Federico Adolfi
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View article: Interpretability requires interaction and integration of complexity-theoretic and experimental efforts
Interpretability requires interaction and integration of complexity-theoretic and experimental efforts Open
System opacity underlies many of the risks we currently worry about in AI and undermines many of the intended scientific applications. Understanding the conditions under which we can provably or otherwise reasonably guarantee that interpre…
View article: Content-agnostic online segmentation as a core operation
Content-agnostic online segmentation as a core operation Open
We approach the problem of explaining segmentation --- the human capacity to partition input streams into representations of appropriate form and content for efficient downstream processing --- by exploring a theoretically minimalistic and…
View article: From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences
From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences Open
Meta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific progress. While certainly useful to shed…
View article: The Computational Complexity of Circuit Discovery for Inner Interpretability
The Computational Complexity of Circuit Discovery for Inner Interpretability Open
Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of viab…
View article: Reclaiming AI as a Theoretical Tool for Cognitive Science
Reclaiming AI as a Theoretical Tool for Cognitive Science Open
The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with A…
View article: Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience Open
Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debated. Moreover, recent critiques raise issues that question i…
View article: MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments Open
Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses …
View article: From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences
From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences Open
Meta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific progress. While certainly useful to shed…
View article: Reclaiming AI as a theoretical tool for cognitive science
Reclaiming AI as a theoretical tool for cognitive science Open
The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with A…
View article: On the importance of severely testing deep learning models of cognition
On the importance of severely testing deep learning models of cognition Open
Researchers studying the correspondences between Deep Neural Networks (DNNs) and humans often give little consideration to severe testing when drawing conclusions from empirical findings, and this is impeding progress in building better mo…
View article: Clarifying status of DNNs as models of human vision
Clarifying status of DNNs as models of human vision Open
On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that…
View article: Introducing the MindSet benchmark for comparing DNNs to human vision
Introducing the MindSet benchmark for comparing DNNs to human vision Open
We describe the MindSet benchmark designed to facilitate the testing of DNNs against controlled experiments reported in psychology. MindSet will focus on a range of low-, middle-, and high-level visual findings that provide important const…
View article: Successes and critical failures of neural networks in capturing human-like speech recognition
Successes and critical failures of neural networks in capturing human-like speech recognition Open
Natural and artificial audition can in principle acquire different solutions to a given problem. The constraints of the task, however, can nudge the cognitive science and engineering of audition to qualitatively converge, suggesting that a…
View article: A Computational Complexity Perspective on Segmentation as a Cognitive Subcomputation
A Computational Complexity Perspective on Segmentation as a Cognitive Subcomputation Open
Computational feasibility is a widespread concern that guides the framing and modeling of natural and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about the s…
View article: Deep Problems with Neural Network Models of Human Vision
Deep Problems with Neural Network Models of Human Vision Open
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNN…
View article: Obstacles to inferring mechanistic similarity using Representational Similarity Analysis
Obstacles to inferring mechanistic similarity using Representational Similarity Analysis Open
Representational Similarity Analysis (RSA) is an innovative approach used to compare neural representations across individuals, species and computational models. Despite its popularity within neuroscience, psychology and artificial intelli…
View article: Successes and critical failures of neural networks in capturing human-like speech recognition
Successes and critical failures of neural networks in capturing human-like speech recognition Open
Natural and artificial audition can in principle acquire different solutions to a given problem. The constraints of the task, however, can nudge the cognitive science and engineering of audition to qualitatively converge, suggesting that a…
View article: Computational Complexity of Segmentation
Computational Complexity of Segmentation Open
Computational feasibility is a widespread concern that guides the framing and modeling of biological and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about th…
View article: Gibbs Sampling with People
Gibbs Sampling with People Open
A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Marko…
View article: Against the Epistemological Primacy of the Hardware: The Brain from Inside Out, Turned Upside Down
Against the Epistemological Primacy of the Hardware: The Brain from Inside Out, Turned Upside Down Open
Before he wrote the recent book The Brain from Inside Out , the neuroscientist György Buzsáki previewed some of the arguments in a paper written 20 years ago (“The brain-cognitive behavior problem: a retrospective”), now finally published.…
View article: Time to Face Language: Embodied Mechanisms Underpin the Inception of Face-Related Meanings in the Human Brain
Time to Face Language: Embodied Mechanisms Underpin the Inception of Face-Related Meanings in the Human Brain Open
In construing meaning, the brain recruits multimodal (conceptual) systems and embodied (modality-specific) mechanisms. Yet, no consensus exists on how crucial the latter are for the inception of semantic distinctions. To address this issue…
View article: Gibbs Sampling with People
Gibbs Sampling with People Open
Materials for paper.
View article: Dynamic neurocognitive changes in interoception after heart transplant
Dynamic neurocognitive changes in interoception after heart transplant Open
Heart–brain integration dynamics are critical for interoception (i.e. the sensing of body signals). In this unprecedented longitudinal study, we assessed neurocognitive markers of interoception in patients who underwent orthotopic heart tr…
View article: Altered neural signatures of interoception in multiple sclerosis
Altered neural signatures of interoception in multiple sclerosis Open
Multiple sclerosis (MS) patients present several alterations related to sensing of bodily signals. However, no specific neurocognitive impairment has yet been proposed as a core deficit underlying such symptoms. We aimed to determine wheth…
View article: Justify your alpha
Justify your alpha Open
View article: Justify Your Alpha
Justify Your Alpha Open
In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
View article: Justify Your Alpha
Justify Your Alpha Open
In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
View article: Attention, in and Out: Scalp-Level and Intracranial EEG Correlates of Interoception and Exteroception
Attention, in and Out: Scalp-Level and Intracranial EEG Correlates of Interoception and Exteroception Open
Interoception, the monitoring of visceral signals, is often presumed to engage attentional mechanisms specifically devoted to inner bodily sensing. In fact, most standardized interoceptive tasks require directing attention to internal sign…
View article: Enhanced Working Memory Binding by Direct Electrical Stimulation of the Parietal Cortex
Enhanced Working Memory Binding by Direct Electrical Stimulation of the Parietal Cortex Open
Recent works evince the critical role of visual short-term memory (STM) binding deficits as a clinical and preclinical marker of Alzheimer's disease (AD). These studies suggest a potential role of posterior brain regions in both the neuroc…
View article: Convergence of interoception, emotion, and social cognition: A twofold fMRI meta-analysis and lesion approach
Convergence of interoception, emotion, and social cognition: A twofold fMRI meta-analysis and lesion approach Open