Technical Program: Paper Detail || CCN 2023 || 2023 Conference on Cognitive Computational Neuroscience || Oxford, UK || August 24 - 27, 2023

P-2B.110

Biases towards compositionally simpler hypotheses are robust and unaffected by learning

Valerio Rubino, University of Trento, Italy; Peter Dayan, Charley Wu, University of Tubingen, Germany

Session:

Posters 2B Poster

Location:

Marquee

Presentation Time:

Fri, 25 Aug, 13:00 - 15:00 United Kingdom Time

Abstract:

Compositionality is an important and yet poorly understood feature of human behaviour. In this study, participants navigated mazes with hidden, compositional structure, which were generated using operations over spatial primitives. Although they were not informed about the underlying structure, participants improved their accuracy and decreased primitive-inconsistent actions over the course of the task. Participants also selectively tested hypothesis corresponding to compositionally simpler expectations (simplicity bias), with a large proportion of errors due to expecting greater compositional structure than present in the true path. However, this simplicity bias did not change over the course of the experiment, and remained robust throughout. These results suggest that the human bias towards simplicity is unaffected by experience with compositional structure, at least in the time-frame of our experiment.

Manuscript:

View Manuscript

License:

Creative Commons License

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

DOI:

10.32470/CCN.2023.1392-0

Publication:

2023 Conference on Cognitive Computational Neuroscience

Session P-2B

P-2B.61: Characterising representation dynamics in recurrent neural networks for object recognition

Sushrut Thorat, Adrien Doerig, Tim Kietzmann, Osnabrück University, Germany

P-2B.62: A Grid Cell-Place Cell Scaffold Allows Rapid Learning and Generalization at Multiple Levels on Mental Navigation Tasks

Jaedong Hwang, Sujaya Neupane, Mehrdad Jazayeri, Ila Fiete, MIT, United States

P-2B.63: Neural correlates of uncertainty- and reward-based strategies for directed exploration

Alexander Paunov, Maëva L’Hôtellier, INSERM / CEA / University Paris-Saclay, France; Dalin Guo, Zoe He, University of California San Diego, United States; Angela Yu, Technical University of Darmstadt, Germany; Florent Meyniel, INSERM / CEA / University Paris-Saclay, France

P-2B.64: Learning from Disinformation

Juan Vidal-Perez, Raymond J. Dolan, UCL, United Kingdom; Rani Moran, Queen Mary University of London, United Kingdom

P-2B.65: Speech features are weighted by selective attention

Nika Jurov, Grayson Wolf, William Idsardi, Naomi Feldman, University of Maryland, United States

P-2B.66: Distilling the neural code for word recognition

Aakash Agrawal, Stanislas Dehaene, Neurospin,, France

P-2B.67: Interplay of episodic memory and recency effects in an odor-in-context association task

Nikolaos Chrysanthidis, Florian Fiebig, KTH Royal Institute of Technology, Sweden; Anders Lansner, KTH Royal Institute of Technology, Stockholm University, Sweden; Pawel Herman, KTH Royal Institute of Technology, Digital Futures, Sweden

P-2B.68: An Interplay of Nucleus Accumbens and Anterior Insula Activity Predicts Risky Choice

Leili Mortazavi, Brian Knutson, Stanford University, United States

P-2B.69: Unravelling the computational mechanisms underlying choice history biases.

Robin Vloeberghs, KU Leuven, Belgium; Anne Urai, Leiden University, Netherlands; Kobe Desender, KU Leuven, Belgium

P-2B.70: Lazy Learning: a Biologically-Inspired Plasticity Rule for Fast and Energy Efficient Synaptic Plasticity

Aaron Pache, Mark van Rossum, University of Nottingham, United Kingdom

P-2B.71: EEG-fMRI fusion analysis of attention and visual working memory

Viljami Salmela, University of Helsinki, Finland; Lijing Guo, Sichuan Normal University, China; Kimmo Alho, University of Helsinki, Finland; Chaoxiong Ye, University of Jyväskylä, Finland

