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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
neuroscienceworking memoryrecurrent neural networksdiffusion modelsbehavioral modeling
Abstract:

Discovering the neural mechanisms underpinning cognition is one of the grand challenges of neuroscience. Addressing this challenge greatly benefits from specific hypotheses about the underlying neural network dynamics. However, previous approaches bridging neural network dynamics and cognitive behaviour required iterative refinement of network architectures and/or objectives for normative task optimization, resulting in a long, and mostly heuristic, human-in-the-loop design process. Here, we offer an alternative approach that automates this process by explicitly training recurrent neural networks (RNNs) to reproduce behaviour, including the same characteristic errors, that humans and animals produce in a cognitive task. Achieving this required two main innovations. First, as the amount of behavioural data that can be collected in experiments is often too limited to suffice for training RNNs, we use a non-parametric generative model of behavioural responses to produce surrogate data for training RNNs. Second, to capture all relevant statistical moments in the data, rather than a limited number of hand-picked low-order moments as in previous moment matching-based approaches, we developed a novel diffusion model-based approach for training RNNs. We chose a visual working memory (VWM) task as our test-bed, as behaviour in this task is well known to produce response distributions that are patently multimodal (due to so-called swap errors). The resulting network dynamics correctly predicted previously reported qualitative features of neural data recorded in macaques. Importantly, this was only the case when RNNs were trained using our approach, fitting the full richness of behavioural data -- and not when only a limited set of behavioural signatures were fitted, nor when RNNs were trained for task optimality instead of reproducing behaviour (as has been typical for RNNs used to generate dynamical hypotheses). Our model also makes novel predictions about the mechanism of swap errors, which can be readily tested in experiments. These results suggest that fitting rich patterns of behaviour provides a powerful way for the automatic discovery of neural network dynamics supporting important cognitive functions.

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Overview

Overall Novelty Assessment

The paper proposes an automated approach to discovering neural mechanisms by training recurrent neural networks to reproduce human behavioral errors in working memory tasks. It sits within the 'Swap Errors and Feature Binding Failures' leaf of the taxonomy, which contains only three papers total. This is a relatively sparse research direction within the broader field of fifty papers, suggesting the specific focus on automated neural mechanism discovery through error reproduction is not yet heavily explored. The taxonomy indicates this leaf addresses neural substrates causing misattribution of features between objects, positioning the work at the intersection of computational modeling and error-specific mechanisms.

The taxonomy reveals neighboring research directions that provide important context. The sibling leaf 'Noise and Signal Degradation in Neural Populations' contains one paper examining how neural noise produces errors, while 'Sensory-Memory Interference' addresses interference mechanisms. The broader parent branch 'Neural Mechanisms of Specific Error Types' sits alongside 'Neural Substrates and Dynamics,' which includes computational modeling approaches under 'Neural Dynamics and Computational Models' with three papers. The original work appears to bridge these areas by using computational models specifically to capture error patterns rather than general working memory dynamics, distinguishing it from purely normative optimization approaches common in adjacent branches.

Among twenty-nine candidates examined through limited semantic search, the contribution-level analysis reveals mixed novelty signals. The core contribution of automated RNN-based mechanism discovery examined ten candidates with one appearing to provide overlapping prior work. The non-parametric generative modeling contribution examined nine candidates with none clearly refuting it, suggesting relative novelty in this specific methodological component. The diffusion model-based training approach examined ten candidates with one potential overlap. These statistics indicate that within the limited search scope, the generative modeling component appears most distinctive, while the RNN training and diffusion approaches have at least some precedent in the examined literature.

