Dataset Information

The Depression Schema: How Labels, Features, and Causal Explanations Affect Lay Conceptions of Depression.

ABSTRACT: Depression is a common clinical disorder characterized by a complex web of psychological, behavioral, and neurological causes and symptoms. Here we investigate everyday beliefs and attitudes about depression, as well as the factors that shape the depression schemas people hold. In each of three studies, participants read about a person experiencing several symptoms of depression and answered questions about their conception of the disorder. In some cases the symptoms were presented in isolation while in other cases the symptoms were presented with a diagnostic label and/or descriptions of its possible causes (e.g., genes versus personal experience). Results indicated that beliefs and attitudes toward depression were largely shaped by individual difference factors (e.g., personal experience, political ideology) and that the experimental manipulations primarily impacted attributions of responsibility and suggestions for a course of treatment. These findings represent an important advance in our understanding of the factors that influence the folk psychiatry of depression and help inform theories of schema formation for abstract and complex domains.

SUBMITTER: Thibodeau PH

PROVIDER: S-EPMC4648076 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

The Depression Schema: How Labels, Features, and Causal Explanations Affect Lay Conceptions of Depression.

Thibodeau Paul H PH Fein Mira J MJ Goodbody Elizabeth S ES Flusberg Stephen J SJ

Frontiers in psychology 20151117

Depression is a common clinical disorder characterized by a complex web of psychological, behavioral, and neurological causes and symptoms. Here we investigate everyday beliefs and attitudes about depression, as well as the factors that shape the depression schemas people hold. In each of three studies, participants read about a person experiencing several symptoms of depression and answered questions about their conception of the disorder. In some cases the symptoms were presented in isolation ...[more]

PMID: 26635653

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Project description:Summary Counterfactual (CF) explanations have been employed as one of the modes of explainability in explainable artificial intelligence (AI)—both to increase the transparency of AI systems and to provide recourse. Cognitive science and psychology have pointed out that people regularly use CFs to express causal relationships. Most AI systems, however, are only able to capture associations or correlations in data, so interpreting them as casual would not be justified. In this perspective, we present two experiments (total n = 364) exploring the effects of CF explanations of AI systems’ predictions on lay people’s causal beliefs about the real world. In Experiment 1, we found that providing CF explanations of an AI system’s predictions does indeed (unjustifiably) affect people’s causal beliefs regarding factors/features the AI uses and that people are more likely to view them as causal factors in the real world. Inspired by the literature on misinformation and health warning messaging, Experiment 2 tested whether we can correct for the unjustified change in causal beliefs. We found that pointing out that AI systems capture correlations and not necessarily causal relationships can attenuate the effects of CF explanations on people’s causal beliefs. The bigger picture Explainable artificial intelligence provides methods for bringing in transparency into black-box artificial intelligence (AI) systems. These methods produce explanations of AI systems’ predictions that are aimed at increasing the understanding of the AI systems’ behavior and help us to appropriately calibrate our trust in these systems. In this perspective, we explore some of the potential undesirable effects of providing explanations of AI systems to human users and ways to mitigate such effects. We start from the observation that most AI systems capture correlations and associations in data and not causal relationships. Explanations of the AI systems’ predictions would make the correlations more transparent. They would not, however, make the explained relationships causal. In two experiments, we show how providing counterfactual explanations of AI systems’ predictions unjustifiably changes people’s beliefs about causal relationships in the real world. We also show how we may go about preventing such a change in beliefs and hope to open doors for further exploration into psychological effects of AI explanations on human recipients. Most AI systems capture correlations and associations in data. Explaining AI systems’ predictions amounts to making these correlational relationships more transparent, yet not causal. Some AI explainability methods produce types of explanations that people would naturally use to communicate causal relationships in the world. We provide evidence that these explanations of AI systems, known as counterfactual explanations, can lead to an unjustified change in people’s causal beliefs about the world. We also propose a way to prevent this unjustified change.

Project description:Being moved has received increased attention in emotion psychology as a social emotion that fosters bonds between individuals and within communities. This increased attention, however, has also sparked debates about whether the term "being moved" refers to a single distinct profile of emotion components or rather to a range of different emotion profiles. We addressed this question by investigating lay conceptions of the emotion components (i.e., elicitors, cognitive appraisals, subjective feelings, bodily symptoms, and consequences for thought/action) of "bewegt sein" (the German term for "being moved"). Participants (N = 106) provided written descriptions of both a moving personal experience and their conceptual prototype of "being moved," which were subjected to content analysis to obtain quantitative data for statistical analyses. Based on latent class analyses, we identified two classes for both the personal experiences (joyfully-moved and sadly-moved classes) and the being-moved prototype (basic-description and extended-description classes). Being joyfully moved occurred when social values and positive relationship experiences were salient. Being sadly moved was elicited by predominantly negative relationship experiences and negatively salient social values. For both classes, the most frequently reported consequences for thought/action were continued cognitive engagement, finding meaning, and increased valuation of and striving for connectedness/prosociality. Basic descriptions of the prototype included "being moved" by positive or negative events as instances of the same emotion, with participants in the extended-description class also reporting joy and sadness as associated emotions. Based on our findings and additional theoretical considerations, we propose that the term "being moved" designates an emotion with an overall positive valence that typically includes blends of positively and negatively valenced emotion components, in which especially the weight of the negative components varies. The emotion's unifying core is that it involves feeling the importance of individuals, social entities, and abstract social values as sources of meaning in one's life.

Dataset Information

The Depression Schema: How Labels, Features, and Causal Explanations Affect Lay Conceptions of Depression.

Publications

The Depression Schema: How Labels, Features, and Causal Explanations Affect Lay Conceptions of Depression.

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