Biology subjects

Hayden, B.

Publications and source records attributed to Hayden, B..

34 records · Page 2Linked to original sources

bioRxiv2020

Curiosity is associated with enhanced tonic firing in dorsal anterior cingulate cortex

Disparity between current and desired information, known as information gap, is an important driver of information-seeking and curiosity. To gain insight into its neural basis, we recorded responses of single neurons in dorsal anterior cingulate cortex (dACC) while rhesus macaques performed a task that induces and quantifies demand for information. We find that enhanced firing rates in dACC before the start of a trial predict a stronger bias towards information-seeking choices. Following choices of uninformative options, firing rates are tonically enhanced until information is delivered. The level of enhancement observed is correlated on a trial-by-trial basis with the value assigned to the prospective information. Finally, variation in this tone is positively correlated with receptiveness to new information, as inferred by preference changes on subsequent trials. These patterns are not observed in a complementary dataset collected in orbitofrontal cortex (OFC), suggesting these effects reflect at least somewhat anatomically localized processing.

neuroscience↗

bioRxiv2020

Shared neuronal bases of inhibition and economic choice in orbitofrontal cortex

Economic choice and inhibition are two important elements of our cognitive repertoires that may be closely related. We and others have noted that during economic choice, options are typically considered serially; this fact provides important constraints on our understanding of choice. Notably, asynchronous contemplation means that each individual option is subject to an accept-reject decision. We have proposed that these component accept-reject decisions may have some kinship with stopping decisions. One prediction of this idea is that stopping and choice may reflect similar neural processes occurring in overlapping brain circuits. To test the idea, we recorded neuronal activity in orbitofrontal cortex (OFC) Area 13 while macaques performed a stop signal task interleaved with a structurally matched choice task. Using neural network decoders, we find that OFC ensembles have overlapping codes for stopping and choice: the decoder that was only trained to identify accept vs. reject trials performed with higher efficiency even when tested on the stop trials. These results provide tentative support for the idea that mechanisms underlying inhibitory control and choice selection may be subject to theoretical unification.

neuroscience↗

bioRxiv2020

Human dorsal anterior cingulate neurons signal conflict by amplifying task-relevant information

Hemodynamic activity in dorsal anterior cingulate cortex (dACC) correlates with conflict, suggesting it contributes to conflict processing. This correlation could be explained by multiple neural processes that can be disambiguated by population firing rates patterns. We used targeted dimensionality reduction to characterize activity of populations of single dACC neurons as humans performed a task that manipulates two forms of conflict. Although conflict enhanced firing rates, this enhancement did not come from a discrete population of domain-general conflict-encoding neurons, nor from a distinct conflict-encoding response axis. Nor was it the epiphenomenal consequence of simultaneous coactivation of action plans. Instead, conflict amplified the task-relevant information encoded across the neuronal population. Effects of conflict were weaker and more heterogeneous in the dorsolateral prefrontal cortex (dlPFC), suggesting that dACCs role in conflict processing may be somewhat specialized. Overall, these results support the theory that conflict biases competition between sensorimotor transformation processes occurring in dACC.

neuroscience↗

bioRxiv2020

The breadth-depth dilemma in a finite capacity model of decision-making

Decision-makers are often faced with limited information about the outcomes of their choices. Current formalizations of uncertain choice, such as the explore-exploit dilemma, do not apply well to decisions in which search capacity can be allocated to each option in variable amounts. Such choices confront decision-makers with the need to tradeoff between breadth - allocating a small amount of capacity to each of many options - and depth - focusing capacity on a few options. We formalize the breadth-depth dilemma through a finite sample capacity model. We find that, if capacity is smaller than 4-7 samples, it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, that decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, reflects a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multi-alternative decisions with bounded capacity.

neuroscience↗

bioRxiv2020

Dorsal anterior cingulate cortex neurons only partially integrate determinants of value

Evaluation often involves integrating multiple determinants of value, such as the different possible outcomes in risky choice. A brain region can be placed either before or after a presumed evaluation stage by measuring how responses of its neurons depend on multiple determinants of value. A brain region could also, in principle, show partial integration, which would indicate that it occupies a middle position between (pre-evaluative) non-integration and (post-evaluative) full integration. Existing mathematical techniques cannot distinguish full from partial integration and therefore risk misidentifying regional function. Here we use a new Bayesian regression-based approach to analyze responses of neurons in dorsal anterior cingulate cortex (dACC) to risky offers. We find that dACC neurons only partially integrate across outcome dimensions, indicating that dACC cannot be assigned to either a pre- or post-evaluative position. Neurons in dACC also show putative signatures of value comparison, thereby demonstrating that comparison does not require complete evaluation before proceeding.