P-2B.72: Predictive Coding Meets Deep Learning: Learning By Predicting Representations

Ibrahim Hashim, Mario Senden, Rainer Goebel, Maastricht University, Netherlands

P-2B.73: Enumerating and discovering highly discriminative tasks for probing the cognitive architecture underlying complex behavior

Tzuhsuan Ma, Rishika Mohanta, Glenn Turner, Ann Hermundstad, HHMI Janelia Research Campus, United States

P-2B.74: Common neural choice signals emerge artifactually amidst multiple distinct value signals

Romy Froemer, University of Birmingham, United Kingdom; Matthew R. Nassar, Brown University, United States; Benedikt V. Ehinger, University of Stuttgart, Germany; Amitai Shenhav, Brown University, United States

P-2B.75: Leveraging Artificial Neural Networks to Enhance Diagnostic Efficiency in Autism Spectrum Disorder: A Study on Facial Emotion Recognition

Kushin Mukherjee, University of Wisconsin-Madison, United States; Na Yeon Kim, California Institute of Technology, United States; Shirin Taghian Alamooti, York University, Canada; Ralph Adolphs, California Institite of Technology, United States; Kohitij Kar, York University, Canada

P-2B.76: Probing Brain Context-Sensitivity with Masked-Attention Generation

Alexandre Pasquiou, Inria, France; Yair Lakretz, INSERM, CEA, France; Bertrand Thirion, Inria, France; Christophe Pallier, INSERM, CEA, France

P-2B.77: Generic Discriminative Features Exhibit Signatures of Human Numerical Perception

Daniel Janini, Talia Konkle, George Alvarez, Harvard University, United States

P-2B.78: Face-voice –integration in dorsal STS

Ilkka Muukkonen, Viljami Salmela, University of Helsinki, Finland

P-2B.79: Optimal feedback control under uncertainty explains errors, variability and behavioral strategies in human navigation

Fabian Kessler, Julia Frankenstein, Constantin A. Rothkopf, TU Darmstadt, Germany

P-2B.80: Allocentric representation of goal locations at reward-time but not during navigation in mouse mPFC

Peter Doohan, Beatriz Godinho, Mark Walton, Timothy Behrens, Thomas Akam, University of Oxford, United Kingdom

P-2B.81: Hierarchical Planning using Active Inference and Successor Representations

Prashant Rangarajan, Rajesh P. N. Rao, University of Washington, United States

P-2B.82: Abstract representations encoded by human hippocampal neurons support behavioral inference and can be induced through verbal instruction.

Hristos Courellis, Juri Minxha, California Institute of Technology, United States; Araceli Cardenas, Taufik Valiante, Toronto Western Hospital, Canada; Adam Mamelak, Cedars-Sinai Medical Center, United States; Ralph Adolphs, California Institute of Technology, United States; Stefano Fusi, Columbia University, United States; Ueli Rutishauser, California Institute of Technology, United States

P-2B.83: A visuospatial reference frame structures perceptual and memory interactions

Adam Steel, Dartmouth College, United States; Edward Silson, University of Edinburgh, United Kingdom; Brenda Garcia, Caroline Robertson, Dartmouth College, United States

P-2B.84: Two determinants of adaptability in learning magnitudes and probabilities

Cedric Foucault, Florent Meyniel, University of Paris-Saclay, France

P-2B.85: A Hidden Markov Model Reveals Neural Correlates of Motivation-States in Monkeys

Luke Priestley, Matthew Rushworth, Nima Khalighinejad, University of Oxford, United Kingdom

P-2B.86: The Elusive Relationship between Mental Health Profiles and Valence-related Biases in Reinforcement Learning

Zoe Koopmans, Sophie Bavard, Stefano Palminteri, École normale supérieure, France

P-2B.87: Selective memory for reward-relevant features is modulated by expertise during reward learning

Yirong Xiong, University of Tübingen, Germany; Nir Moneta, Max Planck Institute for Human Development, Germany; Mihaly Banyai, Max Planck Institute for Biological Cybernetics, Germany; Charley Wu, University of Tübingen, Germany

