Search Results - "Conwell, Colin"

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines by Conwell, Colin, Prince, Jacob S., Kay, Kendrick N., Alvarez, George A., Konkle, Talia

“…The rapid release of high-performing computer vision models offers new potential to study the impact of different inductive biases on the emergent brain…”
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A Signature of Orientation Invariance in Human fMRI & (Some) Deep Neural Networks by Conwell, Colin, Alvarez, George

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Is Rodent Visual Cortex Really Just a Randomly Initialized Neural Network? by Conwell, Colin, Alvarez, George

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Large datasets: a Swiss Army knife for diverse research aims in neuroAI by Prince, Jacob, Conwell, Colin, Konkle, Talia

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Your Visual System (Probably) Knows More Physics than You Do by Conwell, Colin, Alvarez, George

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Sparse null codes emerge and dominate representations in deep neural network vision models by Robinson, Brian S., Drenkow, Nathan, Conwell, Colin, Bonner, Michael F.

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Unveiling core, interpretable image properties underlying model-brain similarity with generative models by Rong, Yingqi, Conwell, Colin, Hidalgo, Dianna, Bonner, Michael

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Leveling the Field: Comparing the Visual Perception of Stability across Humans and Machines by Conwell, Colin, Alvarez, George A

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Language Models of Visual Cortex: Where do they work? And why do they work so well where they do? by Conwell, Colin, Prince, Jacob S., Alvarez, George A., Konkle, Talia

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What can 5.17 billion regression fits tell us about the representational format of the high-level human visual system? by Konkle, Talia, Conwell, Colin, Prince, Jacob S., Alvarez, George A.

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Purely Perceptual Machines Robustly Predict Human Visual Arousal, Valence, and Aesthetics by Conwell, Colin, Graham, Daniel, Konkle, Talia, Vessel, Edward

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Large-scale Deep Neural Network Benchmarking in Dynamic Social Vision by Garcia, Kathy, Conwell, Colin, McMahon, Emalie, Bonner, Michael F., Isik, Leyla

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Language model prediction of visual cortex responses to dynamic social scenes by McMahon, Emalie, Conwell, Colin, Garcia, Kathy, Bonner, Michael F., Isik, Leyla

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Intertemporal choice reflects value comparison rather than self-control: insights from confidence judgements by Bulley, Adam, Lempert, Karolina M, Conwell, Colin, Irish, Muireann, Schacter, Daniel L

“…Intertemporal decision-making has long been assumed to measure self-control, with prominent theories treating choices of smaller, sooner rewards as failed…”
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Is visual cortex really “language-aligned”? Perspectives from Model-to-Brain Comparisons in Human and Monkeys on the Natural Scenes Dataset by Conwell, Colin, McMahon, Emalie, Vinken, Kasper, Prince, Jacob S., Alvarez, George, Konkle, Talia, Isik, Leyla, Livingstone, Margaret

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Uninstructed Structure: Self-Supervised Deep Learning as a Steppingstone to Object Invariance, Intuitive Physics, and the Experience of Beauty by Conwell, Colin

“…The fundamental first step of any externally grounded psychological experience is the rapid organization of richly shaped, but largely indecipherable sensory…”
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Intrinsic curiosity may give rise to animate attention by De Freitas, Julian, Kim, Kun Ho, Haber, Nick, Conwell, Colin, Alvarez, George A, Yamins, Daniel L.K.

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Testing Relational Understanding in Text-Guided Image Generation by Conwell, Colin, Ullman, Tomer

“…Relations are basic building blocks of human cognition. Classic and recent work suggests that many relations are early developing, and quickly perceived…”
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Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics by Conwell, Colin, Hamblin, Christopher, Boccagno, Chelsea, Mayo, David, Cummings, Jesse, Isik, Leyla, Barbu, Andrei

“…When we experience a visual stimulus as beautiful, how much of that experience derives from perceptual computations we cannot describe versus conceptual…”
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Training the Untrainable: Introducing Inductive Bias via Representational Alignment by Subramaniam, Vighnesh, Mayo, David, Conwell, Colin, Poggio, Tomaso, Katz, Boris, Cheung, Brian, Barbu, Andrei

“…We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another…”
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