neuroscience↗

bioRxiv2019

Rule adherence warps decision-making

We have the capacity to follow arbitrary stimulus-response rules, meaning policies that determine how we will behave across circumstances. Yet, it is not clear how rules guide sensorimotor decision-making in the brain. Here, we recorded from neurons in three regions linked to decision-making, the orbitofrontal cortex, ventral striatum, and dorsal striatum, while macaques performed a rule-based decision-making task. We found that different rules warped the neural representations of chosen options by expanding rule-relevant coding dimensions relative to rule-irrelevant ones. Some cognitive theories suggest that warping could increase processing efficiency by facilitating rule-relevant computations at the expense of irrelevant ones. To test this idea, we modeled rules as the latent causes of decisions and identified a set of "rule-free" choices that could not be explained by simple rules. Contrasting these with rule-based choices revealed that following rules decreased the energetic cost of decision-making while warping the representational geometry of choice. SIGNIFICANCE STATEMENTOne important part of our ability to adapt flexibly to the world around us is our ability to implement arbitrary stimulus-response mappings, known as "rules". Many studies have shown that when we follow a rule, its identity is encoded in neuronal firing rates. However, it remains unclear how rules regulate behavior. Here, we report that rules warp the way that sensorimotor information is represented in decision-making circuits: enhancing information that is relevant to the current rule at the expense of information that is irrelevant. These results imply that rules are implemented as a kind of attentional gate on what information is available for decision-making.

neuroscience↗

bioRxiv2019

Divergent strategies for learning in males and females

A frequent assumption in value-based decision-making tasks is that agents make decisions based on the feature dimension that reward probabilities vary on. However, in complex, multidimensional environments, stimuli can vary on multiple dimensions at once, meaning that the feature deserving the most credit for outcomes is not always obvious. As a result, individuals may vary in the strategies used to sample stimuli across dimensions, and these strategies may have an unrecognized influence on decision-making. Sex is a proxy for multiple genetic and endocrine influences that can influence decision-making strategies, including how environments are sampled. In this study, we examined the strategies adopted by female and male mice as they learned the value of stimuli that varied in both image and location in a visually-cued two-armed bandit, allowing two possible dimensions to learn about. Female mice acquired the correct image-value associations more quickly than male mice, and they used a fundamentally different strategy to do so. Female mice constrained their decision-space early in learning by preferentially sampling one location over which images varied. Conversely, male strategies were inconsistent, changing frequently and strongly influenced by the immediate experience of stochastic rewards. Individual strategies were related to sex-gated changes in neuronal activation in early learning. Together, we find that in mice, sex is linked with divergent strategies for sampling and learning about the world, revealing substantial unrecognized variability in the approaches implemented during value-based decision-making.

animal behavior and cognition↗

bioRxiv2019

Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion

Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesized that the dorsal anterior cingulate cortex (dACC), which communicates bidirectionally with both the hippocampal complex and premotor/motor areas, would serve a mapping role in this process. We recorded responses of dACC ensembles in two macaques performing a joystick-controlled continuous pursuit/evasion task. We found that dACC multiplexes two sets of signals, (1) world-centric variables that together form a representation of the position and velocity of all relevant agents (self, prey, and predator) in the virtual world, and (2) avatar-centric variables, i.e. self-prey distance and angle. Both sets of variables are multiplexed within an overlapping set of neurons. Our results suggest that dACC may contribute to pursuit and evasion by computing and continuously updating a multicentric representation of the unfolding task state, and support the hypothesis that it plays a high-level abstract role in the control of behavior.

neuroscience↗

bioRxiv2018

Orbitofrontal neuron ensembles contribute to inhibitory control

Stopping, or inhibition, is a form of self-control that is a core part of adaptive behavior. We hypothesize that inhibition commands originate, in part, from the orbitofrontal cortex (OFC). We recorded activity of OFC neurons in macaques performing a stop signal task. Decoding analyses revealed a clear difference in ensemble responses that distinguish successful from failed inhibition that begins after the stop signal and before the stop signal reaction time. We also found a different and unrelated ensemble pattern that distinguishes successful from failed stopping before the beginning of the trial. These signals were distinct from, and orthogonal to, value encoding, which was also observed in these neurons. The timing of the early and late signals was, respectively, consistent with the idea that OFC contributes both proactively and reactively to inhibition. These results support the view, inspired by anatomy, that OFC gathers diverse sensory inputs to compute early-stage executive signals.

neuroscience↗

bioRxiv2018

Overlapping neural processes for stopping and economic choice in orbitofrontal cortex

Economic choice and stopping are not traditionally treated as related phenomena. However, we were motivated by foraging models of economic choice to hypothesize that they may reflect similar neural processes occurring in overlapping brain circuits. We recorded neuronal activity in orbitofrontal cortex (OFC), while macaques performed a stop signal task interleaved with a structurally matched economic choice task. Decoding analyses show that OFC ensembles predict successful versus failed stopping both before the trial and immediately after the stop signal, even after controlling for value predictions. These responses indicate that OFC contributes both proactively and reactively to stopping. Moreover, OFC neurons engagement in one task positively predicted their engagement in the other. Finally, firing patterns that distinguished low from high value offers in the economic task distinguished failed and successful trials in the stopping task. These results endorse the idea that economic choice and inhibition may be subject to theoretical unification.

