Static Internal Representation Of Dynamic Situations Reveals Time Compaction In Human Cognition
The time-changing nature of our world demands processing of huge amounts of information in fast and reliable way to generate successful behaviors. Therefore, significant brain resources are devoted to process spatiotemporal information. Neural basis of spatial processing and their cognitive correlates are well established mostly for static environments. Nonetheless, in time-changing situations the brain exploits specific processing mechanisms for temporal information based on prediction and anticipation, as time compression during visual perception and mental navigation. Alternative hypothesis of time compaction integrates both views, postulating that dynamic situations are internally represented as static spatial maps where temporal information is extracted by predicting and structuring the relevant interactions. Nevertheless, empirical approaches tackling the biological soundness of time compaction are still lacking. Here we show that performance in a discrimination learning task involving dynamic situations can be either favored or hampered via previous exposition to interfering static scenes. In this sense, men were effectively conditioned in contrast to a control group, in coherence with the hypothesis. Meanwhile, women performed on par with control men, regardless of the previous conditioning. This suggests time compaction is a salient cognitive strategy in men when dealing with dynamic situations, while women seem to rely on a broader range of information processing strategies. Finally, we further corroborated the time compaction mechanism involved in these experimental findings through a mathematical model of the experimental process. Our results point to some form of static internal representation mechanism at cognitive level involved in decision-making and strategy planning in dynamic situations [...]
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Contextualized representations (e.g. ELMo, BERT) have become the default pretrained representations for downstream NLP applications. In some settings, this transition has rendered their static embedding predecessors (e.g…
Word EmbeddingsFrom Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs
Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed through the interplay among persons, situations, and behaviors. Existing…
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective
Video contrastive learning (v-CL) has gained prominence as a leading framework for unsupervised video representation learning, showcasing impressive performance across various tasks such as action classification and dete…
Action ClassificationContrastive LearningRepresentation LearningRecursive Dynamics in Fast-Weights Homeostatic Reentry Networks: Toward Reflective Intelligence
This study introduces the Fast-Weights Homeostatic Reentry Layer (FH-RL), a neural mechanism that integrates fast-weight associative memory, homeostatic regularization, and learned reentrant feedback to approximate self-…
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
This study investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dyna…
Decision Making