Attachment--and loss.

In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A.G. Greenwald, D.E. McGhee, & J.L.K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.

/pdf/understanding-and-using-the-implicit-association-test-i-an-1sxb24sjrl.pdf

Understanding and using the Implicit Association Test: I. An improved scoring algorithm.

At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated. These states--called core affect--influence reflexes, perception, cognition, and behavior and are influenced by many causes internal and external, but people have no direct access to these causal connections. Core affect can therefore be experienced as free-floating (mood) or can be attributed to some cause (and thereby begin an emotional episode). These basic processes spawn a broad framework that includes perception of the core-affect-altering properties of stimuli, motives, empathy, emotional meta-experience, and affect versus emotion regulation; it accounts for prototypical emotional episodes, such as fear and anger, as core affect attributed to something plus various nonemotional processes.

/pdf/core-affect-and-the-psychological-construction-of-emotion-4bds0c0fm7.pdf

Core Affect and the Psychological Construction of Emotion

This review of 122 research reports (184 independent samples, 14,900 subjects) found average r = .274 for prediction of behavioral, judgment, and physiological measures by Implicit Association Test (IAT) measures. Parallel explicit (i.e., self-report) measures, available in 156 of these samples (13,068 subjects), also predicted effectively (average r = .361), but with much greater variability of effect size. Predictive validity of self-report was impaired for socially sensitive topics, for which impression management may distort self-report responses. For 32 samples with criterion measures involving Black-White interracial behavior, predictive validity of IAT measures significantly exceeded that of self-report measures. Both IAT and self-report measures displayed incremental validity, with each measure predicting criterion variance beyond that predicted by the other. The more highly IAT and self-report measures were intercorrelated, the greater was the predictive validity of each.

https://faculty.washington.edu/agg/pdf/GPU&B.meta-analysis.JPSP.2009.pdf

Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity.

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Beyond verbal self-report: Priming methods in relationship research.

The current research presents an adapted Affect Misattribution Procedure (AMP) to assess the aspects of the relational schema of romantic partners that are difficult to verbalize and tests the prediction that a positive implicit image of the partner constitutes a psychological resource that increases well-being. In Study 1, the partner-AMP predicted well-being in student participants even after controlling for explicit partner attitudes and socially desirable responding. In Study 2, the partner-AMP was assessed in women with a recent history of physical abuse and a control group. As expected, the partner-AMP was related to group membership and predicted well-being over and above explicit partner attitudes and battering experience. The results are compatible with the notion of an implicit secure base schema.

Partner‐AMP and well‐being: Evidence for an implicit secure base script?

This study investigates the malleability of explicit and implicit anxiety through mental imagery. Sixty adults imagined themselves in an anxious, calm, or neutral situation. Thereafter, explicit state and trait anxiety were assessed with self-reports, and implicit anxiety was assessed with a variant of the Implicit Association Test. The results indicate that imagery manipulation changed state anxiety in the expected direction. Explicit trait anxiety and implicit anxiety, however, were found to be stable. These findings suggest that the implicit self-concept of anxiety has trait-like characteristics and is as stable against a short-term voluntary mental control strategy as an established explicit measure of trait anxiety.

Implicit but Stable : Mental Imagery Changes Explicit but not Implicit Anxiety

. Three years ago, Greenwald, McGhee, and Schwartz (1998) presented a new method to measure differential evaluative association of two target concepts: the Implicit Association Test (IAT). ...

Rainer Banse

Papers

Beyond verbal self-report: Priming methods in relationship research.

Partner‐AMP and well‐being: Evidence for an implicit secure base script?

Implicit but Stable : Mental Imagery Changes Explicit but not Implicit Anxiety

Attitude MeasurementUsing the ImplicitAssociation Test (IAT)

Spousal concordance in the use of alternative tobacco products: A multi-country investigation