P-2B.88: Structured Credit Assignment in Mice

Kevin Miller, DeepMind and University College London, United Kingdom; Laurence Freeman, Yu Jin Oh, University College London, United Kingdom; Matthew Botvinick, DeepMind, United Kingdom; Kenneth Harris, University College London, United Kingdom

P-2B.89: Evidence for macroscopic traveling waves from sEEG

David Alexander, Laura Dugué, Université Paris Cité, CNRS, France

P-2B.90: Revisiting the orthographic prediction error for a better understanding of efficient visual word recognition

Wanlu Fu, Benjamin Gagl, University of Cologne, Germany

P-2B.91: Diagnosing Catastrophe: Large Parts of Accuracy Loss in Continual Learning Can Be Accounted for by Readout Misalignment

Daniel Anthes, Sushrut Thorat, Peter König, Tim Kietzmann, Osnabrück University, Germany

P-2B.92: Using Generated Object Reconstructions to Study Object-based Attention

Seoyoung Ahn, Hossein Adeli, Gregory Zelinsky, Stony Brook University, United States

P-2B.93: Humans and CNNs see differently: Action affordances are represented in scene-selective visual cortex but not CNNs

Clemens G. Bartnik, Iris I.A. Groen, University of Amsterdam, Netherlands

P-2B.94: Mental Imagery: Weak Vision or Compressed Vision?

Tiasha Saha Roy, Jesse Breedlove, Ghislain St-Yves, Kendrick Kay, Thomas Naselaris, University of Minnesota, United States

P-2B.95: Dimensions That Matter – Interpretable Object Dimensions in Humans and Deep Neural Networks

Florian Mahner, Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Lukas Muttenthaler, TU Berlin, Germany; Umut Güçlü, Donders Institute for Brain, Cognition and Behaviour, Netherlands; Martin Hebart, Max Planck Institute for Human Cognitive and Brain Sciences, Germany

P-2B.96: Consistency or Fluctuations? How Humans Manage Limited Search Capacity Over Consecutive Uncertain Choices

Alice Vidal, Salvador Soto-Faraco, Ruben Moreno-Bote, Universitat Pompeu Fabra, Spain

P-2B.97: Understanding Learning Trajectories With Infinite Hidden Markov Models

Sebastian Bruijns, Max-Planck-Institute Tübingen, Germany; International Brain Laboratory, International Brain Laboratory, Germany; Peter Dayan, Max-Planck-Institute Tübingen, Germany

P-2B.98: Empirically Identifying and Computationally Modelling the Brain-Behaviour Relationship for Human Scene Categorization

Agnessa Karapetian, Antoniya Boyanova, Freie Universität Berlin, Germany; Muthukumar Pandaram, Bernstein Centre for Computational Neuroscience Berlin, Germany; Klaus Obermayer, Technische Universität Berlin, Germany; Tim C. Kietzmann, Universität Osnabrück, Germany; Radoslaw M. Cichy, Freie Universität Berlin, Germany

P-2B.100: Softer labels support more robust generalization

Ruairidh Battleday, Ilia Sucholutsky, Princeton University, United States; Katherine Collins, Adrian Weller, University of Cambridge, United Kingdom; Thomas Griffiths, Princeton University, United States

P-2B.101: Simulating the Scaling of Long-Term Memory Retrieval

Susanne Haridi, Mirko Thalmann, Eric Schulz, Max-Planck Institute for Biological Cybernetics, Germany

P-2B.102: CHASE – A novel neurocomputational approach to assessing mentalization capabilities

Niklas Bürgi, Gökhan Aydogan, University of Zurich, Switzerland; Arkady Konovalov, University of Birmingham, United Kingdom; Christian Ruff, University of Zurich, Switzerland

P-2B.103: Effect of hypertension duration on white matter structure and its link with cognition

Zilin Li, Beijing Normal University, China

P-2B.104: One bottleneck is not enough

Mani Hamidi, Mihaly Banyai, Charley Wu, University of TÜBINGEN, Germany

P-2B.105: Monte Carlo Predictive Coding: Representing the Posterior Distribution of Latent States in Predictive Coding Networks

