Compositional Sequence Generation in the Entorhinal-Hippocampal System.

Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas such as the prefrontal cortex, indicating that it may be a general principle of neural functionality in the brain. In a recent analysis through the lens of dynamical systems theory, we showed how grid coding can lead to the generation of a diversity of empirically observed sequential reactivations of hippocampal place cells corresponding to traversals of cognitive maps. Here, we extend this sequence generation model by describing how the synthesis of multiple dynamical systems can support compositional cognitive computations. To empirically validate the model, we simulate two experiments demonstrating compositionality in space or in time during sequence generation. Finally, we describe several neural network architectures supporting various types of compositionality based on grid coding and highlight connections to recent work in machine learning leveraging analogous techniques.

Realizing the promise of AI: a new calling for cognitive science.

Rapid progress in artificial intelligence (AI) places a new spotlight on a long-standing question: how can we best develop AI to maximize its benefits to humanity? Answering this question in a satisfying and timely way represents an exciting challenge not only for AI research but also for all member disciplines of cognitive science.

Value representations in the rodent orbitofrontal cortex drive learning, not choice.

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here, we employ a recently developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

Flexible modulation of sequence generation in the entorhinal-hippocampal system.

Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally generated sequences for particular cognitive functions and propose a neural mechanism by which this may be accomplished within the entorhinal-hippocampal circuit. Specifically, we demonstrate that the systematic modulation along the medial entorhinal cortex dorsoventral axis of grid population input into the hippocampus facilitates a flexible generative process that can interpolate between qualitatively distinct regimes of sequential hippocampal reactivations. By relating the emergent hippocampal activity patterns drawn from our model to empirical data, we explain and reconcile a diversity of recently observed, but apparently unrelated, phenomena such as generative cycling, diffusive hippocampal reactivations and jumping trajectory events.

Widespread temporal coding of cognitive control in the human prefrontal cortex.

When making decisions we often face the need to adjudicate between conflicting strategies or courses of action. Our ability to understand the neuronal processes underlying conflict processing is limited on the one hand by the spatiotemporal resolution of functional MRI and, on the other hand, by imperfect cross-species homologies in animal model systems. Here we examine the responses of single neurons and local field potentials in human neurosurgical patients in two prefrontal regions critical to controlled decision-making, the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). While we observe typical modest conflict-related firing rate effects, we find a widespread effect of conflict on spike-phase coupling in the dACC and on driving spike-field coherence in the dlPFC. These results support the hypothesis that a cross-areal rhythmic neuronal coordination is intrinsic to cognitive control in response to conflict, and provide new evidence to support the hypothesis that conflict processing involves modulation of the dlPFC by the dACC.

Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex.

A longstanding view of the organization of human and animal behavior holds that behavior is hierarchically organized-in other words, directed toward achieving superordinate goals through the achievement of subordinate goals or subgoals. However, most research in neuroscience has focused on tasks without hierarchical structure. In past work, we have shown that negative reward prediction error (RPE) signals in medial prefrontal cortex (mPFC) can be linked not only to superordinate goals but also to subgoals. This suggests that mPFC tracks impediments in the progression toward subgoals. Using fMRI of human participants engaged in a hierarchical navigation task, here we found that mPFC also processes positive prediction errors at the level of subgoals, indicating that this brain region is sensitive to advances in subgoal completion. However, when subgoal RPEs were elicited alongside with goal-related RPEs, mPFC responses reflected only the goal-related RPEs. These findings suggest that information from different levels of hierarchy is processed selectively, depending on the task context.

Author Correction: Dorsal hippocampus contributes to model-based planning.

In the version of this article initially published, the green label in Fig. 1c read "rightward choices" instead of "leftward choices." The error has been corrected in the HTML and PDF versions of the article.

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action.

Decision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to which actions to take based on the attended information. However, people often process information across these various levels in parallel. Here we scan participants while they simultaneously weigh how much to attend to two dynamic stimulus attributes and what response to give. Regions of the prefrontal cortex track information about the stimulus attributes in dissociable ways, related to either the predicted reward (ventromedial prefrontal cortex) or the degree to which that attribute is being attended (dorsal anterior cingulate cortex, dACC). Within the dACC, adjacent regions track correlates of uncertainty at different levels of the decision, regarding what to attend versus how to respond. These findings bridge research on perceptual and value-based decision-making, demonstrating that people dynamically integrate information in parallel across different levels of decision-making.

Author Correction: The hippocampus as a predictive map.

In the version of this article initially published, equation (7) read.

The hippocampus as a predictive map.

A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

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.

Dorsal hippocampus contributes to model-based planning.

