Testimonial Injustice and Mindreading

Hypatia ◽  
2016 ◽  
Vol 31 (4) ◽  
pp. 858-873 ◽  
Author(s):  
Krista Hyde

Miranda Fricker maintains that testimonial responsibility is the proper corrective to testimonial injustice. She proposes a perceptual‐like “testimonial sensibility” to explain the transmission of knowledge through testimony. This sensibility is the means by which a hearer perceives an interlocutor's credibility level. When prejudice causes a hearer to inappropriately deflate the credibility attributed to a speaker, the sensibility may have functioned unreliably. Testimonial responsibility, she claims, will make the capacity reliable by reinflating credibility levels to their proper degree. I argue that testimonial sensitivity may be or involve “mindreading,” the cognitive capacity by which we predict human behavior and explain it in terms of mental states. Further, I claim that, if testimonial sensibility is or involves mindreading, and mindreading is a function of brain processes (as claimed by cognitive neuroscientists), testimonial injustice cannot be corrected by testimonial responsibility. This is because 1) it appears to rely on conscious awareness of prejudice, whereas much bias occurs implicitly, and 2) it works at the individual level, whereas testimonial injustice occurs both individually and socially. I argue that the remedy for testimonial injustice is, instead, engaging in social efforts that work below the level of consciousness.

2014 ◽  
Vol 7 (2) ◽  
Author(s):  
Giuseppe Boccignone ◽  
Mario Ferraro ◽  
Sofia Crespi ◽  
Carlo Robino ◽  
Claudio De’Sperati

Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. Here we applied machine learning to detect expertise from the oculomotor behavior of novice and expert billiard players during free viewing of a filmed billiard match with no specific task, and in a dynamic trajectory prediction task involving ad-hoc, occluded billiard shots. We have adopted a ground framework for feature space fusion and a Bayesian sparse classifier, namely, a Relevance Vector Machine. By testing different combinations of simple oculomotor features (gaze shifts amplitude and direction, and fixation duration), we could classify on an individual basis which group - novice or expert - the observers belonged to with an accuracy of 82% and 87%, respectively for the match and the shots. These results provide evidence that, at least in the particular domain of billiard sport, a signature of expertise is hidden in very basic aspects of oculomotor behavior, and that expertise can be detected at the individual level both with ad-hoc testing conditions and under naturalistic conditions - and suitable data mining. Our procedure paves the way for the development of a test for the “expert’s eye”, and promotes the use of eye movements as an additional signal source in Brain-Computer-Interface (BCI) systems.


2009 ◽  
Vol 2 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Arie T. Greenleaf ◽  
Joseph M. Williams

The entrenched intrapsychic perspective that currently dominates the counseling professions does not philosophically support social justice advocacy. Because an intrapsychic approach to counseling focuses almost exclusively on change at the individual level, interventions to change an oppressive environment are routinely ignored. Thus, this manuscript presents the argument that a paradigm shift towards an ecological perspective, one that recognizes human behavior as a function of person-environment interaction, is necessary to provide practitioners a clear rationale to engage in social justice advocacy in counseling.


2018 ◽  
Author(s):  
Thibaud Griessinger ◽  
Giorgio Coricelli ◽  
Mehdi Khamassi

ABSTRACTSocial interactions rely on our ability to learn and adjust our behavior to the behavior of others. Strategic games provide a useful framework to study the cognitive processes involved in the formation of beliefs about the others’ intentions and behavior, what we may call strategic theory of mind. Through the years, the growing field of behavioral economics provided evidence of a systematic departure of human’s behavior from the optimal game theoretical prescriptions. One hypothesis posits that human’s ability to accurately process the other’s behavior is somehow bounded. The question of what constraints the formation of sufficiently high order beliefs remained unanswered. We hypothesize that maximizing final earnings in a competitive repeated game setting, requires moving away from reward-based learning to engage in sophisticated belief-based learning. Overcoming the attraction of the immediate rewards by displaying a computationally costly type of learning might not be a strategy shared among all individuals. In this work, we manipulated the reward structure of the interaction so that the action displayed by the two types of learning becomes (respectively not) discriminable, giving a relative strategic (resp. dis) advantage to the participant given the role endorsed during the interaction. We employed a computational modeling approach to characterize the individual level of belief learning sophistication in three types of interactions (agent-agent, human-human and human-agent). The analysis of the participants’ choice behavior revealed that the strategic learning level drives the formation of more accurate beliefs and eventually leads to convergence towards game optimality (equilibrium). More specifically we show that the game structure interacts with the level of engagement in strategically sophisticated learning to explain the outcome of the interaction. This study provides the first evidence of a key implication of strategic learning heterogeneity in equilibrium departure and provides insight to explain the emergence of a leader-follower dynamics of choice.AUTHOR SUMMARYDynamic interaction between individuals appears to be a cornerstone for understanding how humans grasp other minds. During a strategic interaction, in which the outcome of one’s action depends directly on what the other individual decides, it appears crucial to anticipate the other’s actions in order to adjust our own behavior. In theory, choosing optimally in a strategic setting requires that both players hold correct beliefs over their opponent’s behavior and best-respond to it. However, in practice humans systematically deviate from the game-theoretical (equilibrium), suggesting that our ability to form accurate beliefs is cognitively and/or contextually constrained. Previous studies using computational modelling suggested that during a repeated game interaction humans vary in the sophistication of their learning process leading to the formation of beliefs over their opponent’s behavior of different orders of complexity (level of recursive thinking such as “I think that you think that …”). In this work we show that the individual engagement in sophisticated (belief-based) learning drives the convergence towards equilibrium and ultimately performance. Moreover, we show that this effect is influenced by both the game environment and the cognitive capacity of the participants, shaping the very dynamic of the social interaction.DATA AVAILABILITYThe authors confirm that upon publication the raw behavioral data and Matlab code for reconstruction of all figures, computational models and statistical analyses will be made available for download at the following URL: https://zenodo.org/


