RTs) for stimuli presented at previously attended locations than unattended locations. While facilitatory cueing effects occur at short stimulus onset asynchronies (SOAs) between a cue and target, IOR emerges at later SOAs. Frontoparietal areas including posterior parietal cortex have been implicated in producing IOR, but the contribution of primary motor cortex (M1) is unclear. 
In the present study, participants executed key-press responses to peripheral targets following a cue presented at the same or opposite location at different SOAs (100/300/600/1000ms). Single-pulse transcranial magnetic stimulation (TMS) was applied over right M1 on 50% of trials in a randomized (Experiment 1) or blocked (Experiment 2) order.
IOR appeared to be attenuated by TMS, although RT profiles differed between experiments. Motor-evoked potentials were not altered by the cue-target relationship in either experiment. These findings do not support a key role of M1 in mechanisms of IOR.

The role of primary motor cortex in inhibition of return: An investigation using transcranial magnetic stimulation

Navigation is a fundamental cognitive function of virtually all
moving animals. Several navigation strategies require an
estimate of the current travelling direction that is updated
continuously. In the central complex of the insect brain,
multimodal cues are fused into a compass-like head direction
representation. Based on the proposed connectivity of
columnar neurons in the central complex of the desert locust
we designed a computational model to examine how these
neurons could maintain a stable representation of heading
direction and how shifts occur by optic flow signals when the
animal turns. Our proposed model architecture shows that the
activity of head direction-encoding CL1a neurons remains
stable if the activity of a second class of columnar neurons,
CL2, is exactly the same. Shifts occur via modulation of the
network connectivity. Our model can be used to deduce
testable hypotheses where data are lacking, inspiring new
avenues of experimental investigations.

A Model for Optic Flow Integration in Locust Central-Complex Neurons Tuned to Head Direction

Speech errors are often perceived as categorical substitutions of one sound for another, but phonetic analyses have consistently revealed that errorful productions retain a phonetic trace of the target category. These trace effects have been taken as evidence for the simultaneous activation of multiple categories, both exerting influence on speech production. We develop a dynamic neural field model of voice onset time (VOT) planning, showing how multiple activated categories can be resolved in the field to show trace effects. We evaluate model predictions against measurements of VOT for voiced and voiceless stops in speech error experiments and naturalistic corpora.


A dynamic neural field model of phonetic trace effects in speech errors

In the debate between single-route and dual-route models of verb inflection, the dissociation between regular and irregular verbs in the non-fluent variety of aphasia has been a key sticking point for the proponents of the single-route model. This paper adopts a state-of-the-art neural model which has previously been used to learn inflectional morphology, and shows that it can also be used to model data from non-fluent aphasia. This challenges the assumption that a dual-route model is necessary to capture apparent dissociations in aphasia data and encourages a reanalysis of the deficits involved in non-fluent aphasia.


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Explanations that backfire: Explainable artificial intelligence can cause information overload

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