Distinguishing the desire to learn from the desire to perform: The social value of achievement goals

We sought to distinguish mastery goals (i.e., desire to learn) from performance goals (i.e., desire to achieve more positive evaluations than others) in the light of social judgment research. In a pilot study, we made a conceptual distinction between three types of traits (agency, competence, and effort) that are often undifferentiated. We then tested the relevance of this distinction for understanding how people pursuing either mastery or performance goals are judged. On self-perception, results revealed that effort was predicted by the adoption of mastery goals and agency by performance goals (Study 1). On judgments, results showed that (a) the target pursuing mastery goals was perceived as oriented toward effort, and (b) the target pursuing performance goals was oriented toward agency (Study 2). Finally, these links were shown again by participants who inferred a target’s goals from his traits (Study 3). Results are discussed in terms of the social value of achievement goals at school.     

Does anxiety-linked attentional bias to threatening information reflect bias in the setting of attentional goals,or bias in the execution of attentional goals?

Heightened anxiety vulnerability is characterised by an attentional bias that favours the processing of negative information. However, this anxiety-linked attentional bias is amenable to two quite different explanations. One possibility is that it reflects anxiety-linked bias in the setting of attentional goals that favours setting the goal of attending towards negative information over the alternative goal of attending away from such information. Another possibility is that it reflects anxiety-linked bias in the execution of attentional goals that enhances the execution of the former attentional goal compared to the latter. The present study introduces a novel methodology designed to discriminate the validity of these competing hypotheses, by examining anxiety-linked attentional bias under two conditions. One condition left attentional goals unconstrained. The other condition imposed the attentional goal of either attending towards more negative or more benign emotional stimuli. The finding that anxiety-linked attentional bias was observed only under the former condition supported the hypothesis that anxiety is characterised by a bias favouring the setting attentional goals involving vigilance rather than avoidance of negative information, while giving no support to the hypothesis that anxiety is characterised by a bias reflecting enhanced execution of the former attentional goal compared to the latter.     

Reinforcement,Rationality, and Intentions: How Robust Is Automatic Reinforcement Learning in Economic Decision Making?

Reinforcement learning is often observed in economic decision making and may lead to detrimental decisions. Because of its automaticity, it is difficult to avoid. In three experimental studies, we investigated whether this process could be controlled by goal intentions and implementation intentions. Participants' decisions were investigated in a probability‐updating task in which the normative rule to maximize expected payoff (Bayes' rule) conflicted with the reinforcement heuristic as a simple decision rule. Some participants were asked to set goal intentions designated to foster the optimization of rational decision making, while other participants were asked to furnish these goal intentions with implementation intentions. Results showed that controlling automatic processes of reinforcement learning is possible by means of goal intentions or implementation intentions that focus decision makers on the analysis of decision feedback. Importantly, such beneficial effects were not achieved by simply instructing participants to analyze the feedback, without defining a goal as the desired end state from a first‐person perspective. Regarding intentions supposed to shut down reinforcement processes by controlling negative affect, effects were more complex and depended on the specified goal‐directed behavior. The goal intention to suppress the disappointment elicited by negative feedback was not effective in controlling reinforcement processes. Furnishing this goal with an implementation intention even backfired and strengthened unwanted reinforcement processes. In contrast, asking participants to keep cool in response to negative decision outcomes through the use of goal intentions or implementation intentions increased decisions in line with Bayes' rule. Copyright © 2017 John Wiley & Sons, Ltd.     

Finding The “Self” in Self-Regulation: The Identity-Value Model

Many psychological theories suggest a link between self-regulation and identity, but, until now, a mechanistic account that suggests ways to improve self-regulation has not been put forth. The identity-value model (IVM) connects the idea from social psychology that aspects of identity such as core values and group affiliations hold positive subjective value to the process-focused account from decision making and behavioral economics that self-regulation is driven by a dynamic value integration across a range of choice attributes. Together, these ideas imply that goal-directed behaviors that are identity relevant are more likely to be enacted because they have greater subjective value than identity-irrelevant behaviors. A central hypothesis, therefore, is that interventions that increase the degree to which a target behavior is perceived as self-relevant will improve self-regulation. In addition, identity-based changes in self-regulation are expected to be mediated by changes in subjective value and its underlying neural systems. In this article we define the key constructs relevant to the IVM, explicate the model and delineate its boundary conditions, and describe how it fits with related theories. We also review disparate results in the research literature that might share identity-related value as a common underlying mechanism of action. We close by discussing questions about the model, the answers to which could advance the study of self-regulation.     

Attributional retraining and achievement goals: An exploratory study on theoretical and empirical relationship

IntroductionAchievement goals and attribution theory are theoretically and empirically linked, but existing literature lacks to explore the link between achievement goals and attributional retraining (AR), a motivational intervention based on the causal attribution theory.Objective(s)The aims of this field study were to determine the effectiveness of an AR treatment aimed to restructure college students’ dysfunctional causal explanations of poor performance and to explore whether achievement goals are predictive of the use of adaptive causal attributions.MethodsStudents’ achievement goals orientation and causal attributions were assessed and AR treatment was provided to a sample of second-year college students with maladaptive attributional schemas.ResultsFindings confirmed the effectiveness of AR treatment in restructuring self-defeating stable attributional explanations and suggested that achievement goals are implicated in the adoption of adaptive causal dimensions.ConclusionThe importance of integrating the two discussed theoretical models in order to provide efficacious AR interventions with students at risk is discussed.     

The association between compassionate goals and relational-interdependent self-construal

Although research has examined the consequences of relational self-construal (RSC), little is known about what psychological factors predict it. Four studies examined the association between compassionate goals and RSC. Study 1 showed that compassionate goals are positively associated with RSC in college students. Study 2 replicated this association among adults in romantic relationships. Studies 3 and 4 showed that compassionate goals predict increased RSC over time in college roommates. Moreover, Studies 2–4 showed that responsiveness to relationship partners statistically mediated the association between compassionate goals and self-construal. These studies suggested that people with compassionate goals have high RSCs and that they also develop higher levels of RSC by being responsive to relationship partners.     

Environment and Goals Jointly Direct Category Acquisition

Abstract— Developing categorization schemes involves discovering structures in the world that support a learner's goals. Existing models of category learning, such as exemplar and prototype models, neglect the role of goals in shaping conceptual organization. Here, a clustering approach is discussed that reflects the joint influences of the environment and goals in directing category acquisition. Clusters are a flexible representational medium that exhibits properties of exemplar, prototype, and rule-based models. Clusters reflect the natural bundles of correlated features present in our environment. The clustering model Supervised and Unsupervised Stratified Incremental Adaptive Network (SUSTAIN) operates by assuming the world has a simple structure and adding complexity (i.e., clusters) when existing clusters fail to satisfy the learner's goals and thus elicit surprise. Although simple, this operation is sufficient to address findings from numerous laboratory and cross-cultural categorization studies.     

Category learning with a goal: how goals constrain conceptual acquisition

A novel task was used to examine the roles that goals play in concept acquisition. In Experiment 1, we varied the type of interaction, and thus the task goal, of participants working in a novel domain. Following those interactions, participant responses showed that they had organized their knowledge in terms of goal-relevant features. Using a variation of the same methodology, Experiment 2 provided evidence that the goal relevance also played a role in how the participants structured their knowledge of the items, specifically what information about the items was associated with differentiating the categories encountered. The results suggest that the goals not only highlighted particular features, they determined the centrality of those features in the conceptual knowledge. We discuss the results in terms of the goal-framework hypothesis; the idea that goals structure information and provide coherence to the acquired concepts. We discuss how this approach informs category learning research.