2018 ◽  
Author(s):  
Helen Weng ◽  
Jarrod Lewis-Peacock ◽  
Frederick Hecht ◽  
Melina Uncapher ◽  
David Ziegler ◽  
...  

Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, N = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.


2019 ◽  
Author(s):  
Tuong-Van Vu ◽  
Catrin Finkenauer ◽  
Lydia Krabbendam

How efficiently one can take the perspective of another might be influenced by individualism (I) and collectivism (C), characterized by whether the self is construed as independent or interdependent. Collectivism can be associated with more accurate and faster inference of others’ mental states because of heightened attention to others’ perspective (the attentional hypothesis). However, construing the self as a separate entity from others, as in individualistic self-construal, could lead to better distinction between one’s and another’s mental states because egocentric bias (the tendency to conflate what oneself sees with what another sees) is better mitigated (the representational hypothesis). We measured IC on an individual level (Individual IC), primed participants (N = 142) with either I or C (Situational IC) and assessed their perspective-taking performance with the Director task. Participants primed with collectivism were significantly faster than the control group but not faster than those primed with individualism. On the individual level, being predominantly collectivistic did not lead to faster perspective taking, but was associated with slower non-perspective-taking performance. These findings provide more support for the attentional hypothesis than the representational hypothesis and improve our understanding of how the representation of the self can contribution to the representation of others’ mind.


2018 ◽  
Author(s):  
H.Y. Weng ◽  
J.A. Lewis-Peacock ◽  
F.M. Hecht ◽  
M.R. Uncapher ◽  
D.A. Ziegler ◽  
...  

AbstractMeditation practices are used to cultivate internally-oriented attention to bodily sensations, which may improve health via cognitive and emotion regulation of bodily signals. However, it remains unclear how meditation impacts internal attention states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to (1) learn and recognize internal attentional (IA) states relevant for meditation during a directed IA task, and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N=16), we first used MVPA to develop single-subject brain classifiers for 5 modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states (i.e., meditation-related states: breath attention, mind wandering, and self-referential processing, and control states: attention to feet and sounds). Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all block volumes were tested, N=2160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were then applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, mind wandering, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to mind wandering or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.


2020 ◽  
Vol 51 (3) ◽  
pp. 183-198
Author(s):  
Wiktor Soral ◽  
Mirosław Kofta

Abstract. The importance of various trait dimensions explaining positive global self-esteem has been the subject of numerous studies. While some have provided support for the importance of agency, others have highlighted the importance of communion. This discrepancy can be explained, if one takes into account that people define and value their self both in individual and in collective terms. Two studies ( N = 367 and N = 263) examined the extent to which competence (an aspect of agency), morality, and sociability (the aspects of communion) promote high self-esteem at the individual and the collective level. In both studies, competence was the strongest predictor of self-esteem at the individual level, whereas morality was the strongest predictor of self-esteem at the collective level.


2019 ◽  
Vol 37 (1) ◽  
pp. 18-34
Author(s):  
Edward C. Warburton

This essay considers metonymy in dance from the perspective of cognitive science. My goal is to unpack the roles of metaphor and metonymy in dance thought and action: how do they arise, how are they understood, how are they to be explained, and in what ways do they determine a person's doing of dance? The premise of this essay is that language matters at the cultural level and can be determinative at the individual level. I contend that some figures of speech, especially metonymic labels like ‘bunhead’, can not only discourage but dehumanize young dancers, treating them not as subjects who dance but as objects to be danced. The use of metonymy to sort young dancers may undermine the development of healthy self-image, impede strong identity formation, and retard creative-artistic development. The paper concludes with a discussion of the influence of metonymy in dance and implications for dance educators.


Author(s):  
Pauline Oustric ◽  
Kristine Beaulieu ◽  
Nuno Casanova ◽  
Francois Husson ◽  
Catherine Gibbons ◽  
...  

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