Biology subjects

Russek, E. M.

Publications and source records attributed to Russek, E. M..

6 recordsLinked to original sources

bioRxiv2026

Cognitive capacity and control in the evolution of intelligence

A diversity of intelligences arises from the constraints under which animals evolve. However, characterizing how constraints shape intelligence is challenging because it requires relating the restrictions on cognitive mechanisms to those that affect their evolution. We demonstrate the potentially complex interaction between constraints by considering the case study of working memory. Here, information-processing capability is limited by the storage capacity available to hold representations, and the degree of control over those representations. We present an evolutionary model that is mechanistically detailed enough to capture the interactions between capacity and control. This allows us to make quantitative predictions about the distinct patterns of information processing that might be observed across animals. Further, our model's cognitive detail allows us to fit recall performance on the retro-cue task, illustrating how model predictions can be tested by comparing humans and rhesus monkeys (Macaca mulatta). We find that capacity and control are synergistic and amplify each other's effects. However, evolution prioritizes investment in capacity because it is required for control to be effective. The strength of synergy varies due to interactions between these cognitive components depending on task complexity, cue reliability, and the availability of metabolic energy. Consequently, our model predicts diversity in investment in capacity and control across animals, and identifies a small number of regimes into which lineages could evolve. We discuss how the computational structure of tasks exerts selection on cognitive designs.

animal behavior and cognition↗

bioRxiv2025

Depression symptoms enhance goal-directed behavior underuncertainty

A core premise in decision-making research is that beliefs about uncertain outcomes shape value-based choice: pessimistic expectations should reduce goal pursuit, whereas optimistic expectations should promote it. Depression provides a critical test of this assumption, as motivational symptoms such as apathy and anhedonia are linked to diminished expectations of success. Yet whether such biases actually impair goal-directed behavior, particularly when risk must be learned from experience, remains unknown. We developed a multi-step planning task in which participants (n = 384) learned the structure and risks of maze-like environments across two days, then chose between certain rewards and risky goals varying in reward magnitude and distance, with greater distance entailing higher compounding risk. Using transdiagnostic symptom factors, we observed a dissociation: individuals with elevated apathy-anhedonia reported lower subjective expectations of goal success, yet showed no reduction in risky goal pursuit. Instead, higher apathy-anhedonia was associated with enhanced performance, including stronger discrimination of goal values, increased model-based flexibility, and faster goal navigation. These effects were specific to apathy-anhedonia; other symptom factors, including worry and impulsivity, were unrelated to expectations or goal-directed behavior. Together, these findings demonstrate that pessimistic beliefs and goal-directed competence are dissociable, revealing that motivational symptoms can coexist with sharpened value-based planning when risk is learned from experience.

neuroscience↗

bioRxiv2024

Consolidation of Sequential Planning

Thriving in changing environments requires the capacity to evaluate novel courses of action1. This ability is hypothesized to depend on sequential planning via step-by-step simulations of the future 2-4, using cognitive maps or schemas of task contingencies5,6. However, it is still unclear if, how and where in the brain such flexible planning is enacted. In parallel, it is thought that consolidation transforms memory representations over time to promote adaptive behaviour7. Here, we hypothesize that consolidation strengthens cognitive maps of task contingencies used for simulation during sequential planning. To test this, we developed a novel behavioural task and a new multivariate method for analysis of magnetoencephalography (MEG) data. Using choice and reaction time data we dissociated flexible sequential planning from alternative non-sequential strategies, and identified this behaviour with robust neural markers of step-by-step simulation, localized to the anterior medial temporal lobe. Retesting a week later we showed that consolidation enhanced sequential planning and strengthened markers of sequential simulation in the prefrontal cortex, consistent with systems consolidation theory8-15. By revealing that consolidation improves future simulations for flexible planning we open up a new frontier for the investigation of the functional interactions between memory and decision-making.

neuroscience↗

bioRxiv2021

Neural evidence for the successor representation in choice evaluation

Evaluating choices in multi-step tasks is thought to involve mentally simulating trajectories. Recent theories propose that the brain simplifies these laborious computations using temporal abstraction: storing actions consequences, collapsed over multiple timesteps (the Successor Representation; SR). Although predictive neural representations and, separately, behavioral errors ("slips of action") consistent with this mechanism have been reported, it is unknown whether these neural representations support choices in a manner consistent with the SR. We addressed this question by using fMRI to measure predictive representations in a setting where the SR implies specific errors in multi-step expectancies and corresponding behavioral errors. By decoding measures of state predictions from sensory cortex during choice evaluation, we identified evidence that behavioral errors predicted by the SR are accompanied by predictive representations of upcoming task states reflecting SR predicted erroneous multi-step expectancies. These results provide neural evidence for the SR in choice evaluation and contribute toward a mechanistic understanding of flexible and inflexible decision making.

neuroscience↗

bioRxiv2016

The successor representation in human reinforcement learning

Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. \"Model-based\" algorithms compute the value of candidate actions from scratch, whereas \"model-free\" algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation (SR), which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. SRs reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the tasks sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioral studies with humans. These results suggest that the SR is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.

neuroscience↗

bioRxiv2016

Predictive representations can link model-based reinforcement learning to model-free mechanisms

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.\n\nAuthor SummaryAccording to standard models, when confronted with a choice, animals and humans rely on two separate, distinct processes to come to a decision. One process deliberatively evaluates the consequences of each candidate action and is thought to underlie the ability to flexibly come up with novel plans. The other process gradually increases the propensity to perform behaviors that were previously successful and is thought to underlie automatically executed, habitual reflexes. Although computational principles and animal behavior support this dichotomy, at the neural level, there is little evidence supporting a clean segregation. For instance, although dopamine -- famously implicated in drug addiction and Parkinsons disease -- currently only has a well-defined role in the automatic process, evidence suggests that it also plays a role in the deliberative process. In this work, we present a computational framework for resolving this mismatch. We show that the types of behaviors associated with either process could result from a common learning mechanism applied to different strategies for how populations of neurons could represent candidate actions. In addition to demonstrating that this account can produce the full range of flexible behavior observed in the empirical literature, we suggest experiments that could detect the various approaches within this framework.

neuroscience↗