The transitive inference task to study the neuronal correlates of memory-driven decision making: A monkey neurophysiology perspective

Ramawat, Surabhi; Marc, Isabel Beatrice; Ceccarelli, Francesco; Ferrucci, Lorenzo; Bardella, Giampiero; Ferraina, Stefano; Pani, Pierpaolo; Brunamonti, Emiliano

doi:10.1016/j.neubiorev.2023.105258

Cited by 2 publications

References 118 publications

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A mathematical theory of relational generalization in transitive inference

Lippl,

Kay,

Jensen

et al. 2023

Preprint

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Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items ("compositional generalization"). This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A>B and B>C) and generalize it to new combinations of items (A>C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and often leads to better generalization, deviate in task behavior from living subjects and can make generalization errors. Our findings show systematically how minimal statistical learning principles can explain the rich behaviors empirically observed in TI in living subjects, uncover the mechanistic basis of transitive generalization in standard learning models, and lay out a formally tractable approach to understanding the neural basis of relational generalization.

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A mathematical theory of relational generalization in transitive inference

Lippl,

Kay,

Jensen

et al. 2023

Preprint

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A Neural Decision Signal during Internal Sampling from Working Memory in Humans

van Ede,

Nobre

2024

J. Neurosci.

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How humans transform sensory information into decisions that steer purposeful behaviour is a central question in psychology and neuroscience that is traditionally investigated during the sampling of external, environmental signals. The decision-making framework of gradual information sampling toward a decision has also been proposed to apply when sampling internal sensory evidence from working memory. However, neural evidence for this proposal remains scarce. Here we show (using scalp-EEG in male and female human volunteers) that sampling internal visual representations from working memory elicits a scalp-EEG potential associated with gradual evidence accumulation – the Central Parietal Positivity (CPP). Consistent with an evolving decision process, we show how this signal (i) scales with the time participants require to reach a decision about the cued memory content and (ii) is amplified when having to decide among multiple contents in working memory. These results bring the electrophysiology of decision making into the domain of working memory and suggest that variability in memory-guided behaviour may be driven (at least in part) by variations in the sampling of our inner mental contents.Significance StatementA foundational question in the study of mind and brain is how we transform sensory information into decisions that steer adaptive behaviour. This is traditionally investigated during the sampling of external, environmental signals. Here, we demonstrate that a canonical EEG marker of decision making from the human perception literature – the CPP – also tracks gradual decision making when selecting and accessing internally stored visual information from working memory. These findings bridge the literatures on decision making and working memory and suggest that trial-to-trial variability in memory-guided behaviour is driven, at least in part, by variations in the sampling of our inner mental contents.

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The transitive inference task to study the neuronal correlates of memory-driven decision making: A monkey neurophysiology perspective

Cited by 2 publications

References 118 publications

A mathematical theory of relational generalization in transitive inference

A mathematical theory of relational generalization in transitive inference

A Neural Decision Signal during Internal Sampling from Working Memory in Humans

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