Browse
Submit Data
Databases
API
Help

Dataset Information

9 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The critical role of interference control in metaphor comprehension evidenced by the drift-diffusion model.

ABSTRACT: We address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant's detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in metaphor comprehension using the drift-diffusion model, in an attempt to provide more systematic accounts of the processes underlying metaphor comprehension. Results showed that there were significant negative correlations between response times in metaphor comprehension and scores of the Controlled Oral Word Association Test (COWAT)-Semantic, suggesting that better performances in comprehending metaphors were strongly associated with better interference control. Using the drift-diffusion model, we found that the familiarity, compared to context, had greater leverage in the decision process for metaphor comprehension. Moreover, individuals with better performance in the COWAT-Semantic test demonstrated higher drift rates. In conclusion, with more fine-grained analysis of the decisions involved in metaphor comprehension using the drift-diffusion model, we argue that interference control plays an important role in processing metaphors.

SUBMITTER: Yoon HD

PROVIDER: S-EPMC8481255 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Testing the drift-diffusion model.

Project description:The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

| S-EPMC7776861 | biostudies-literature

The importance of being apt: metaphor comprehension in Alzheimer's disease.

Project description:We investigated the effect of aptness in the comprehension of copular metaphors (e.g., Lawyers are sharks) by Alzheimer's Disease (AD) patients. Aptness is the extent to which the vehicle (e.g., shark) captures salient properties of the topic (e.g., lawyers). A group of AD patients provided interpretations for metaphors that varied both in aptness and familiarity. Compared to healthy controls, AD patients produced worse interpretations, but interpretation ability was related to a metaphor's aptness rather than to its familiarity level, and patients with superior abstraction ability produced better interpretations. Therefore, the ability to construct figurative interpretations for metaphors is not always diminished in AD patients nor is it dependent only on the novelty level of the expression. We show that Alzheimer's patients' capacity to build figurative interpretations for metaphors is related to both item variables, such as aptness, and participant variables, such as abstraction ability.

| S-EPMC4251318 | biostudies-literature

Vicarious Rewards Modulate the Drift Rate of Evidence Accumulation From the Drift Diffusion Model.

Project description:Taking other people's interests into account is a fundamental ability allowing humans to maintain relationships. Yet, the mechanisms by which monetary incentives for close others influence perceptual decision-making processes remain elusive. Here, we compared perceptual decisions motivated by payoffs for oneself or a close relative. According to drift diffusion models (DDMs), perceptual decisions are made when sensory evidence accumulated over time - with a given drift rate - reaches one of the decision boundaries. We used these computational models to identify whether the drift rate of evidence accumulation or the decision boundary is affected by these two sources of motivation. Reaction times and sensitivity were modulated by three factors: the Difficulty (motion coherence of the moving dots), the Payoff associated with, and the Beneficiary of the decision. Reaction times (RTs) were faster for easy compared to difficult trials and faster for high payoffs as compared to low payoffs. More interestingly, RTs were also faster for self than for other-affecting decisions. Finally, using DDM, we found that these faster RTs were linked to a higher drift rate of the decision variable. This study offers a mechanistic understanding of how incentives for others and motion coherence influence decision-making processes.

| S-EPMC6614513 | biostudies-literature

The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making.

Project description:Perceptual decisions requiring the comparison of spatially distributed stimuli that are fixated sequentially might be influenced by fluctuations in visual attention. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple perceptual choices, and to test the extent to which the attentional Drift Diffusion Model (aDDM) provides a good computational description of how attention affects the underlying decision processes. We find evidence for sizable attentional choice biases and that the aDDM provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices and reaction times. We also find that exogenous manipulations of attention induce choice biases consistent with the predictions of the model.

| S-EPMC5573732 | biostudies-literature

Metaphor Comprehension in Individuals with Autism Spectrum Disorder: Core Language Skills Matter.

Project description:Poor metaphor comprehension was considered a hallmark of autism spectrum disorder (ASD), but recent research has questioned the extent and the sources of these difficulties. In this cross-sectional study, we compared metaphor comprehension in individuals with ASD (N = 29) and individuals with typical development (TD; N = 31), and investigated the relationship between core language and metaphor comprehension. Individuals with ASD showed more difficulty but also a more variable performance in both metaphor and literal items of the task used than individuals with TD did. This indicates that core language ability accounts for metaphor comprehension and should be considered in future research and interventions aiming to improve metaphor comprehension in individuals with ASD.

| S-EPMC8732923 | biostudies-literature

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

Project description:The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the ?(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

| S-EPMC3731670 | biostudies-literature

Decomposing Simon task BOLD activation using a drift-diffusion model framework.

Project description:The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks. In this study we use our freely available framework for fitting and comparing non-standard SSMs to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and a conflict counteraction parameter to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable. We find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.

| S-EPMC7054266 | biostudies-literature

The drift diffusion model as the choice rule in reinforcement learning.

Project description:Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

| S-EPMC5487295 | biostudies-literature

Drift in Diffusion Gradients.

Project description:The longstanding problem of Brownian transport in a heterogeneous quasi one-dimensional medium with space-dependent self-diffusion coefficient is addressed in the overdamped (zero mass) limit. A satisfactory mesoscopic description is obtained in the Langevin equation formalism by introducing an appropriate drift term, which depends on the system macroscopic observables, namely the diffuser concentration and current. The drift term is related to the microscopic properties of the medium. The paradoxical existence of a finite drift at zero current suggests the possibility of designing a Maxwell demon operating between two equilibrium reservoirs at the same temperature.

| S-EPMC5521325 | biostudies-other

Difficulty and pleasure in the comprehension of verb-based metaphor sentences: A behavioral study.

Project description:What is difficult is not usually pleasurable. Yet, for certain unfamiliar figurative language, like that which is common in poetry, while comprehension is often more difficult than for more conventional language, it is in many cases more pleasurable. Concentrating our investigation on verb-based metaphors, we examined whether and to what degree the novel variations (in the form of verb changes and extensions) of conventional verb metaphors were both more difficult to comprehend and yet induced more pleasure. To test this relationship, we developed a set of 62 familiar metaphor stimuli, each with corresponding optimal and excessive verb variation and metaphor extension conditions, and normed these stimuli using both objective measures and participant subjective ratings. We then tested the pleasure-difficulty relationship with an online behavioral study. Based on Rachel Giora and her colleagues' 'optimal innovation hypothesis', we anticipated an inverse U-shaped relationship between ease and pleasure, with an optimal degree of difficulty, introduced by metaphor variations, producing the highest degree of pleasure when compared to familiar or excessive conditions. Results, however, revealed a more complex picture, with only metaphor extension conditions (not verb variation conditions) producing the anticipated pleasure effects. Individual differences in semantic cognition and verbal reasoning assessed using the Semantic Similarities Test, while clearly influential, further complicated the pleasure-difficulty relationship, suggesting an important avenue for further investigation.

| S-EPMC8836342 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data