Dataset Information

Sensitivity to Changing Environmental Conditions across Individuals with Subtype 2 Automatically Reinforced and Socially Reinforced Self-injury.

ABSTRACT: Automatically reinforced Subtype 2 self-injurious behavior (ASIB) has been characterized as showing insensitivity to competing reinforcement contingencies in the contexts of both functional analyses and in treatment using reinforcement alone (Hagopian, Rooker, &Yenokyan, 2018). One question is whether this insensitivity is specific to Subtype 2 ASIB as response class in these contexts or whether it is represents a generalized response tendency of the individual that is evident across other response classes. To examine this question, we compared responding on a single-operant task under changing reinforcement schedules for three individuals with Subtype 2 ASIB, relative to a comparison group of three individuals with socially reinforced SIB (which is characterized by sensitivity to changes in reinforcement contingencies). As hypothesized, all individuals showed sensitivity to changes in contingencies. These results provide preliminary support that the insensitivity of Subtype 2 ASIB is a property specific to that response class in these contexts rather than a generalized response tendency of the individual.

SUBMITTER: Rooker GW

PROVIDER: S-EPMC7274523 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Sensitivity to Changing Environmental Conditions across Individuals with Subtype 2 Automatically Reinforced and Socially Reinforced Self-injury.

Rooker Griffin W GW Hagopian Louis P LP Haddock Jennifer N JN Mezhoudi Nabil N Arevalo Alexander R AR

Behavioral development bulletin 20191001 2

Automatically reinforced Subtype 2 self-injurious behavior (ASIB) has been characterized as showing insensitivity to competing reinforcement contingencies in the contexts of both functional analyses and in treatment using reinforcement alone (Hagopian, Rooker, &Yenokyan, 2018). One question is whether this insensitivity is specific to Subtype 2 ASIB as response class in these contexts or whether it is represents a generalized response tendency of the individual that is evident across other respo ...[more]

PMID: 32509138

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Project description:Positive self-beliefs are important for well-being, and are influenced by how others evaluate us during social interactions. Mechanistic accounts of self-beliefs have mostly relied on associative learning models. These account for choice behaviour but not for the explicit beliefs that trouble socially anxious patients. Neither do they speak to self-schemas, which underpin vulnerability according to psychological research. Here, we compared belief-based and associative computational models of social-evaluation, in individuals that varied in fear of negative evaluation (FNE), a core symptom of social anxiety. We used a novel analytic approach, 'clinically informed model-fitting', to determine the influence of FNE symptom scores on model parameters. We found that high-FNE participants learn more easily from negative feedback about themselves, manifesting in greater self-negative learning rates. Crucially, we provide evidence that this bias is underpinned by an overall reduced belief about self-positive attributes. The study population could be characterized equally well by belief-based or associative models, however large individual differences in model likelihood indicated that some individuals relied more on an associative (model-free), while others more on a belief-guided strategy. Our findings have therapeutic importance, as positive belief activation may be used to specifically modulate learning.Author summaryUnderstanding how we form and maintain positive self-beliefs is crucial to understanding how things go awry in disorders such as social anxiety. The loss of positive self-belief in social anxiety, especially in inter-personal contexts, is thought to be related to how we integrate evaluative information that we receive from others. We frame this social information integration as a learning problem and ask how people learn whether someone approves of them or not. We thus elucidate why the decrease in positive evaluations manifests only for the self, but not for an unknown other, given the same information. We investigated the mechanics of this learning using a novel computational modelling approach, comparing models that treat the learning process as series of stimulusresponse associations with models that treat learning as updating of beliefs about the self (or another). We show that both models characterise the process well and that individuals higher in symptoms of social anxiety learn more from negative information specifically about the self. Crucially, we provide evidence that this originates from a reduction in the amount of positive attributes that are activated when the individual is placed in a social evaluative context.

Project description:BackgroundBiological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network.Methodology/principal findingsStarting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response.Conclusions/significanceNetwork-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.

Dataset Information

Sensitivity to Changing Environmental Conditions across Individuals with Subtype 2 Automatically Reinforced and Socially Reinforced Self-injury.

Publications

Sensitivity to Changing Environmental Conditions across Individuals with Subtype 2 Automatically Reinforced and Socially Reinforced Self-injury.

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