Components of Negative Emotion and Emotion-Congruent Information Processing Biases

<p>Negative emotion is often associated with emotion-congruent biases in information processing. However, rather than all negative emotion being associated with biases in all information processes, certain components of emotion appear to be associated with specific biases. This project examined two examples of specific associations. First, Williams, Watts, MacLeod, & Mathews (1988, 1997) have argued that anxiety is associated with biases on tasks involving priming, and depression is associated with biases on tasks involving elaboration. Second, most models of mood-congruent bias have given purely cognitive explanations; these models suggest that biases should be more closely associated with the cognitive symptoms than the somatic symptoms of depression (Horowitz, Nelson, & Person, 1997). Evidence is reviewed that suggests this may not be the case. These issues were examined in two experiments, each of which administered a broad range of tasks to a large sample of students. The experiments examined attention and judgement, and explicit, implicit, and autobiographical memory. It was hypothesised that Williams et al.'s (1988, 1997) predictions about the task-specific effects of anxiety and depression would be confirmed, and that the somatic symptoms of depression would have a greater influence on information processing biases than the cognitive symptoms. Emotion-congruent biases were not shown on every task, but on the tasks where biases were shown, the hypotheses were broadly confirmed. Strengths, limitations, and implications of the studies are discussed. Current cognitive and neuropsychological models of emotion are used to provide a possible explanation of the results.</p>

Components of Negative Emotion and Emotion-Congruent Information Processing Biases

<p>Negative emotion is often associated with emotion-congruent biases in information processing. However, rather than all negative emotion being associated with biases in all information processes, certain components of emotion appear to be associated with specific biases. This project examined two examples of specific associations. First, Williams, Watts, MacLeod, & Mathews (1988, 1997) have argued that anxiety is associated with biases on tasks involving priming, and depression is associated with biases on tasks involving elaboration. Second, most models of mood-congruent bias have given purely cognitive explanations; these models suggest that biases should be more closely associated with the cognitive symptoms than the somatic symptoms of depression (Horowitz, Nelson, & Person, 1997). Evidence is reviewed that suggests this may not be the case. These issues were examined in two experiments, each of which administered a broad range of tasks to a large sample of students. The experiments examined attention and judgement, and explicit, implicit, and autobiographical memory. It was hypothesised that Williams et al.'s (1988, 1997) predictions about the task-specific effects of anxiety and depression would be confirmed, and that the somatic symptoms of depression would have a greater influence on information processing biases than the cognitive symptoms. Emotion-congruent biases were not shown on every task, but on the tasks where biases were shown, the hypotheses were broadly confirmed. Strengths, limitations, and implications of the studies are discussed. Current cognitive and neuropsychological models of emotion are used to provide a possible explanation of the results.</p>

Inflexible Interpretations of Ambiguous Social Situations: A Novel Predictor of Suicidal Ideation and the Beliefs That Inspire It

Suicidal ideation has been linked to a bias toward interpreting ambiguous information in consistently less positive or more negative manners ( positive/negative interpretation bias), implying that information-processing biases might distort beliefs thought to inspire suicidal ideation (e.g., those regarding burdensomeness). Therefore, in the present study, we examined whether suicidal ideation and beliefs highlighted in theories of suicide are related to positive/negative interpretation bias and/or a bias against revising negative interpretations in response to evidence against them ( negative interpretation inflexibility). Data were collected in three waves, each 1 week apart. Network analyses and structural equation models provided evidence that negative interpretation bias (cross-sectionally) and negative interpretation inflexibility (cross-sectionally and over time) were related to suicidal ideation and that the latter relationship was mediated by perceived burdensomeness. By identifying this mediation pathway in the present study, we provide a potential mechanism by which perceptions of burdensomeness, a key risk factor for suicidality, might arise and/or persist.

Towards Strong AI

Strong AI—artificial intelligence that is in all respects at least as intelligent as humans—is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines.