Gaspard Oliviers, Rafal Bogacz, University of Oxford, United Kingdom; Alexander Meulemans, ETH Zurich, Switzerland

P-2B.106: Representational learning in forming relational memory

Quinn Lin, Beijing Normal University, China; Wei Ding, Jia Liu, Tsinghua University, China

P-2B.107: Synergies between Synaptic Working Memory Mechanisms in Spiking Network Models

Florian Fiebig, Nikolaos Chrysanthidis, Anders Lansner, Pawel Herman, KTH Royal Institute of Technology, Sweden

P-2B.108: Precision mapping of human extra-striate visual cortex with high-resolution fMRI inter-subject alignment

Alexis Thual, CEA Neurospin, Inria Saclay, France; Thomas Dighiero-Brecht, Minye Zhan, CEA Neurospin, France; Bertrand Thirion, Inria Saclay, CEA Neurospin, France; Stanislas Dehaene, CEA Neurospin, France

P-2B.109: Feature-disentangled reconstruction of perception from multi-unit recordings

Thirza Dado, Radboud University, Netherlands; Paolo Papale, Antonio Lozano, Netherlands Institute for Neuroscience, Netherlands; Lynn Le, Marcel van Gerven, Radboud University, Netherlands; Pieter Roelfsema, Netherlands Institute for Neuroscience, Netherlands; Yağmur Güçlütürk, Umut Güçlü, Radboud University, Netherlands

P-2B.110: Biases towards compositionally simpler hypotheses are robust and unaffected by learning

Valerio Rubino, University of Trento, Italy; Peter Dayan, Charley Wu, University of Tubingen, Germany

P-2B.111: What aspects of NLP models and brain datasets affect brain-NLP alignment?

SUBBA REDDY OOTA, Inria Bordeaux, France; Mariya Toneva, MPI for Software Systems, Germany

P-2B.112: Normative Modeling of Auditory Memory for Natural Sounds

Bryan Medina, Josh McDermott, Massachusetts Institute of Technology (MIT), United States

P-2B.113: A computational shortcut to coordination: common knowledge and neural alignment

Cong Wang, Lusha Zhu, Peking University, China

P-2B.114: Learning progress and uncompensated rewards as motivational drivers of engagement

Franziska Brändle, Max Planck Institute for Biological Cybernetics, Germany; Charley M. Wu, University of Tübingen, Germany; Eric Schulz, Max Planck Institute for Biological Cybernetics, Germany

P-2B.115: The Dynamic Nature of Procrastination

Peiyuan Zhang, New York University, United States; Yijun Lin, University of Florida, United States; Falk Lieder, Max Planck Institute for Intelligent Systems, Germany; Wei Ji Ma, New York University, United States

P-2B.116: Self-supervised transformers predict dynamics of object-based attention in humans

Hossein Adeli, Seoyoung Ahn, Stony Brook University, United States; Nikolaus Kriegeskorte, Columbia University, United States; Gregory Zelinsky, Stony Brook University, United States

P-2B.117: Latent space decomposition supports efficient less-than-one-shot learning

Ilia Sucholutsky, Thomas L. Griffiths, Princeton University, United States

P-2B.118: A Hierarchical Structure for Perceptual Awareness in the Human Brain

Nadine Dijkstra, Oliver Warrington, Peter Kok, Stephen Fleming, University College London, United Kingdom

P-2B.119: An architecture for zero-shot decision-making using plastic attractors

Sanjay Manohar, Oxford, United Kingdom; Christopher Whyte, Cambridge, United Kingdom; Eva Feredoes, Reading, United Kingdom; Alexandra Woolgar, Cambridge, United Kingdom

P-2B.120: Brain signal variability tracks uncertainty in Bayesian inference

Alexander Skowron, Douglas D. Garrett, Max Planck Institute for Human Development, Germany

©2024 CCN. All rights reserved.

Last updated Last updated 22 August 2023.