Biology subjects

Ashby, F. G.

Publications and source records attributed to Ashby, F. G..

3 recordsLinked to original sources

bioRxiv2025

Context modulates brain state dynamics and behavioral responses during narrative comprehension

Narrative comprehension is inherently context-sensitive, yet the brain and cognitive mechanisms by which brief contextual priming shapes story interpretation remain unclear. Using hidden Markov modeling (HMM) of fMRI data, we identified dynamic brain states as participants listened to an ambiguous spoken story under two distinct narrative contexts (affair vs. paranoia). We identified recurrent states involving auditory, language, and default mode network (DMN) regions that were expressed across both groups, as well as additional states characterized by recruitment of multiple-demand network (MDN) systems, including control, dorsal attention, and salience networks. Bayesian mixed-effects modeling revealed that contextual framing modulated how specific linguistic and character-related features influenced the probability of occupying these states. Complementary behavioral data showed parallel context-sensitive modulation of participants moment-to-moment interpretive judgments. Together, these findings suggest that contextual priming influences narrative comprehension through subtle, feature-dependent adjustments in the engagement of DMN- and MDN-related brain states during naturalistic story listening.

neuroscience↗

bioRxiv2024

Criterial Learning and Feedback Delay: Insights from Computational Models and Behavioral Experiments

The notion of a response criterion is ubiquitous in psychology, yet its cognitive and neural underpinnings remain poorly understood. To address this shortcoming, three computational models that capture different hypotheses about criterial learning were developed and tested. The time-dependent drift model assumes the criterion is stored in working memory and that its value drifts over time. The delay-sensitive learning model assumes that the magnitude of criterial learning is temporally discounted by feedback delay. The reinforcement-learning model assumes that criterial learning emerges from stimulus-response association learning without an explicit representation of the criterion, with learning rate also temporally discounted by feedback delay. The performance of these models was investigated under varying feedback delay and intertrial interval (ITI) durations. The time-dependent drift model predicted that long ITIs and feedback delays both impair criterial learning. In contrast, the delay-sensitive and reinforcement-learning models predicted impairments only with feedback delays. Two behavioral experiments, which tested these predictions, showed that human criterial learning is impaired by delayed feedback but not by long ITIs. These results support the delay-sensitive and reinforcement-learning models, and suggest that even in tasks that appear to rely on explicit, rule-based reasoning, criterial learning may have strong associative underpinnings.

neuroscience↗

bioRxiv2018

Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data

Many research questions in visual perception involve determining whether stimulus properties are represented and processed independently. In visual neuroscience, there is great interest in determining whether important object dimensions are represented independently in the brain. For example, theories of face recognition have proposed either completely or partially independent processing of identity and emotional expression. Unfortunately, most previous research has only vaguely defined what is meant by \"independence,\" which hinders its precise quantification and testing. This article develops a new quantitative framework that links signal detection theory from psychophysics and encoding models from computational neuroscience, focusing on a special form of independence defined in the psychophysics literature: perceptual separability. The new theory allowed us, for the first time, to precisely define separability of neural representations and to theoretically link behavioral and brain measures of separability. The framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach. In particular, the theory identifies exactly what valid inferences can be made about independent encoding of stimulus dimensions from the results of multivariate analyses of neuroimaging data and psychophysical studies. In addition, commonly used operational tests of independence are re-interpreted within this new theoretical framework, providing insights on their correct use and interpretation. Finally, we apply this new framework to the study of separability of brain representations of face identity and emotional expression (neutral/sad) in a human fMRI study with male and female participants.\n\nAuthor SummaryA common question in vision research is whether certain stimulus properties, like face identity and expression, are represented and processed independently. We develop a theoretical framework that allowed us, for the first time, to link behavioral and brain measures of independence. Unlike previous approaches, our framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach in the study of independence. This allows to identify what kind of inferences can be made about brain representations from multivariate analyses of neuroimaging data or psychophysical studies. We apply this framework to the study of independent processing of face identity and expression.

neuroscience↗