The effect of financial literacy on behavioral biases of individual investors in the Egyptian stock exchange

The purpose of this paper is to examine how the dimensions of financial literacy could affect the behavioral biases of individual investors in the Egyptian stock exchange. The study examines the data collected from 403 individual investors in Egypt. The findings revealed the presence of some kinds of behavioral biases among individual investors in the Egyptian stock exchange, which could be categorized into three main categories: belief perseverance biases, information processing biases, and emotional biases (Pompian, 2012). This supports the view that individual investors do not necessarily act rationally. The findings also support the general view that financial literacy has a negative effect on behavioral biases; however, the effect differs between the categories of the behavioral biases, with the most effect on information processing biases, moderate effect on belief perseverance biases, and low effect on emotional biases. Also, this study indicated that the impact of financial literacy on behavioral biases is greater on females than males (Baker, Kumar, Goyal, & Gaur, 2019). Financial intermediaries and consultants can possibly become more effective by understanding the decision-making processes of individual investors. This study adds to the limited academic research that attempted to tackle the impact of financial literacy on the categories of behavioral biases

Cognitive Restructuring

This chapter introduces the idea of information-processing biases using the “amber-colored glasses” metaphor. Although information-processing biases are well established in the research literature, this is a difficult concept to communicate to socially anxious individuals. By describing the amber-colored glasses as a natural outcome of a particular combination of genetics, family environment, and important experiences, the therapist can indicate that the way in which the client processes information makes sense; it just may not be functional. The chapter also covers identification of automatic thoughts (ATs) and finding logical errors (the identification of thinking errors) in ATs. The notion of ATs is used extensively throughout treatment.

Identifying the Thoughts That Cause Anxiety

Cognitive restructuring is a procedure that helps to examine how people are thinking and to consider whether there may be a more useful way to look at a situation that makes them anxious. Clients learn to identify the thinking errors in the automatic thoughts (ATs) they have when they get anxious. They also have the opportunity to see if they tend to use particular thinking errors when they have anxious thoughts. Clients are taught (a) what they think influences how anxious they feel and (b) how to change what they are thinking so that they can better manage their anxiety and do the things they want to do in life. This chapter introduces the idea of information-processing biases using the “amber-colored glasses” metaphor. As people learn to change their ATs, they feel less anxious and depressed.

D-Cycloserine as Adjunct to Brief Computerised CBT for Spider Fear: Effects on Fear, Behaviour, and Cognitive Biases

In anxiety disorders, cognitive behavioural therapy (CBT) improves information-processing biases such as implicit fear evaluations and avoidance tendencies, which predicts treatment response, so they might constitute important treatment targets. This study investigated (i) whether information-processing biases changed following single-session computerised CBT for spider fear, and (ii) whether this effect could be augmented by administration of D-cycloserine (DCS). Spider-fearful individuals were randomized to receiving 250mg of DCS (n=21) or placebo (n=17) and spider fear was assessed using self-report, behavioural, and information-processing (Extrinsic Affective Simon Task & Approach Avoidance Task) measures. Linear mixed-effects analyses indicated improvements on self-report and behavioural spider fear following CBT, but not on cognitive bias measures. There was no evidence of an augmentation effect of DCS on any outcome. Cognitive biases at 1-day were not predictive of 1-month follow-up spider fear. These findings provide no evidence for information-processing biases relating to CBT response or augmentation with DCS.

Information Processing Biases in Impairment Decisions: Effect of Reversibility of Impairment Losses and Disclosure Transparency

ABSTRACT We investigate the effect of regulatory requirements on impairment decisions and managers' search for and evaluation of impairment information. We manipulate reversibility of impairment losses (“can be reversed” versus “cannot be reversed”) and transparency in disclosures of impairment assumptions (more transparent versus less transparent) in a 2 × 2 experiment. We find that managers are more willing to impair when impairment losses can be reversed than when they cannot be reversed, but this effect does not vary with disclosure transparency. We also find that managers display information search bias in all four experimental conditions, however, regulatory requirements do not result in differences in the level of information search bias across the conditions. In contrast, regulatory requirements affect the differences in the level of information evaluation bias across conditions. We find that when impairment losses cannot be reversed, information evaluation bias is higher when disclosures are more transparent than less transparent. JEL Classification: M40; M41.