Biology subjects

Hodapp, A.

Publications and source records attributed to Hodapp, A..

5 recordsLinked to original sources

bioRxiv2025

Neuromodulatory systems partially account for the topography of cortical networks of learning under uncertainty

Learning in dynamic and stochastic environments is notoriously difficult. Neuromodulatory systems may shape this process, thereby constraining where learning-related neural activity emerges according to the spatial distribution of receptors and transporters across the brain. However, the extent of this constraint, and which neuromodulatory systems contribute the most, remains unclear. Here, we focus on fMRI data from four probabilistic learning studies that was combined with a Bayesian ideal observer model. This model formalizes latent computational variables such as confidence and surprise that drive human learning. We show that the functional correlates of confidence, and to a lesser extent surprise, exhibit strong spatial invariance across tasks. This invariance suggests that these functional correlates of learning reflect a stable cortical organization largely independent of sensory modality and task structure. We found that this invariance aligns with the cortical chemoarchitecture using 20 PET-derived receptor and transporter density maps. Distinct receptor and transporter patterns mapped onto confidence- and surprise-related activity, including both known catecholamine pathways and novel opioid associations. Together, our findings provide evidence for a neuromodulatory account of adaptive learning and offer receptor-level hypotheses. More broadly, our approach provides a general framework to understand how neuromodulatory systems shape diverse cognitive processes.

neuroscience↗

bioRxiv2024

Bayesian semantic surprise based on different types of regularities predicts the N400 and P600 brain signals

The brains remarkable ability to extract patterns from sequences of events has been demonstrated across cognitive domains and is a central assumption of predictive processing theories. While predictions shape language processing at the level of meaning, little is known about the underlying learning mechanism. Here, we investigated how continuous statistical inference in a semantic sequence influences the neural response. 60 participants were presented with a semantic oddball-like roving paradigm, consisting of sequences of nouns from different semantic categories. Unknown to the participants, the overall sequence contained an additional manipulation of transition probability between categories. Two Bayesian sequential learner models that captured different aspects of probabilistic learning were used to derive theoretical surprise levels for each trial and investigate online probabilistic semantic learning. The N400 ERP component was primarily modulated by increased probability with repeated exposure to the categories throughout the experiment, which essentially represents repetition suppression. This N400 repetition suppression likely prevented sizeable influences of more complex predictions such as those based on transition probability, as any incoming information was already continuously active in semantic memory. In contrast, the P600 was associated with semantic surprise in a transition probability model over recent observations, possibly indicating a working memory update in response to violations of these conditional dependencies. The results support probabilistic predictive processing of semantic information and demonstrate that continuous update of distinct statistics differentially influences language related ERPs.

neuroscience↗

bioRxiv2023

Error-based implicit learning in language: the effect of sentence context and constraint in a repetition paradigm

Prediction errors drive implicit learning in language, but the specific mechanisms underlying these effects remain debated. This issue was addressed in an electroencephalogram (EEG) study manipulating the context of a repeated unpredictable word (repetition of the complete sentence or repetition of the word in a new sentence context) and sentence constraint. For the manipulation of sentence constraint, unexpected words were presented either in high constraint (eliciting a precise prediction) or low constraint sentences (not eliciting any specific prediction). Repetition induced reduction of N400 amplitudes and of power in the alpha/beta frequency band was larger for words repeated with their sentence context as compared to words repeated in a new low constraint context, suggesting that implicit learning happens not only at the level of individual items but additionally improves sentence-based predictions. These processing benefits for repeated sentences did not differ between constraint conditions, suggesting that sentence-based prediction update might be proportional to the amount of unpredicted semantic information, rather than to the precision of the prediction that was violated. Additionally, the consequences of high constraint prediction violations, as reflected in a frontal positivity and increased theta band power, were reduced with repetition. Overall, our findings suggest a powerful and specific adaptation mechanism that allows the language system to quickly adapt its predictions when unexpected semantic information is processed, irrespective of sentence constraint, and to reduce potential costs of strong predictions that were violated.

neuroscience↗

bioRxiv2021

The N400 ERP component reflects a learning signal during language comprehension

The functional significance of the N400 ERP component is still actively debated. Based on neural network modeling it was recently proposed that the N400 component can be interpreted as the change in a probabilistic representation corresponding to an internal temporal-difference prediction error at the level of meaning that drives adaptation in language processing. These computational modeling results imply that increased N400 amplitudes should correspond to greater adaptation. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task, which influenced N400 amplitudes, and critically also later implicit memory for the manipulated word: reaction times in a perceptual identification task were significantly faster for previously unexpected words. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences also exhibited a larger implicit memory benefit. These findings support the interpretation of the N400 as an implicit learning signal in language processing.

neuroscience↗

bioRxiv2020

Opposing signatures of neural excitability and sensory input in initial cortical responses differentially predict intensity perception

Perception of sensory information is determined by stimulus features (e.g., intensity) and instantaneous neural states (e.g., excitability). Commonly, it is assumed that both are reflected similarly in evoked brain potentials, that is, higher evoked activity leads to a stronger percept of a stimulus. We tested this assumption in a somatosensory discrimination task in humans, simultaneously assessing (i) single-trial excitatory post-synaptic currents inferred from short-latency somatosensory evoked potentials (SEP), (ii) pre-stimulus alpha oscillations (8-13 Hz), and (iii) peripheral nerve measures. Fluctuations of neural excitability shaped the perceived stimulus intensity already during the very first cortical response (at [~]20 ms) yet demonstrating opposite neural signatures as compared to the effect of presented stimulus intensity. We reconcile this discrepancy via a common framework based on modulations of electro-chemical membrane gradients linking neural states and responses, which calls for reconsidering conventional interpretations of brain potential magnitudes in stimulus intensity encoding.

neuroscience↗