Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

Kryklywy, James H.; Ehlers, Mana R.; Beukers, Andre; Moore, Sarah R.; Todd, Rebecca M.; Anderson, Adam K.

doi:10.1101/2020.09.24.310383

Cited by 2 publications

(9 citation statements)

References 93 publications

(225 reference statements)

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“…Bayesian Information Criterion (BIC) was used to find the best combination of POIs for each ROI. Multiple regression was used to obtain beta coefficients for each POI (Kryklywy, Ehlers, et al, 2021). D. RSA was performed separately on early, mid, and late CS- only trials to examine reactivation of US information over the course of conditioning.…”

Section: Resultsmentioning

confidence: 99%

“…Representational information in BOLD responses to the US was identified by using Bayesian Information Criterion (BIC) to fit predefined POIs – idealized similarity matrices modelling specific information content (e.g., the valence of the touch stimulus) – to the observed US similarity data (Figure 1c). The representation of appetitive and aversive US, independent from CS- only trials, are focus of a different study (Kryklywy, Ehlers, et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…To identify the extent to which US patterns were reactivated by CS following learning, a reconstructed measure of US activation similarity (rUS) was fit to CS similarity data from early-, mid-, and late-conditioning trials. rUS were constructed by summing the scaled contribution of each POI contributing to overall similarity in an ROI (identified though hypothesis-driven PCM; see (Kryklywy, Ehlers, et al, 2021). Next, to examine the degree to which each specific US- defined POI was comparable to the pattern of CS activation – the content driven reactivation – separate Bayesian linear models with the combination of US–defined POIs as predictors, and rUS and CS representational patterns as outcome variables were fitted in order to compare the (beta) weight of each predictor for the CS representational pattern to that of the US (see Figure 1d).…”

Section: Resultsmentioning

confidence: 99%

“…In order to characterize the content of CS (and US) representation in key regions of interest, we developed a theory-guided implementation of Pattern Component Modeling (PCM) (Diedrichsen et al, 2018;Kriegeskorte & Kievit, 2013;Kryklywy, Ehlers, et al, 2021;Kryklywy, Forys, et al, 2021). The details are described in Kryklywy, Ehlers, et al, 2021. In brief, we created Specific Touch, 4) Appetitive Brush, 5) Aversive Pressure, 6) Touch Valence, 7) Positive Events, 8) Negative Events, 9) All Valence, 10) Salience, 11) Face Stimulus, 12) Violation of Expectation and 13) Temporal Adjacency (see Figure 1c). In order to determine the POI combinations that best explained the observed correlation in the US data in each ROI, a Bayesian Information Criterion (BIC) analysis and multiple regression implemented in our R package 'PCMforR' (Kryklywy, Forys, et al, 2021) were conducted.…”

Section: Pattern Component Modelingmentioning

confidence: 99%

“…RSA was combined with a hypothesis-driven variation of Pattern Component Modeling (PCM) (Diedrichsen et al, 2018). This is an approach we recently developed to use fMRI to decode and fit representational patterns in response to appetitive and aversive US to predefined pattern components modeling theoretical representations patterns of interest (POIs) present in the US evoked signal (Kryklywy, Ehlers, et al, 2021). With this technique, we could investigate the extent to which hedonic and non-hedonic aspects of sensation are reproduced by the CS as novel affective associations are acquired.…”

Section: Introductionmentioning

confidence: 99%

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Reactivation of hedonic but not sensory representations in human emotional learning

Ehlers

Kryklywy

Beukers

et al. 2021

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Learning which stimuli in our environment co-occur with painful or pleasurable events is critical for survival. Previous research has established the basic neural and behavioural mechanisms of aversive and appetitive conditioning; however, it is unclear what precisely is learned. Here we examined what aspects of the unconditioned stimulus (US) – sensory and hedonic – are transferred to the conditioned stimulus (CS). To decode the content of brain activation patterns elicited during appetitive (soft touch) and aversive (painful touch) conditioning of faces, a novel variation of representational similarity analysis (RSA) based on theoretically driven representational patterns of interest (POIs) was applied to fMRI data. Once face associations were learned through conditioning, globally the CS reactivated US representational patterns showing conditioning-dependent reactivation. More specifically, in higher order brain regions, the CS only reactivated hedonic but not sensory aspects of the US – suggesting that affective conditioning primarily carries forward the valence of the experience rather than its sensory origins.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Pattern Component Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reactivation of hedonic but not sensory representations in human emotional learning

Ehlers

Kryklywy

Beukers

et al. 2021

Preprint

Self Cite

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Threat induction biases processing of emotional expressions

2022

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Threats can derive from our physical or social surroundings and bias the way we perceive and interpret a given situation. They can be signaled by peers through facial expressions, as expressed anger or fear can represent the source of perceived threat. The current study seeks to investigate enhanced attentional state and defensive reflexes associated with contextual threat induced through aversive sounds presented in an emotion recognition paradigm. In a sample of 120 healthy participants, response and gaze behavior revealed differences in perceiving emotional facial expressions between threat and safety conditions: Responses were slower under threat and less accurate. Happy and neutral facial expressions were classified correctly more often in a safety context and misclassified more often as fearful under threat. This unidirectional misclassification suggests that threat applies a negative filter to the perception of neutral and positive information. Eye movements were initiated later under threat, but fixation changes were more frequent and dwell times shorter compared to a safety context. These findings demonstrate that such experimental paradigms are capable of providing insight into how context alters emotion processing at cognitive, physiological, and behavioral levels. Such alterations may derive from evolutionary adaptations necessary for biasing cognitive processing to survive disadvantageous situations. This perspective sets up new testable hypotheses regarding how such levels of explanation may be dysfunctional in patient populations.

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Decoding representations of discriminatory and hedonic information during appetitive and aversive touch

Cited by 2 publications

References 93 publications

Reactivation of hedonic but not sensory representations in human emotional learning

Reactivation of hedonic but not sensory representations in human emotional learning

Threat induction biases processing of emotional expressions

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