Refinements to Indices for Perceived Humanness, Attractiveness, and Eeriness

Using a hypothetical graph, Masahiro Mori proposed in 1970 the relation between the human likeness of robots and other anthropomorphic characters and an observer’s affective or emotional appraisal of them. The relation is positive apart from a U-shaped region known as the uncanny valley. To measure the relation, we previously developed and validated indices for the perceptual-cognitive dimension humanness and three affective dimensions: interpersonal warmth, attractiveness, and eeriness. Nevertheless, the design of these indices was not informed by how the untrained observer perceives anthropomorphic characters categorically. As a result, scatter plots of humanness vs. eeriness show the stimuli cluster tightly into categories that are widely separated from each other. The present study applies a card sorting task, laddering interview, and adjective evaluation (N = 30) to revise the humanness, attractiveness, and eeriness indices and validate them via a representative survey (N =1,311). The revised eeriness index maintains its orthogonality to humanness (r = .04, p = .285), but the stimuli show much greater spread, reflecting the breadth of their range in human likeness and eeriness. The revised indices enable empirical relations among characters to be plotted similarly to Mori’s graph of the uncanny valley. Accurate measurement with these indices can be used to enhance the design of androids and 3D computer-animated characters.

[1]  T. Kanda,et al.  Psychology in human-robot communication: an attempt through investigation of negative attitudes and anxiety toward robots , 2004, RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759).

[2]  L. Jäncke,et al.  Human Neuroscience , 2022 .

[3]  D. Roberson,et al.  Categorical Perception for Unfamiliar Faces , 2010, Psychological science.

[4]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[5]  Tyler J. Burleigh,et al.  A reappraisal of the uncanny valley: categorical perception or frequency-based sensitization? , 2015, Front. Psychol..

[6]  A. Takanishi,et al.  Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner , 2015, Front. Psychol..

[7]  Karl F. MacDorman,et al.  Familiar faces rendered strange: Why inconsistent realism drives characters into the uncanny valley , 2016, Journal of vision.

[8]  Karl F. MacDorman,et al.  Revisiting the uncanny valley theory: Developing and validating an alternative to the Godspeed indices , 2010, Comput. Hum. Behav..

[9]  H. Ishiguro,et al.  Opening Pandora’s Box , 2020, Marriage Equality.

[10]  T. Gärling.  A multidimensional scaling and semantic differential technique study of the perception of environmental settings , 1976 .

[11]  Tatsuya Nomura,et al.  Rapport–Expectation with a Robot Scale , 2016, Int. J. Soc. Robotics.

[12]  H. Arkes.  Costs and benefits of judgment errors: Implications for debiasing. , 1991 .

[13]  S. Harnad.  Category Induction and Representation , 1987 .

[14]  S. Turkle.  Authenticity in the age of digital companions , 2007 .

[15]  C. Macrae,et al.  Social cognition: thinking categorically about others. , 2000, Annual review of psychology.

[16]  T. Wheatley,et al.  The Tipping Point of Animacy , 2010, Psychological science.

[17]  Marek P. Michalowski,et al.  A spatial model of engagement for a social robot , 2006, 9th IEEE International Workshop on Advanced Motion Control, 2006..

[18]  J. Kätsyri,et al.  A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness , 2015, Front. Psychol..

[19]  Matthias Scheutz,et al.  A mismatch in the human realism of face and voice produces an uncanny valley , 2011, i-Perception.

[20]  Wendy A. Rogers,et al.  Why Some Humanoid Faces Are Perceived More Positively Than Others: Effects of Human-Likeness and Task , 2014, International Journal of Social Robotics.

[21]  Marcus Cheetham,et al.  Category Processing and the human likeness dimension of the Uncanny Valley Hypothesis: Eye-Tracking Data , 2013, Front. Psychol..

[22]  Naomi H. Feldman,et al.  The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference. , 2009, Psychological review.

[23]  P. Bentler.  Semantic Space is (Approximately) Bipolar , 1969 .

[24]  M. Lorr,et al.  A semantic differential mood scale. , 1988, Journal of Clinical Psychology.

[25]  Bruce Mangan.  The uncanny valley as fringe experience , 2015 .

[26]  Karl F. MacDorman,et al.  Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures , 2008, AI & SOCIETY.