Based on the limited top-K semantic search covering twenty-nine papers, the work appears to occupy a moderately novel position. The sparse taxonomy leaf and the generative modeling component suggest contributions beyond immediate prior work, though the RNN training approach shows some overlap within examined candidates. The analysis does not cover exhaustive literature review and focuses on semantic proximity rather than comprehensive field coverage, leaving open questions about related work in adjacent computational neuroscience domains not captured by the search scope.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: discovering neural mechanisms underlying cognitive errors in working memory tasks. The field is organized around multiple complementary perspectives that together illuminate how the brain encodes, maintains, and retrieves information under capacity constraints. The taxonomy reveals several major branches: one focuses on specific error types such as swap errors and feature binding failures (Swap Errors Working Memory[1], Automatic Discovery Cognitive Errors[0]), another examines the neural substrates and dynamics that support working memory processes (Noise Neural Populations[2], Prefrontal Persistent Activity[6]), while additional branches address cognitive control and executive functions (Adaptive Cognitive Control[31]), neuroimaging methodologies (Neuroimaging Working Memory[11]), individual differences and clinical impairments (ADHD Lateral Prefrontal[35]), neurobiological modulatory systems (Dopamine Receptor Decline[10]), theoretical frameworks (Cognitive Networks Cognits[25]), training interventions (Neuroscience Capacity Training[16]), domain-specific applications, and methodological innovations. These branches collectively map how researchers move from behavioral phenomena to circuit-level explanations, integrating computational models with empirical observations. Particularly active lines of work explore the tension between capacity-limited representations and the fidelity of feature binding, with studies examining how neural noise and interference lead to systematic errors. Swap Errors Working Memory[1] and Alpha Phase Coding[18] illustrate how misbinding of features to locations or objects can arise from overlapping neural codes, while Visual Working Memory[3] and Overcoming Sensory Memory Interference[4] address how sensory interference degrades maintenance. Automatic Discovery Cognitive Errors[0] sits within this cluster focused on swap errors and feature binding failures, sharing with Swap Errors Working Memory[1] an emphasis on characterizing specific error signatures but potentially differing in its approach to automatically identifying neural correlates rather than testing predefined hypotheses. Compared to Alpha Phase Coding[18], which examines oscillatory mechanisms of binding, the original work may leverage data-driven discovery methods to uncover novel neural patterns associated with cognitive errors, bridging mechanistic and exploratory frameworks in understanding working memory failures.

Claimed Contributions

Automated discovery of neural mechanisms by training RNNs to reproduce behavioral errors

The authors propose a method that trains recurrent neural networks to replicate not just optimal task performance but also the characteristic errors and suboptimalities observed in human and animal behavior. This automated approach eliminates the need for iterative, heuristic model refinement and enables discovery of neural mechanisms underlying cognitive functions.

10 retrieved papers
Can Refute
Non-parametric generative model for producing surrogate training data

To address the data scarcity problem inherent in behavioral experiments, the authors use a non-parametric generative model to create synthetic behavioral data that captures the statistical properties of real behavior, enabling RNN training at scale.

9 retrieved papers
Diffusion model-based training approach for capturing complex behavioral distributions

The authors develop a novel training criterion inspired by diffusion models that enables RNNs to generate complex, multimodal continuous response distributions. This approach overcomes limitations of traditional moment-matching methods and allows fitting to the full richness of behavioral response distributions.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Automated discovery of neural mechanisms by training RNNs to reproduce behavioral errors

The authors propose a method that trains recurrent neural networks to replicate not just optimal task performance but also the characteristic errors and suboptimalities observed in human and animal behavior. This automated approach eliminates the need for iterative, heuristic model refinement and enables discovery of neural mechanisms underlying cognitive functions.

Contribution

Non-parametric generative model for producing surrogate training data

To address the data scarcity problem inherent in behavioral experiments, the authors use a non-parametric generative model to create synthetic behavioral data that captures the statistical properties of real behavior, enabling RNN training at scale.

Contribution

Diffusion model-based training approach for capturing complex behavioral distributions

The authors develop a novel training criterion inspired by diffusion models that enables RNNs to generate complex, multimodal continuous response distributions. This approach overcomes limitations of traditional moment-matching methods and allows fitting to the full richness of behavioral response distributions.

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