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Multimodal behavioral cues analysis of the sense of presence and co-presence during a social interaction with a virtual patient

Magalie Ochs, Jérémie Bousquet, Jean-Marie Pergandi, & Philippe Blache

Frontiers in Computer Science, 2022, 4:746804  @HAL

User’s experience evaluation is a key challenge when studying human-agent interaction. Besides user’s satisfaction, this question is addressed in virtual reality through the sense of presence and social presence, generally assessed thanks to subjective post-experience questionnaires. We propose in this article a novel approach making it possible to evaluate automatically these notions by correlating objective multimodal cues produced by users to their subjective sense of presence and social presence. This study is based on a multimodal human-agent interaction corpus collected in a task-oriented context: a virtual environment aiming at training doctors to break bad news to a patient played by a virtual agent. Based on a corpus study, we applied machine learning approaches to build a model predicting the user’s sense of presence and social presence thanks to specific multimodal behavioral cues. We explore different classification algorithms and machine learning techniques (oversampling and clustering) to cope with the dimensionality of the dataset and to optimize the prediction performance. We obtain models to automatically and accurately predict the level of presence and social presence. The results highlight the relevance of a multimodal model, based both on verbal and non-verbal cues as objective measures of (social) presence. The main contribution of the article is two-fold: 1/ proposing the first presence and social prediction presence models offering a way to automatically provide a user’s experience evaluation and 2/ showing the importance of multimodal information for describing these notions.

Associative symmetry: a divide between humans and nonhumans?

Thomas Chartier & Joël Fagot

Trends in Cognitive Sciences (2022) Volume 26, issue 4, 286-289  @HAL

Anthropocentrism can bias scientific conclusions. As a case study, we challenge the 40-year-old associative symmetry dogma, supposed to cognitively set apart humans from other species. Out of 37 human studies surveyed, only three truly demonstrate symmetry, of which only one (on five participants) suggests that symmetry is spontaneously formed.

Learning Higher‐Order Transitional Probabilities in Nonhuman Primates

Arnaud Rey, Joël Fagot, Fabien Mathy, Laura Lazartigues, Laure Tosatto, Guillem Bonafos, Jean‐Marc Freyermuth, & Frédéric Lavigne

Cognitive Science, 46 (4), 2022, e13121  @HAL

The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the co-occurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus given the first (i.e., p(B|A)). In the present study, nonhuman primates (Guinea baboons, Papio papio) were exposed to a serial version of the XOR (i.e., exclusive-OR), in which they had to process sequences of three stimuli: A, B, and C. In this manipulation, first-order TPs (i.e., AB and BC) were uninformative due to their transitional probabilities being equal to .5 (i.e., p(B|A) = p(C|B) = .5), while second-order TPs were fully predictive of the upcoming stimulus (i.e., p(C|AB) = 1). In Experiment 1, we found that baboons were able to learn second-order TPs, while no learning occurred on first-order TPs. In Experiment 2, this pattern of results was replicated, and a final test ruled out an alternative interpretation in terms of proximity to the reward. These results indicate that a nonhuman primate species can learn a nonlinearly separable problem such as the XOR. They also provide fine-grained empirical data to test models of statistical learning on the interaction between the learning of different orders of TPs. Recent bioinspired models of associative learning are also introduced as promising alternatives to the modeling of statistical learning mechanisms.

Interaction between orthographic and graphomotor constraints in learning to write

Jérémy Danna, Marieke Longcamp, Ladislas Nalborczyk, Jean-Luc Velay, Claire Commengé, & Marianne Jover

Learning and Instruction (80), August 2022, 101622  @HAL

We investigated the combined effects of orthographic and graphomotor constraints as a function of handwriting proficiency in children. Twenty-four first graders, 20 third graders,
and 21 fifth graders wrote single five-letter words in cursive writing on a sheet of paper affixed to a digitizing tablet. The words were chosen according to two orthographic
constraints, namely their lexical frequency and the graphemic complexity of the last three letters, and one graphomotor constraint resulting from the motor difficulty of tracing the first
letter. In addition to massive improvements of handwriting with grade, the results revealed, in the youngest group only, an interaction between first-letter difficulty and lexical frequency.
This finding suggests that, before handwriting movement becomes automated,  the cognitive resources needed for retrieving word spelling interferes with motor processing while writing a
difficult letter. When students start learning to write, they are particularly sensitive to the combination of orthographic and graphomotor constraints.

Chunking mechanisms are central to the acquisition of sequences

Baboons are invited to learn sequences by pointing to a red target circle appearing on a touch screen (Panels A and B). Once the target is touched, it moves to a different position. Baboons repeatedly produce the same sequence of 9 touches for 1000 trials, which are then analyzed as 10 consecutive blocks of 100 trials. Panel C shows mean response times for each position in the sequence in blocks 1, 7, and 10, for one baboon. The sequence is processed in 3 chunks in Block 1 and only 2 in Block 10. Among other results, chunks become fewer and longer with practice.

Tosatto, L., Fagot, J., Nemeth, D., & Rey, A. (2022). The evolution of chunks in sequence learning. Cognitive Science. 46(4), e13124.  @HAL