Bayesian model selection maps for group studies

Rosa, Maria J.; Bestmann, Sven; Harrison, L.; Penny, Will D.

doi:10.1016/j.neuroimage.2009.08.051

Cited by 84 publications

(118 citation statements)

References 33 publications

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“…This allows for computing how likely it is that a specific model caused the observed data of a randomly chosen subject and, furthermore, the exceedance probability of one model being more likely than any other model (Stephan et al, 2009). Put simply, this approach can be viewed as a randomeffects analysis in which a (potentially different) model is assigned to each member of the group (Rosa et al, 2010). We used the conditional model probability to quantify an exceedance probability, i.e., our belief that a particular model is more likely than any other model, given the data from all participants (Stephan et al, 2009;Rosa et al, 2010).…”

Section: Model Assumptions and Model Comparisonmentioning

confidence: 99%

“…Put simply, this approach can be viewed as a randomeffects analysis in which a (potentially different) model is assigned to each member of the group (Rosa et al, 2010). We used the conditional model probability to quantify an exceedance probability, i.e., our belief that a particular model is more likely than any other model, given the data from all participants (Stephan et al, 2009;Rosa et al, 2010).…”

Section: Model Assumptions and Model Comparisonmentioning

confidence: 99%

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Action Reprogramming in Parkinson's Disease: Response to Prediction Error Is Modulated by Levels of Dopamine

Galea¹,

Bestmann²,

Beigi³

et al. 2012

J. Neurosci.

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Humans are able to use knowledge of previous events to estimate the probability of future actions. Consequently, an unexpected event will elicit a prediction error as the prepared action has to be replaced by an unprepared option in a process known as "action reprogramming" (AR). Here we show that people with Parkinson's disease (PD) have a dopamine-sensitive deficit in AR that is proportional to the size of the prediction error. Participants performed a probabilistic reaction time (RT) task in the context of either a predictable or unpredictable environment. For an overall predictable sequence, PD patients, on and off dopamine medication, and healthy controls showed similar improvements in RT. However, in the context of a generally predictable sequence, PD patients off medication were impaired in reacting to unexpected events that elicit large prediction errors and require AR. Critically, this deficit in AR was modulated by the prediction error associated with the upcoming event. The prolongation of RT was not observed during an overall unpredictable sequence, in which relatively unexpected events evoke little prediction error and the requirement for AR should be minimal, given the context. The data are compatible with recent theoretical accounts suggesting that levels of dopamine encode the reliability, i.e., precision, of sensory information. In this scheme, PD patients off medication have low dopamine levels and may therefore be less confident about incoming sensory information and more reliant on top-down predictions. Consequently, when these internal predictions are incorrect, PD patients take longer to respond appropriately to unexpected sensory information.

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Section: Model Assumptions and Model Comparisonmentioning

confidence: 99%

Section: Model Assumptions and Model Comparisonmentioning

confidence: 99%

Action Reprogramming in Parkinson's Disease: Response to Prediction Error Is Modulated by Levels of Dopamine

Galea¹,

Bestmann²,

Beigi³

et al. 2012

J. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ratio of two model evidences is the log-evidence ratio, commonly known the Bayes factor [Kass and Raftery, 1995]. In this study, we followed the analysis procedure described by Rosa et al [2010]. We first estimated each model, described above, using the first-level Bayesian estimation procedure [Penny et al, 2005] for every subject.…”

Section: Bayesian Model Comparisonmentioning

confidence: 99%

“…This resulted in a voxel-wise whole-brain log-evidence r De Baene et al r r 642 r map for every subject and for every model we have estimated. We then applied the random effects approach [Stephan et al, 2009] to the group model log-evidence data in a voxel-wise manner, providing a posterior probability map [PPM; Friston and Penny, 2003] and an exceedance probability map (EPM) for each model [Rosa et al, 2010] at group-level. The posterior probability is a measure of likelihood that a specific model generated the data of a randomly chosen subject.…”

Section: Bayesian Model Comparisonmentioning

confidence: 99%

Challenging a decade of brain research on task switching: Brain activation in the task‐switching paradigm reflects adaptation rather than reconfiguration of task sets