Planning can be defined as action selection that leverages an internal model of the outcomes likely to follow each possible action. Its neural mechanisms remain poorly understood. Here we adapt recent advances from human research for rats, presenting for the first time an animal task that produces many trials of planned behavior per session, making multitrial rodent experimental tools available to study planning. We use part of this toolkit to address a perennially controversial issue in planning: the role of the dorsal hippocampus. Although prospective hippocampal representations have been proposed to support planning, intact planning in animals with damaged hippocampi has been repeatedly observed. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide causal evidence directly linking dorsal hippocampus with planning behavior. Our results and methods open the door to new and more detailed investigations of the neural mechanisms of planning in the hippocampus and throughout the brain.

Toward a Rational and Mechanistic Account of Mental Effort.

In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us.

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Dorsal anterior cingulate cortex and the value of control.

Debates over the function(s) of dorsal anterior cingulate cortex (dACC) have persisted for decades. So too have demonstrations of the region's association with cognitive control. Researchers have struggled to account for this association and, simultaneously, dACC's involvement in phenomena related to evaluation and motivation. We describe a recent integrative theory that achieves this goal. It proposes that dACC serves to specify the currently optimal allocation of control by determining the overall expected value of control (EVC), thereby licensing the associated cognitive effort. The EVC theory accounts for dACC's sensitivity to a wide array of experimental variables, and their relationship to subsequent control adjustments. Finally, we contrast our theory with a recent theory proposing a primary role for dACC in foraging-like decisions. We describe why the EVC theory offers a more comprehensive and coherent account of dACC function, including dACC's particular involvement in decisions regarding foraging or otherwise altering one's behavior.

Dorsal anterior cingulate and ventromedial prefrontal cortex have inverse roles in both foraging and economic choice.

Recent research has highlighted a distinction between sequential foraging choices and traditional economic choices between simultaneously presented options. This was partly motivated by observations in Kolling, Behrens, Mars, and Rushworth, Science, 336(6077), 95-98 (2012) (hereafter, KBMR) that these choice types are subserved by different circuits, with dorsal anterior cingulate (dACC) preferentially involved in foraging and ventromedial prefrontal cortex (vmPFC) preferentially involved in economic choice. To support this account, KBMR used fMRI to scan human subjects making either a foraging choice (between exploiting a current offer or swapping for potentially better rewards) or an economic choice (between two reward-probability pairs). This study found that dACC better tracked values pertaining to foraging, whereas vmPFC better tracked values pertaining to economic choice. We recently showed that dACC's role in these foraging choices is better described by the difficulty of choosing than by foraging value, when correcting for choice biases and testing a sufficiently broad set of foraging values (Shenhav, Straccia, Cohen, & Botvinick Nature Neuroscience, 17(9), 1249-1254, 2014). Here, we extend these findings in 3 ways. First, we replicate our original finding with a larger sample and a task modified to address remaining methodological gaps between our previous experiments and that of KBMR. Second, we show that dACC activity is best accounted for by choice difficulty alone (rather than in combination with foraging value) during both foraging and economic choices. Third, we show that patterns of vmPFC activity, inverted relative to dACC, also suggest a common function across both choice types. Overall, we conclude that both regions are similarly engaged by foraging-like and economic choice.

Statistical learning of temporal community structure in the hippocampus.

The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.

Evidence integration in model-based tree search.

Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.

Cognitive effort avoidance and detection in people with schizophrenia.

Many people with schizophrenia exhibit avolition, a difficulty initiating and maintaining goal-directed behavior, considered to be a key negative symptom of the disorder. Recent evidence indicates that patients with higher levels of negative symptoms differ from healthy controls in showing an exaggerated cost of the physical effort needed to obtain a potential reward. We examined whether patients show an exaggerated avoidance of cognitive effort, using the demand selection task developed by Kool, McGuire, Rosen, and Botvinick (Journal of Experimental Psychology. General, 139, 665-682, 2010). A total of 83 people with schizophrenia or schizoaffective disorder and 71 healthy volunteers participated in three experiments where instructions varied. In the standard task (Experiment 1), neither controls nor patients showed expected cognitive demand avoidance. With enhanced instructions (Experiment 2), controls demonstrated greater demand avoidance than patients. In Experiment 3, patients showed nonsignificant reductions in demand avoidance, relative to controls. In a control experiment, patients showed significantly reduced ability to detect the effort demands associated with different response alternatives. In both groups, the ability to detect effort demands was associated with increased effort avoidance. In both groups, increased cognitive effort avoidance was associated with higher IQ and general neuropsychological ability. No significant correlations between demand avoidance and negative symptom severity were observed. Thus, it appears that individual differences in general intellectual ability and effort detection are related to cognitive effort avoidance and likely account for the subtle reduction in effort avoidance observed in schizophrenia.