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Recognizing Human Internal States: A Conceptor-Based Approach

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Madeleine Bartlett, Daniel Hernandez Garcia, Serge Thill, Tony Belpaeme

Human-Computer Interaction · Submitted 9 Sep 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We report the initial results of this system in a proof-of-concept study and outline plans for future work.

how it is stated · asserted 0.98 read the record →

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arXiv ID1909.04747 (version 1)
Pith ID326MPQPN, Pith's permanent record of this paper and its review
CitationDaniel Hernandez Garcia, Madeleine Bartlett, Serge Thill, Tony Belpaeme. "Recognizing Human Internal States: A Conceptor-Based Approach." arXiv:1909.04747 (2019). https://pith.science/paper/326MPQPN
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,915 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.61 · human participants 0.85 · controlled comparison 0.04 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.07 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. We report the initial results of this system in a proof-of-concept study and outline plans for future work.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. background 1.00 0.04 0.02 asserted 1.00
s1 We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. contribution 0.37 0.11 0.09 hedged possibility 0.95
s2 The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. background 0.86 0.07 0.04 asserted 0.92
s3 Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? background 0.50 0.16 0.03 asserted 0.84
s4 In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. contribution 0.99 0.79 0.19 asserted 0.99
s5 We report the initial results of this system in a proof-of-concept study and outline plans for future work. future work 0.51 0.89 0.16 asserted 0.98

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from observation or experiment 0.93.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. We report the initial results of this system in a proof-of-concept study and outline plans for future work.

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