Evidence for shallow cognitive maps in schizophrenia.

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

The construction and use of cognitive maps in model-based control.

When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure. We found that participants who encoded stronger higher-order relationships among choice options showed increased planning and better performance. These higher-order relationships were more strongly encoded among objects encountered in high-reward contexts, indicating a role for motivation during cognitive map construction. In contrast, lower-order relationships such as simple visual co-occurrence of objects did not predict goal-directed planning. These results show that the construction of cognitive maps is an active process, with motivation dictating the degree to which higher-order relationships are encoded and used for planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cortico-hippocampal networks carry information about characters and their relationships in an extended narrative.

Social information is a centerpiece of human experience. Despite a wealth of research into the way we understand social relationships and how aspects of social life might be supported by the brain, relatively little is known about how the brain represents individual people and their relationships with others. How do intrinsic networks in the brain track people and their connections in complex situations? Here, we sought to understand this issue using an open neuroimaging dataset in which people freely viewed "The Grand Budapest Hotel." Using support vector machine classification of fMRI activity patterns, we found that character identity could be decoded throughout subsystems of the brain's "Default Mode" Network, especially in regions of an Anterior Temporal and a Medial Prefrontal subsystem, as well as a Medial Temporal Network (MTN). We tested character relationships in two ways - onscreen co-occurrence and shared semantic information from an independent sample of character descriptions - and found evidence for these representations throughout the "Default Mode" Network, and the MTN. The extent to which each variant of character relationships fit neural patterns differed across networks, with abstract semantic relatedness being especially prominent in regions of Anterior Temporal and Medial Prefrontal Networks. These data show that subsystems of the brain's "Default Mode" Network and MTN carry information about individual people as well as their connections, and highlight a particularly strong role for the Anterior Temporal network in representing this information.

Generalization of cognitive maps across space and time.

Prominent theories posit that associative memory structures, known as cognitive maps, support flexible generalization of knowledge across cognitive domains. Here, we evince a representational account of cognitive map flexibility by quantifying how spatial knowledge formed one day was used predictively in a temporal sequence task 24 hours later, biasing both behavior and neural response. Participants learned novel object locations in distinct virtual environments. After learning, hippocampus and ventromedial prefrontal cortex (vmPFC) represented a cognitive map, wherein neural patterns became more similar for same-environment objects and more discriminable for different-environment objects. Twenty-four hours later, participants rated their preference for objects from spatial learning; objects were presented in sequential triplets from either the same or different environments. We found that preference response times were slower when participants transitioned between same- and different-environment triplets. Furthermore, hippocampal spatial map coherence tracked behavioral slowing at the implicit sequence transitions. At transitions, predictive reinstatement of virtual environments decreased in anterior parahippocampal cortex. In the absence of such predictive reinstatement after sequence transitions, hippocampus and vmPFC responses increased, accompanied by hippocampal-vmPFC functional decoupling that predicted individuals' behavioral slowing after a transition. Collectively, these findings reveal how expectations derived from spatial experience generalize to support temporal prediction.