Avoidance of Novelty Contributes to the Uncanny Valley

Abstract

A hypothesis suggests that objects with a high degree of visual similarity to real humans trigger negative impressions (i.e., the uncanny valley). Previous studies have suggested that difficulty in object categorization elicits negative emotional reactions to enable the avoidance of potential threats. The present study further investigated this categorization-difficulty hypothesis. In an experiment, observers categorized morphed images of photographs and human doll faces as “photograph” or “doll” and evaluated the perceived eeriness of the images. Additionally, we asked the observers to answer questionnaires on behavioral inhibition systems (BIS). The results indicated that individual differences in the BIS score were associated with enhanced eeriness in the objects with a specific human likeness. These findings suggest that the tendency to avoid a potentially threatening novel experience contributes to promoting the perceived eeriness of objects with some degree of visual similarity to real humans.

Creepy cats and strange high houses: Support for configural processing in testing predictions of nine uncanny valley theories

“…Novelty avoidance theories predict that the UV effect is elicited by stimuli that do not belong to an established category ( Sasaki, Ihaya, & Yamada, 2017 ). One such theory states that categorizing an exemplar into a novel category produces an aversive response.…”

Virtual Faces Evoke Only a Weak Uncanny Valley Effect: An Empirical Investigation With Controlled Virtual Face Images

“…Image morphing is arguably one of the most commonly applied controlled stimulus generation methods for investigating the UV. This method has been used, for example, to create image continua from artificial to human-like faces where the artificial images are CG faces (Cheetham, Suter, & Jäncke, 2011; MacDorman & Chattopadhyay, 2016), robot faces (Lischetzke et al., 2017; MacDorman & Ishiguro, 2006), cartoon faces (Sasaki, Ihaya, & Yamada, 2017; Yamada et al., 2013), or doll faces (Looser & Wheatley, 2010; Seyama & Nagayama, 2009). Image morphing is a particularly promising method for studying the UV because it allows generating well-controlled continua with several intermediate steps.…”

Crowdsourcing visual perception experiments: a case of contrast threshold

“…Crowdsourcing, however, allows researchers to recruit participants from around the world, and hence, mass data from participants with various personality traits can be collected. Indeed, we and others have already shown the relation between individual differences in personality traits (e.g., social anxiety, behavioral activation/inhibition systems, and mood) and emotional reactions using crowdsourcing (Chaya et al, 2016; Sasaki, Ihaya & Yamada, 2017). Moreover, we previously conducted a perceptual study indicating the age and sex differences in the perception of pattern randomness (Yamada, 2015).…”

“…In the online conditions, the experiment was conducted on a web browser with a JavaScript application (jsPsych; de Leeuw, 2015). jsPsych is a useful toolbox for psychological research, employed in several previous studies (de Leeuw & Motz, 2016; Pinet et al, 2017; Sasaki, Ihaya & Yamada, 2017).…”

“…Various kinds of online experiments and tasks have been conducted with crowdsourcing. For example, many experimental studies have reported findings based on self-report questionnaires (Crangle & Kart, 2015; Garcia et al, 2016; Gottlieb & Lombrozo, 2018; Hurling et al, 2017; Sasaki, Ihaya & Yamada, 2017) and crowdsourced tasks: visual search (de Leeuw & Motz, 2016), reaction time (Nosek, Banaji & Greenwald, 2002; Sasaki, Ihaya & Yamada, 2017; Schubert et al, 2013), keystroke (Pinet et al, 2017), Stroop (Barnhoorn et al, 2015; Crump, McDonnell & Gureckis, 2013; Majima, 2017), attentional blink (Barnhoorn et al, 2015; Brown et al, 2014), flanker (Simcox & Fiez, 2014; Majima, 2017; Zwaan et al, 2018), Simon (Majima, 2017; Zwaan et al, 2018), lexical decision (Simcox & Fiez, 2014), category learning (Crump, McDonnell & Gureckis, 2013), memory (Brown et al, 2014; Zwaan et al, 2018), priming (Zwaan et al, 2018), and decision-making tasks (Berinsky, Huber & Lenz, 2012; Brown et al, 2014). A previous study using auditory stimuli likewise employed crowdsourcing (Woods et al, 2017).…”