Sebastian Musslick
Datos Biográficos
| ID | 1751470 |
|---|---|
| NOMBRE | Sebastian Musslick |
| NOMBRES | Sebastian |
| APELLIDO | Musslick |
| FIRMA | MUSSLICK S |
| AFILIACIONES | Osnabrück University |
| ORCID | 0000-0002-8896-639X |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 5 |
| TOTAL DE CITAS | 1 |
| OBRAS COMO AUTOR | 5 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2017 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 1 |
Curriculum Learning in Humans and Neural Networks
The sequencing of training trials can significantly influence learning outcomes in humans and neural networks. However, studies comparing the effects of training curricula between the two have typically focused on the acquisition of multiple tasks. Here, we investigate curriculum learning in a single perceptual decision-making task, examining whether the behavior of a parsimonious network trained on different curricula would be replicated in huma…
Intelligent Tutoring Systems Need Teachers
Task performance errors and rewards affect voluntary task choices
Humans are remarkably flexible in adapting their behavior to current demands. It has been suggested that the decision which of multiple tasks to perform is based on a variety of factors pertaining to the rewards associated with each task as well as task performance (e.g., error rates associated with each task and/or error commission on the previous trial). However, further empirical investigation is needed to examine whether task performance stil…
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Toward a Rational and Mechanistic Account of Mental Effort
In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate…
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Toward a Rational and Mechanistic Account of Mental Effort
In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate…
Task performance errors and rewards affect voluntary task choices
Humans are remarkably flexible in adapting their behavior to current demands. It has been suggested that the decision which of multiple tasks to perform is based on a variety of factors pertaining to the rewards associated with each task as well as task performance (e.g., error rates associated with each task and/or error commission on the previous trial). However, further empirical investigation is needed to examine whether task performance stil…
Large language models surpass human experts in predicting neuroscience results
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predi…
Curriculum Learning in Humans and Neural Networks
The sequencing of training trials can significantly influence learning outcomes in humans and neural networks. However, studies comparing the effects of training curricula between the two have typically focused on the acquisition of multiple tasks. Here, we investigate curriculum learning in a single perceptual decision-making task, examining whether the behavior of a parsimonious network trained on different curricula would be replicated in huma…
Intelligent Tutoring Systems Need Teachers
Psychology (4 obras) · Cognitive psychology (2 obras) · Cognitive science (2 obras) · Computer Science (2 obras) · Decision-Making and Behavioral Economics (2 obras) · Neural and Behavioral Psychology Studies (2 obras) · Neuroscience (2 obras) · Online Learning and Analytics (2 obras) · Academic achievement (1 obra) · Artificial Intelligence (1 obra)