Temporal stability of multiple response systems to 7.5% carbon dioxide challenge

Roberson‐Nay, Roxann; Gorlin, Eugenia I.; Beadel, Jessica R.; Cash, Therese V.; Vrana, Scott R.; Teachman, Bethany A.

doi:10.1016/j.biopsycho.2017.01.014

Cited by 8 publications

(8 citation statements)

References 44 publications

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“…As in the theory development stage, the theory testing stage thus calls for us to deduce the precise data models implied by our theory and to compare those implied data models to empirical data models. For example, a very well-developed theory of panic disorder would be able to predict the peak value of perceived threat likely to result from a particular arousal-inducing manipulation (e.g., by breathing CO2 enriched air; Roberson-Nay et al, 2017). More importantly, it may predict that previously unconsidered treatments should be effective in the treatment of Panic Disorder or that a characteristic pattern of target system behavior signifies vulnerability to panic attacks, thereby identifying a marker of vulnerability that can support preventive interventions before the disorder arises (cf.…”

Section: Testing Theorymentioning

confidence: 99%

Modeling Psychopathology: From Data Models to Formal Theories

Haslbeck¹,

Ryan²,

Robinaugh³

et al. 2019

Preprint

112

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Over the past decade there has been a surge of empirical research investigating mental disorders as complex systems. In this paper, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the Abductive Formal Theory Construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science.

show abstract

Section: Testing Theorymentioning

confidence: 99%

Modeling Psychopathology: From Data Models to Formal Theories

Haslbeck¹,

Ryan²,

Robinaugh³

et al. 2019

Preprint

112

View full text Add to dashboard Cite

show abstract

“…The increase in feelings of anxiety is eight times higher in individuals that are more responsive to CO 2 , than that of less responsive individuals [ 62 ]. Feelings of anxiety and experiences of panic due to ~ 7% CO 2 inhalation are consistent between repeated inhalations [ 62 , 63 ]. Vulnerability to CO 2 -induced anxiety and panic increases in people diagnosed with panic disorder [ 3 , 64 ] and individuals with a first-degree relative diagnosed with panic disorder [ 4 , 65 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

Individual differences in rat sensitivity to CO2

2021

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Feelings of fear, anxiety, dyspnea and panic when inhaling carbon dioxide (CO2) are variable among humans, in part due to differences in CO2 sensitivity. Rat aversion to CO2 consistently varies between individuals; this variation in aversion may reflect CO2 sensitivity, but other personality traits could also account for individual differences in aversion. The aims of this study were to 1) assess the stability of individual differences in rat aversion to CO2, 2) determine if individual differences in sweet reward motivation are associated with variation in aversion to CO2, and 3) assess whether variation in aversion to CO2 is related to individual differences in motivation to approach gains (promotion focus) or maintain safety (prevention focus). Twelve female Sprague Dawley rats were exposed multiple times at three different ages (3, 9 and 16 months old) to CO2 in approach-avoidance testing to assess motivation to avoid CO2 against motivation to gain sweet rewards. Rats were also tested for motivation to find hidden sweet rewards, and for their motivation to approach rewards or darkness. Tolerance to CO2 increased with repeated exposures and was higher at older ages. Individual differences in aversion to CO2 were highly repeatable but unrelated to motivation for sweet rewards or the strength of promotion and prevention focus. These results indicate that individual differences in aversion to CO2 reflect variation in CO2 sensitivity.

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“…That is, a prediction that goes beyond merely positing a refutation of the null hypothesis (e.g., a statistically significant association) or even a directional prediction (e.g., a positive association) to instead make precise point predictions about what should be observed. For example, a very well-developed theory of panic disorder would be able to predict the precise value of perceived threat (or interval of perceived threat values) which are likely to result from a particular arousal-inducing manipulation (e.g., by breathing CO2 enriched air; Roberson-Nay et al, 2017). In other words, just as in the theory development stage, the theory testing stage calls for us to deduce the precise data models implied by our theory and to compare those implied data models to empirical data models.…”

Section: Testing Theorymentioning

confidence: 99%

Modeling psychopathology: From data models to formal theories.

Haslbeck¹,

Ryan²,

Robinaugh³

et al. 2021

Psychological Methods

View full text Add to dashboard Cite

Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science.

show abstract

Temporal stability of multiple response systems to 7.5% carbon dioxide challenge

Cited by 8 publications

References 44 publications

Modeling Psychopathology: From Data Models to Formal Theories

Modeling Psychopathology: From Data Models to Formal Theories

Individual differences in rat sensitivity to CO2

Modeling psychopathology: From data models to formal theories.

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