[PREPRINT] Mind the Machines: Applying Implicit Measures of Mind Perception to Social Robotics

Beyond conscious beliefs and goals, automatic cognitive processes shape our social encounters, and interactions with complex machines like social robots are no exception. With this in mind, it is surprising that research in human-robot interaction (HRI) almost exclusively uses explicit measures, such as subjective ratings and questionnaires, to assess human attitudes towards robots - seemingly ignoring the importance of implicit measures. This is particularly true for research focusing on the question whether or not humans are willing to attribute complex mental states mind perception, such as agency (i.e., the capacity to plan and act) and experience (i.e., the capacity to sense and feel), to robotic agents. In the current study, we (i) created the mind perception implicit association test (MP-IAT) to examine subconscious attributions of mental capacities to agents of different degrees of human-likeness (here: human vs. humanoid robot), and (ii) compared the outcomes of the MP-IAT to explicit mind perception ratings of the same agents.Results indicate that (i) already at the subconscious level, robots are associated with lower levels of agency and experience compared to humans, and that (ii) implicit and explicit measures of mind perception are not significantly correlated. This suggests that mind perception (i) has an implicit component that can be measured using implicit tests like the IAT and (ii) might be difficult to modulate via design or experimental procedures due to its fast-acting, automatic nature.

Different armpits under my new nose: olfactory sex but not gender affects implicit measures of embodiment

Research has shown that conflicting multisensory signals may alter embodiment to the point of self-identifying with a foreign body, but the role of olfaction in this process has been overlooked. Here, we study in healthy participants how sex (male and female sweat odors) and gender (male and female cosmetic scents) olfactory stimuli contribute to embodiment. Participants saw from the perspective of a sex mismatching person in virtual reality and received synchronous visuo-tactile stimulation to elicit illusory embodiment of the seen body while smelling either sex- or gender- congruent stimuli. We assessed implicit (skin conductance responses to visual threats) and explicit (questionnaire) measures of embodiment. Stronger responses to threat were found when participants smelled the sex-congruent compared to the sex-incongruent odor, while no such differences were found for the cosmetic scents. According to the questionnaire, embodiment did not differ between conditions. Post-experimental assessment of the presented cues, suggest that while both sweat odors were considered generally male, cosmetic scents were not. The presented scents were generally not associated to the embodied body. Our results suggest that sex-related body odors influence implicit but not explicit aspects of embodiment and are in line with unique characteristics of olfaction in other aspects of cognition.

The Older, the More Forgiving? Characteristics of Forgiveness of Chinese Older Adults

This study explores the characteristics of forgiveness in the aging cohorts, which is regarded to be associated with healthy outcomes. Data were drawn from a sample of 308 older adults (aged from 60 to 98 years) who completed the forgiveness questionnaire: forgiving others of The Heartland Forgiveness Scale (HFS) to examine explicit forgiveness, and among the participants, 44 older adults were administrated on the variant single category of implicit association test (SC-IAT) to examine the implicit forgiveness. The results revealed that (1) there is no correlation between explicit forgiveness and implicit forgiveness of older adults. (2) The result of explicit forgiveness is relatively high while that of implicit forgiveness is relatively low. (3) There was no significant correlation between explicit forgiveness and age, but there was significant difference between age groups, as forgiveness tendency of the elderly had a trough in the age group of 70–79 and then rebounded. (4) Implicit forgiveness was significantly correlated with age, and the difference between age groups was marginal. The forgiveness tendency of the elderly over 80 years old was significantly higher than that of the other two age groups. (5) Gender differences are found in both explicit and implicit forgiveness. The findings indicated that (1) explicit and implicit measures in this study have assessed independent and complementary aspects of forgiveness tendency in older adults. (2) Implicit forgiveness falls behind explicit forgiveness, and true internal forgiveness is difficult and rare in older adults according to data analysis. (3) The trend of explicit forgiveness with age is not obvious, because explicit forgiveness in the middle old age group presents an inflection point. However, implicit forgiveness increases slowly with age. (4) Women excel men in scores obtained with both explicit and implicit measures for forgiveness.