[27]  Bertrand Tondu,et al.  A New Interpretation of mori's Uncanny Valley for Future Humanoid Robots , 2011, Int. J. Robotics Autom..

[28]  Takashi Yamauchi,et al.  Labeling bias and categorical induction: generative aspects of category information. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[29]  Roger K. Moore.  A Bayesian explanation of the ‘Uncanny Valley’ effect and related psychological phenomena , 2012, Scientific Reports.

[30]  D. Gerbing,et al.  Viability of exploratory factor analysis as a precursor to confirmatory factor analysis , 1996 .

[31]  Roger K. Moore,et al.  The Uncanny Valley: A Focus on Misaligned Cues , 2014, ICSR.

[32]  Kerstin Dautenhahn,et al.  An Interactive Game with a Robot: Peoples' Perceptions of Robot Faces and a Gesture-Based User Interface , 2013, ACHI 2013.

[33]  Yuki Yamada,et al.  Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon , 2013 .

[34]  Wynne W. Chin,et al.  On the use, usefulness, and ease of use of structural equation modeling in MIS research: a note of caution , 1995 .

[35]  R. Keen.  Why People Fail to Recognize Their Own Incompetence , 2010 .

[36]  Steven O. Entezari,et al.  Individual differences predict sensitivity to the uncanny valley , 2015 .

[37]  Aaron Powers,et al.  Matching robot appearance and behavior to tasks to improve human-robot cooperation , 2003, The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003..

[38]  Henk A. L. Kiers,et al.  Why Factor Analysis Often is the Incorrect Model for Analyzing Bipolar Concepts, and What Model to Use Instead , 1994 .

[39]  Andreas Herrmann,et al.  The influence of stimulus ambiguity on category and attitude formation , 2010 .

[40]  Evgenios Vlachos,et al.  An open-ended approach to evaluating Android faces , 2015, 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[41]  Maya B. Mathur,et al.  Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley , 2016, Cognition.

[42]  C. Nelson,et al.  Categorical representation of facial expressions by 7-month-old infants. , 1988 .

[43]  Gordon Rugg,et al.  The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts , 1997, Expert Syst. J. Knowl. Eng..

[44]  K. MacDorman,et al.  Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not , 2016, Cognition.

[45]  J. Kruger,et al.  Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments. , 1999, Journal of personality and social psychology.

[46]  L. Ross,et al.  The Bias Blind Spot: Perceptions of Bias in Self Versus Others , 2002 .

[47]  Dana Kulic,et al.  Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots , 2009, Int. J. Soc. Robotics.

[48]  Hiroshi Kobayashi,et al.  Android Patient Robot Simulating Depressed Patients for Diagnosis Training of Psychiatric Trainees , 2013, 2013 Second International Conference on Robot, Vision and Signal Processing.

[49]  P. S. Vivekananthan,et al.  A multidimensional approach to the structure of personality impressions. , 1968, Journal of personality and social psychology.

[50]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[51]  Peter Robinson,et al.  Empathizing with robots: Fellow feeling along the anthropomorphic spectrum , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[52]  Karl F. MacDorman,et al.  Human emotion and the uncanny valley: A GLM, MDS, and Isomap analysis of robot video ratings , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[53]  Karl F. MacDorman,et al.  Too real for comfort? Uncanny responses to computer generated faces , 2009, Comput. Hum. Behav..

[54]  Jun'ichiro Seyama,et al.  The Uncanny Valley: Effect of Realism on the Impression of Artificial Human Faces , 2007, PRESENCE: Teleoperators and Virtual Environments.

[55]  M. Wedel,et al.  An investigation into the association pattern technique as a quantitative approach to measuring means-end chains , 1998 .

[56]  H. Ishiguro,et al.  EXPLORING THE UNCANNY VALLEY WITH GEMINOID HI-1 IN A REAL-WORLD APPLICATION , 2010 .

[57]  E. Pronin.  Perception and misperception of bias in human judgment , 2007, Trends in Cognitive Sciences.

[58]  Robert T. Clemen,et al.  Subjective Probability Assessment in Decision Analysis: Partition Dependence and Bias Toward the Ignorance Prior , 2005, Manag. Sci..

[59]  M. Mori.  THE UNCANNY VALLEY , 2020, The Monster Theory Reader.

[60]  Catrin Misselhorn,et al.  Empathy with Inanimate Objects and the Uncanny Valley , 2009, Minds and Machines.