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Glitz, L.

Publications and source records attributed to Glitz, L..

2 recordsLinked to original sources

Distinct roles of hippocampus and neocortex in symbolic compositional generalization

Humans can combine symbols to generate new meanings. Here, we studied the regional neural mechanisms that might make this possible. We asked participants to combine two discrete, symbolic features (a shape and a colour) to make a novel spatial inference. BOLD data suggested that the hippocampus encoded elementary visual attributes in a high-dimensional, parallel format that permitted flexible individuation. In ventromedial prefrontal cortex (vmPFC), posterior parietal cortex (PPC) and primary visual cortex (V1), neural patterns for novel stimuli (composites) could be predicted as a linear combination of signals for familiar stimuli (elements). In vmPFC, this composition occurred in a high-dimensional format, but in PPC and V1, it took place in a low-dimensional, spatial, response-consistent frame of reference. These data offer new insights into the neural circuit underlying compositional generalization.

neuroscience↗

Generalising state transition structure in the human medial temporal lobe

Effective planning involves knowing where different actions will take us. However natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution to this problem is to share the neural state transition functions used for planning between similar contexts. Here, we asked human participants to perform a sequential decision making task designed so that knowledge could be shared between some contexts but not others. Computational modelling showed that participants shared a model of state transitions between contexts where appropriate. fMRI data identified the medial temporal lobe as a locus for learning of state transitions, and within the same region, correlated BOLD patterns were observed in contexts where state transition information were shared. Finally, we show that the transition model is updated more strongly following the receipt of positive compared to negative outcomes, a finding that challenges conventional theories of planning which assume knowledge about our environment is updated independently of outcomes received. Together, these findings propose a computational and neural account of how information relevant for planning can be shared between contexts.

neuroscience↗