Too much experience: A desensitization bias in emotional perspective taking.

Campbell, Troy; O’Brien, Ed; Boven, Leaf Van; Schwarz, Robert; Ubel, Peter A.

doi:10.1037/a0035148

Cited by 64 publications

(46 citation statements)

References 54 publications

(69 reference statements)

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“…The temporal difference error is correlated with dopamine signaling in the brain [79] on the one hand, and a correlation between dopamine signaling and euphoria exists on the other [80]. Joy reactions habituate upon repeated exposure to jokes [81]. The temporal difference signal for a particular situation habituates upon repeated exposure [2].…”

Section: Discussionmentioning

confidence: 99%

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

Broekens

Chétouani

2021

IEEE Trans. Affective Comput.

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Robots and virtual agents need to adapt existing and learn novel behavior to function autonomously in our society. Robot learning is often in interaction with or in the vicinity of humans. As a result the learning process needs to be transparent to humans. Reinforcement Learning (RL) has been used successfully for robot task learning. However, this learning process is often not transparent to the users. This results in a lack of understanding of what the robot is trying to do and why. The lack of transparency will directly impact robot learning. The expression of emotion is used by humans and other animals to signal information about the internal state of the individual in a language-independent, and even species-independent way, also during learning and exploration. In this article we argue that simulation and subsequent expression of emotion should be used to make the learning process of robots more transparent. We propose that the TDRL Theory of Emotion gives sufficient structure on how to develop such an emotionally expressive learning robot. Finally, we argue that next to such a generic model of RL-based emotion simulation we need personalized emotion interpretation for robots to better cope with individual expressive differences of users.

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Section: Discussionmentioning

confidence: 99%

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

Broekens

Chétouani

2021

IEEE Trans. Affective Comput.

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“…For example, people overestimate how much others share their preferences and attitudes (Epley et al 2009;Irmak, Vallen, and Sen 2010). Problematically, people do not fully adjust from their personal anchor and fail to take into account situational and idiosyncratic differences between themselves and others (Campbell et al 2014;Van Boven et al 2013), leading to inaccurate predictions. Moreover, egocentrism is largely resilient to debiasing efforts (Eyal, Steffel, and Epley (2018).…”

Section: Egocentrismmentioning

confidence: 99%

The Effect of Egocentric Taste Judgments on Stereotyping of Welfare Recipients and Attitudes Toward Welfare Policy

Shepherd¹,

Campbell²

2019

Journal of Public Policy & Marketing

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Despite the centrality of purchasing behavior and choice to the welfare debate, research has generally understood attitudes toward welfare at a broader level and as a function of rational or deliberative processes (e.g., self-interest, ideology). This project identifies the effect of egocentrism on welfare attitudes, finding that a welfare recipient’s purchase of an item that the participant personally values less (vs. more) leads to increased stereotyping of welfare recipients (e.g., irresponsibility, impulsivity) and favorable attitudes toward policy that would restrict that purchase. This effect is illustrated for both chronic and situational preferences and across a number of products commonly debated in welfare policy. The authors find that egocentrism is robust to debiasing; therefore, tests of boundary conditions involved countering the stereotype of irresponsibility rather than the bias itself. For example, the effects do not emerge in the context of healthy foods and necessities, nor when information suggests that the target consumer is otherwise responsible (e.g., budgeting, clipping coupons). Implications for policy and welfare advocacy are discussed. In general, these findings establish how personal preferences may shape attitudes toward marginalized consumers and related policy.

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“…Hence, it's not surprising when unknowingly various cognitive bias develops. These mental shortcuts are coping mechanisms to enable medical staff to keep up with an ever-increasing workload [35]. But this can work counter productively when cognitive biases develop [33].…”

Section: Need For Cam In Neonatal Radiologymentioning

confidence: 99%

A Deep Learning Based Suggested Model to Detect Necrotising Enterocolitis in Abdominal Radiography Images

Druten

Sharif

Chan

et al. 2019

2019 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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Despite decades of exploration into necrotising enterocolitis (NEC), we still lack the capacity to accurately diagnose the disease to improve outcomes in its management. Existing diagnostics struggle to delineate NEC from other neonatal intestinal diseases; it is also unable to highlight those likely to deteriorate to needing emergency life-saving surgery before it is too late. The diagnosis of NEC is heavily dependent on interpretation of radiological findings, especially abdominal radiography (AR) and abdominal ultrasound (AUS). Interexpert variability in interpreting AR imaging, and in the case of AUS, performing and interpreting the test, remains an unresolved challenge. With the compounding impact of the shrinking radiology workforce, a novel approach is imperative. Computer assisted detection (CAD) and classification of abnormal pathology in medical imaging is a rapidly evolving field of clinical and biomedical research. This technology is widely used as a preliminary screening tool. This research paper proposes a deep learning-based model to classify AR images in an automated manner, generating class activation maps (CAM) from various imaging features consistent with NEC pathology, as agreed by expert consensus papers (in neonatology and paediatric radiology). It also compares it with conventional machine learning methods. The suggested model aims to produce heatmaps for various imaging features to highlight NEC pathology in AR (or in future AUS). Once the model is trained, validation is done through quantitative measures and visually by the attending radiologist (clinician) reviewing the validity of the colour maps highlighting the pathology of the AR image (future extension to AUS). As the volume of imaging data is increasing year by year, CAD can be a key strategy to assist radiology departments meet service needs. This technology can greatly assist in screening for NEC, improving the detection of NEC and potentially aid in the earlier identification of disease. Furthermore, it can fast track research cost effectively by creating big data through the automatic labeling of imaging data to create big-data for NEC databases.

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Too much experience: A desensitization bias in emotional perspective taking.

Cited by 64 publications

References 54 publications

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

Towards Transparent Robot Learning Through TDRL-Based Emotional Expressions

The Effect of Egocentric Taste Judgments on Stereotyping of Welfare Recipients and Attitudes Toward Welfare Policy

A Deep Learning Based Suggested Model to Detect Necrotising Enterocolitis in Abdominal Radiography Images

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