Baene¹,

Kühn²,

Braß³

2011

Human Brain Mapping

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In daily life, we permanently need to adapt our behavior to new task situations, requiring cognitive control. Such adaptive processes are commonly investigated with the task-switching paradigm. Many fMRI studies have interpreted stronger activation for switch than repeat trials in fronto-parietal brain areas as reflecting an active reconfiguration process in switch trials, tuning the cognitive system for proper task execution. From the single cell literature, however, one could deduce the alternative interpretation that switch-specific activity reflects reduced brain activity in repeat trials due to adaptation. These alternative explanations cannot be distinguished by simply comparing brain activity in switch and repeat trials. Therefore, we used a parametric approach to examine which interpretation is more powerful to account for the data. In all areas of the fronto-parietal network, adaptation explained the data better than reconfiguration. Therefore, our results call the classical reconfiguration interpretation into question and provide first evidence for adaptation of abstract task representations.

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“…To answer the above question, we performed a Bayesian model estimation and comparison with participants as random effects (Rosa, Bestmann, Harrison & Penny, 2010;Stephan, Penny, Daunizeau, Moran & Friston, 2009). Parameters of two linear models were estimated with the data of the 5 participants from which we obtained causal judgments.…”

Section: Fmrimentioning

confidence: 99%

Broca's Area Activity in the Lexical Semantics of Visual Causal Events

Limongi

Habib

Young

et al. 2016

RLA

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From a cognitive linguistics perspective, is assessed the effects of causative constructions on the activity of Broca's area during the processing of visual causal and non-causal events. Lexical causatives (e.g., the orange ball moves the purple ball) describe only direct causal events whereas periphrastic causatives (e.g., the orange ball causes the purple ball to move) describe both direct and indirect causal events. Based on this difference, is used lexical and periphrastic causatives as verbal instructions that directed participants to judge billiards balls collisions depicting direct, indirect, and non-causal events. By using functional magnetic resonance imaging (fMRI) and region of interest analysis, is found that judgments of all three visual events more strongly activated Broca's area following the periphrastic instruction than following the lexical instruction, and judgments of direct events produced stronger activity in Broca's area than indirect events. Interestingly, causal judgments were segregated between pars opercularis and pars triangularis. Results are discussed within the context of the linguistic category priming hypothesis, linear ballistic accumulator models, and the hierarchical organization of the prefrontal cortex. Because this our data suggest functional segregation during causal judgments, it is proposed that 44 RLA. Revista de Lingüística Teórica y Aplicada, 54 (2), II Sem. 2016 effective connectivity between these regions is worth evaluating in a follow-up study via dynamic causal modeling of fMRI data.Keywords: Causal judgment; causal conceptualization; lexical causatives; periphrastic causatives; Broca's area. RESuMENDesde una perspectiva de la lingüística cognitiva, se evalúa los efectos de las construcciones causativas en la actividad del área de Broca durante el procesamiento de eventos visuales causales y no causales. Las estructuras léxicas causativas (e.g., la bola anaranjada mueve a la bola púrpura) describen solamente eventos causales directos mientras que las estructuras perifrásticas causativas (e.g., la bola anaranjada hace mover a la bola púrpura) describen tanto los eventos directos como los indirectos. Basados en esta diferencia, se emplea estructuras causativas léxicas y perifrásticas como instrucciones verbales que dirigían a los participantes a juzgar si colisiones del tipo "bolas de billar" simulaban eventos causales directos, causales indirectos y no causales. Mediante el uso de resonancia magnética nuclear funcional (RMNf ) y el análisis por región de interés se encontró que los juicios sobre los tres tipos de eventos activaron más la región de Broca cuando eran precedidos por la instrucción perifrástica que cuando eran precedidos por la instrucción léxica. Por otra parte, los juicios sobre los eventos directos producían mayor actividad en el área de Broca que los eventos indirectos. Se encontró interesante que los juicios causales estuvieron asociados a actividad segregada en el pars opercularis y en el pars triangularis. Los resultados son discutid...

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Bayesian model selection maps for group studies

Cited by 84 publications

References 33 publications

Action Reprogramming in Parkinson's Disease: Response to Prediction Error Is Modulated by Levels of Dopamine

Action Reprogramming in Parkinson's Disease: Response to Prediction Error Is Modulated by Levels of Dopamine

Challenging a decade of brain research on task switching: Brain activation in the task‐switching paradigm reflects adaptation rather than reconfiguration of task sets

Broca's Area Activity in the Lexical Semantics of Visual Causal Events

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