Mixing the implicit: A Linear Mixed-Effects Models approach for a Rasch analysis of the Implicit Association Test and the Single Category Implicit Association Test

<div>The indirect investigation of psychological constructs has become prominent in social sciences thanks to the so-called implicit measures. Different implicit measures can be administered concurrently to the same respondents for obtaining detailed and multifaceted information on the constructs of interest. In this study, a Rasch analysis of accuracy and time responses of two commonly used implicit measures is presented. The focus in on the concurrent administration of the Implicit Association Test (IAT; Greenwald et al., 1998) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006). Linear Mixed-Effects Models are used to address the within– and between–measures sources of variability and to obtain a Rasch parametrization of the data. By disentangling the respondent’s contribution from the stimulus contribution to the observed responses, these models allow for delving deeper on the functioning of the IAT and the SC-IAT, as well as for grasping a better understanding of the processes driving a behavioral decision. Implications of the results for social sciences and future research directions are discussed.</div>

EXPRESS: Emotion perception bias associated with the hijab in Austrian and Turkish participants

In a cross-cultural study, we investigated the link between explicit attitudes towards the hijab, and implicit measures of cultural and religious bias during the recognition of emotions. Participants tested in Austria (N = 71), and in Turkey (N = 70) reported their attitude towards the hijab, and categorised in a mousetracker task happy and sad faces of women, shown with five levels of intensity, and framed either by a hijab or by an oval-shaped mask. The two samples did not differ in their explicit attitudes towards the hijab. However, negative attitude towards the hijab predicted greater sadness attribution to happy faces with the hijab in Austrian participants. Unrelated to their explicit attitudes, Turkish participants attributed more sadness to happy faces with than without the hijab. Results suggest that the sight of the hijab activated, in both Austrian and Turkish participants, implicit biases resulting in associations with sadness and negative emotions.

Mixing the implicit: A Linear Mixed-Effects Models approach for a Rasch analysis of the Implicit Association Test and the Single Category Implicit Association Test

<div>The indirect investigation of psychological constructs has become prominent in social sciences thanks to the so-called implicit measures. Different implicit measures can be administered concurrently to the same respondents for obtaining detailed and multifaceted information on the constructs of interest. In this study, a Rasch analysis of accuracy and time responses of two commonly used implicit measures is presented. The focus in on the concurrent administration of the Implicit Association Test (IAT; Greenwald et al., 1998) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006). Linear Mixed-Effects Models are used to address the within– and between–measures sources of variability and to obtain a Rasch parametrization of the data. By disentangling the respondent’s contribution from the stimulus contribution to the observed responses, these models allow for delving deeper on the functioning of the IAT and the SC-IAT, as well as for grasping a better understanding of the processes driving a behavioral decision. Implications of the results for social sciences and future research directions are discussed.</div>

Persuading the Implicit Mind: Changing Negative Implicit Evaluations With an 8-Minute Podcast

Implicit evaluations can be malleable via reinterpretation of previously encountered evidence. Here, we report three studies ( N = 1,007) investigating the robustness of this updating modality using ecologically realistic materials. Participants were first introduced to a target who killed an endangered black rhino in Namibia. They then listened to a real podcast providing counterattitudinal information on the benefits of trophy hunting. The podcast resulted in considerable revisions of initially negative implicit evaluations toward positivity, consistently across implicit measures (affect misattribution procedures vs. implicit association test), samples (American students vs. nonstudents from various countries), study settings (lab vs. online), and the presence versus absence of a memory retrieval manipulation prompting reflection on participants’ views on trophy hunting. Taken together, these findings suggest that reinterpretation can shift implicit evaluations of even highly negative targets, including under conditions of external validity.

The Episodic Prototypes Model (EPM): On the nature and genesis of facial representations

Faces undergo massive changes over time and life events. We need a mental representation which is flexible enough to cope with the existing visual varieties, but which is also stable enough to be the basis for valid recognition. Two main theoretical frameworks exist to describe facial representations: prototype models assuming one central item comprising all visual experiences of a face, and exemplar models assuming single representations of each visual experience of a face. We introduce a much more ecological valid model dealing with episodic prototypes (the Episodic Prototypes Model—EPM), where faces are represented by a low number of prototypes that refer to specific Episodes of Life (EoL, e.g., early adulthood, mature age) during which the facial appearance shows only moderate variation. Such an episodic view of mental representation allows for efficient storage, as the number of needed prototypes is relatively low, and it allows for the needed variation within a prototype that keeps the everyday and steadily ongoing changes across a certain period of time. Studies 1–3 provide evidence that facial representations are highly dependent on temporal aspects which is in accord with EoL, and that individual learning history generates the structure and content of respective prototypes. In Study 4, we used implicit measures (RT) in a face verification task to investigate the postulated power of the EPM. We could demonstrate that episodic prototypes clearly outperformed visual depictions of exhaustive prototypes, supporting the general idea of our approach.