Technical Program: Paper Detail || CCN 2022 || 2022 Conference on Cognitive Computational Neuroscience || San Francisco || August 25 - 28, 2022

P-3.30

Fixation duration variability increases with mind wandering during scene viewing

Kevin O'Neill, Kristina Krasich, Felipe De Brigard, Duke University, United States; Samuel Murray, Providence College, United States; James Brockmole, University of Notre Dame, United States; Antje Nuthmann, Kiel University, Germany

Session:

Posters 3 Poster

Location:

Pacific Ballroom H-O

Presentation Time:

Sat, 27 Aug, 19:30 - 21:30 Pacific Time (UTC -8)

Abstract:

In scene viewing, both increased visual-cognitive processing demands and mind wandering have been associated with longer fixation durations. To better understand how the same behavioral phenomenon reflects seemingly incompatible states of visual-cognitive processing, we ran simulations using an established random-walk model for saccade timing and programming to assess which model parameters best predicted modulations in fixation durations associated with mind wandering compared to attentive viewing. Mind wandering-related fixation durations reflected an increase in the variability of the fixation-generating process. In contrast, past research showed that increased processing demands increased the mean duration of the fixation-generating process. Thus, we showed that mind wandering and increased visual-cognitive processing demands modulate fixation durations through different mechanisms.

Manuscript:

View Manuscript

License:

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

DOI:

10.32470/CCN.2022.1194-0

Publication:

2022 Conference on Cognitive Computational Neuroscience

Session P-3

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P-3.2: Voxel-wise Encoding Models with Hierarchical Task-optimized Brain Atlas

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P-3.3: Hidden knobs: Representations for flexible goal-directed decision-making

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P-3.4: AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games

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P-3.5: Intrinsic ionic dynamics, oscillations, and resonance are reflected in and can be extracted from neuronal spike-train cross-correlations

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P-3.6: Prediction of brain regions from single channel ECoG signals by deep learning

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P-3.7: Image Embeddings Informed by Natural Language Significantly Improve Predictions and Understanding of Human Higher-level Visual Cortex

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P-3.8: An Efficient Search for Novel Behavioral Strategies in a Vast Program Space

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P-3.9: The Individualized Neural Tuning Model: Precise and generalizable cartography of functional architecture in individual brains

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P-3.12: The best advice you can give.

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P-3.13: Modality specificity and generality in the hierarchical levels of cognitive control

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P-3.14: Sequential object-based attention for robust visual reasoning

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P-3.15: Deep convolutional neural networks fail to classify images ‘in the wild’

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P-3.16: Modeling pain in the brain with conditional variational autoencoder

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P-3.18: Using Massive Individual fMRI Movie Data to Align Artificial and Brain Representations in an Auditory Network

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P-3.19: Conversion of ConvNets to Spiking Neural Networks With Less Than One Spike per Neuron

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P-3.20: Brain-to-Brain Linguistic Coupling in Natural Conversations

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P-3.21: Optimal encoding of prior information in noisy working memory systems

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P-3.22: Angular gyrus responses show joint statistical dependence with brain regions selective for different categories

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P-3.23: Different computational strategies for different reinforcement learning problems

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P-3.24: Investigating individual differences in structure learning

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P-3.26: Revealing dimensions underlying the organization of observed actions

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P-3.27: Human-like capacity limitation in multi-system models of working memory

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P-3.29: Do Convolutional Neural Networks Model Inferior Temporal Cortex Because of Perceptual or Semantic Features?

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P-3.30: Fixation duration variability increases with mind wandering during scene viewing

Kevin O'Neill, Kristina Krasich, Felipe De Brigard, Duke University, United States; Samuel Murray, Providence College, United States; James Brockmole, University of Notre Dame, United States; Antje Nuthmann, Kiel University, Germany

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P-3.33: Do deep convolutional neural networks accurately model representations beyond the ventral stream?

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P-3.34: Rationalizing Behavior during Virtual Reality Navigation

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P-3.35: Neural Mechanisms of Credit Assignment for Inferred Relationships in a Structured World

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P-3.36: Developmental differences in social brain responses during movie viewing

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P-3.37: Transfer learning in a 3D-CNN is beneficial for small sample sizes in HCP task data

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P-3.38: Gaze-centered spatial representations in human hippocampus

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P-3.39: Large Scale Resting-State Network Connectivities Predict Verbal Suggestibility

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P-3.41: Lateral Inhibition Facilitates Sequential Learning in a Hippocampus-Inspired Auto-Associator

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P-3.42: Latent dimensionality scales with the performance of deep learning models of visual cortex

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P-3.43: Towards Precise and Robust Hippocampus Segmentation using Self-Supervised Contrastive Learning

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P-3.44: Effects of Predictability and Controllability on Pain Perception

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P-3.45: A mathematical framework for bridging Marr’s levels

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P-3.46: Precision-weighted evidence integration predicts time-varying influence of memory on perceptual decisions

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P-3.47: How Well Do Contrastive Learning Algorithms Model Human Real-time and Life-long Learning?

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P-3.48: Different Brain Mechanisms of Time Estimation Depending on Situational Information

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P-3.50: A New Computational Framework for Estimating Spatio-temporal Population Receptive Fields in Human Visual Cortex

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P-3.51: Economically expanding internal models in human density estimation

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P-3.52: The geometry of cognitive maps under dynamic cognitive control

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P-3.53: Reconstruction of line illusion from human brain activity

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P-3.54: Statistical inference on representational geometries

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P-3.55: Network Architecture of Cortex and Cerebellum for Supporting Super-learning

Serhat Çağdaş, Yalova University, Turkey; Ismail Akturk, Ozyegin University, Turkey; N. Serap Şengör, İstanbul Technical University, Turkey

P-3.56: Humans learning a complex task are picky and sticky

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P-3.57: Leaving alternatives behind: A theoretical and experimental investigation of the role of mutual inhibition in shaping choice

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P-3.58: Orthogonal neural encoding of targets and distractors supports cognitive control

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P-3.59: Identifying transfer learning in the reshaping of inductive biases

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P-3.60: The Neural Representation of Real-World Object Size in Natural Images

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P-3.62: Time cell encoding is decoupled from time perception in deep reinforcement learning agents

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P-3.63: Syntax in working memory using a simple plastic attractor

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P-3.64: Relating covariability in visual cortex to natural image statistics

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P-3.65: Learning efficient attractor-based working memory representations in heterogeneous environments

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P-3.66: Attractor dynamics account for decision uncertainty in macaque prefrontal cortex

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P-3.67: A Characterization of the Neural Representation of Confidence during Probabilistic Learning

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P-3.68: Representational dynamics of listened and imagined musical sound sequences

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P-3.69: Analysis of Transformer attention in EEG signal classification

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P-3.70: Superstitious learning of abstract order from random reinforcement

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P-3.72: Probing population codes and circuit dynamics of probabilistic learning

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