Both unmedicated and medicated individuals with schizophrenia show impairments across a wide array of cognitive and reinforcement learning tasks

Background Schizophrenia is a disorder characterized by pervasive deficits in cognitive functioning. However, few well-powered studies have examined the degree to which cognitive performance is impaired even among individuals with schizophrenia not currently on antipsychotic medications using a wide range of cognitive and reinforcement learning measures derived from cognitive neuroscience. Such research is particularly needed in the domain of reinforcement learning, given the central role of dopamine in reinforcement learning, and the potential impact of antipsychotic medications on dopamine function. Methods The present study sought to fill this gap by examining healthy controls (N = 75), unmedicated (N = 48) and medicated (N = 148) individuals with schizophrenia. Participants were recruited across five sites as part of the CNTRaCS Consortium to complete tasks assessing processing speed, cognitive control, working memory, verbal learning, relational encoding and retrieval, visual integration and reinforcement learning. Results Individuals with schizophrenia who were not taking antipsychotic medications, as well as those taking antipsychotic medications, showed pervasive deficits across cognitive domains including reinforcement learning, processing speed, cognitive control, working memory, verbal learning and relational encoding and retrieval. Further, we found that chlorpromazine equivalency rates were significantly related to processing speed and working memory, while there were no significant relationships between anticholinergic load and performance on other tasks. Conclusions These findings add to a body of literature suggesting that cognitive deficits are an enduring aspect of schizophrenia, present in those off antipsychotic medications as well as those taking antipsychotic medications.

Entities also require relational coding and binding

Commentary on: An Integrative Memory model of recollection and familiarity to understand memory deficits by Bastin, Besson, Simon, Delhaye, Geurten, Willems, & Salmon. Behavioral and Brain Sciences. Abstract: Although Bastin et al. propose a useful model for thinking about the structure of memory and memory deficits, their distinction between entities and relational encoding is incompatible with data showing that even individual objects – prototypical ‘entities’ – are made up of distinct features which require binding. Thus, ‘entity’ and ‘relational’ brain regions may need to solve fundamentally the same problems.

Can a relational mindset boost analogical retrieval?

Background Memory retrieval is driven by similarity between a present situation and some prior experience, but not all similarity is created equal. Analogical retrieval, rooted in the similarity between two situations in their underlying structural relations, is often responsible for new insights and innovative solutions to problems. However, superficial similarity is instead more likely to drive spontaneous retrieval. How can we make analogical retrieval more likely? Inducing a relational mindset via an analogical reasoning task has previously been shown to boost subsequent relational thinking. In this paper, we examined whether inducing a relational mindset could also boost analogical retrieval. Results We find that a relational mindset can increase analogical retrieval if induced before information is encoded in the first place, amplifying the effect of a clearly labelled relational structure. On the other hand, inducing a relational mindset at the time of retrieval did not increase analogical retrieval. Conclusion This work further demonstrates the central importance of high-quality relational encoding for subsequent relation-based analogical retrieval, and that inducing a relational mindset can improve those encodings.

Entities also require relational coding and binding

Although Bastin et al. propose a useful model for thinking about the structure of memory and memory deficits, their distinction between entities and relational encoding is incompatible with data showing that even individual objects – prototypical “entities” – are made up of distinct features which require binding. Thus, “entity” and “relational” brain regions may need to solve fundamentally the same problems.