neuroscience↗

bioRxiv2018

Monkeys are Curious about Counterfactual Outcomes

While many non-human animals show basic exploratory behaviors, it remains unclear whether any animals possess human-like curiosity. We propose that human-like curiosity satisfies three formal criteria: (1) willingness to pay (or to sacrifice reward) to obtain information, (2) that the information provides no instrumental or strategic benefit (and the subject understands this), and (3) the amount the subject is willing to pay scales with the amount of information available. Although previous work, including our own, demonstrates that some animals will sacrifice juice rewards for information, that information normally predicts upcoming rewards and their ostensible curiosity may therefore be a byproduct of reinforcement processes. Here we get around this potential confound by showing that macaques sacrifice juice to obtain information about counterfactual outcomes (outcomes that could have occurred had the subject chosen differently). Moreover, willingness-to-pay scales with the information (Shannon entropy) offered by the counterfactual option. These results demonstrate human-like curiosity in non-human animals according to our strict criteria, which circumvent several confounds associated with less stringent criteria.

animal behavior and cognition↗

bioRxiv2018

On the flexibility of basic risk attitudes in monkeys

Monkeys and other animals appear to share with humans two risk attitudes predicted by prospect theory: an inverse-S-shaped probability weighting function and a steeper utility curve for losses than for gains. These findings suggest that such preferences are stable traits with common neural substrates. We hypothesized instead that animals tailor their preferences to subtle changes in task contexts, making risk attitudes flexible. Previous studies used a limited number of outcomes, trial types, and contexts. To gain a broader perspective, we examined two large datasets of male macaques risky choices: one from a task with real (juice) gains and another from a token task with gains and losses. In contrast to previous findings, monkeys were risk-seeking for both gains and losses (i.e. lacked a reflection effect) and showed steeper gain than loss curves (loss-seeking). Utility curves for gains were substantially different in the two tasks. Monkeys showed nearly linear probability weightings in one task and S-shaped ones in the other; neither task produced a consistent inverse-S-shaped curve. To account for these observations, we developed and tested various computational models of the processes involved in the construction of reward value. We found that adaptive differential weighting of prospective gamble outcomes could partially account for the observed differences in the utility functions across the two experiments and thus, provide a plausible mechanism underlying flexible risk attitudes. Together, our results support the idea that risky choices are flexibly constructed at the time of elicitation and place important constraints on neural models of economic choice.

neuroscience↗

bioRxiv2017

A neuronal theory of sequential economic choice

Principles derived from recent studies have begun to converge and point to a consensus for the neural basis of economic choice. These principles include the idea that evaluation is limited to the option within the focus of attention and that we accept or reject that option relative to the entire set of alternatives. Rejection leads attention to a new option, although it can later switch back to a previously rejected one. The referent of a value-coding neuron is dynamically determined by attention and not stably by labeled lines. Comparison results not from explicit competition between discrete representations, but from value-dependent changes in responsiveness. Consequently, comparison can occur within a single pool of neurons rather than by competition between two or more neuronal populations. Comparison may nonetheless occur at multiple levels (including premotor levels) simultaneously through a distributed consensus. This framework suggests a solution to a set of otherwise unresolved neuronal binding problems that result from the need to link options to values, comparisons to actions, and choices to outcomes.

neuroscience↗

bioRxiv2017

The foraging perspective on economic choice

Foraging theory offers an alternative foundation for understanding economic choice, one that sees economic choices as the outcome of psychological processes that evolved to help our ancestors search for food. Most of the choices encountered by foragers are between pursuing an encountered prey (accept) or ignoring it in favor of continued search (reject). Binary choices, which typically occur between simultaneously presented items, are special case, and are resolved through paired alternating accept-reject decisions limited by the narrow focus of attention. The foraging approach also holds out promise for helping to understand self-control and invites a reconceptualization of the mechanisms of binary choice, the relationship between choosing and stopping, and of the meaning of reward value.\n\nHighlightsO_LIForaging provides a basis for modeling economic choice based on adaptiveness\nC_LIO_LIForaging choices are accept-reject; foraging models interpret binary choice accordingly\nC_LIO_LIThe foraging view offers a different perspective on self-control decisions\nC_LIO_LIEconomic and stopping decisions may have a common basis\nC_LI

neuroscience↗

bioRxiv2016

Neuronal responses support a role for orbitofrontal cortex in cognitive set reconfiguration

We are often faced with the need to abandon no-longer beneficial rules and adopt new ones. This process, known as cognitive set reconfiguration, is a hallmark of executive control. Although cognitive functions like reconfiguration are most often associated with dorsal prefrontal structures, recent evidence suggests that the orbitofrontal cortex (OFC) may play an important role as well. We recorded activity of OFC neurons while rhesus macaques performed a version of the Wisconsin Card Sorting Task that involved a trial-and-error stage. OFC neurons demonstrated two types of switch-related activity, an early (switch-away) signal and a late (switch-to) signal, when the new task set was established. We also found a pattern of match modulation: a significant change in activity for the stimulus that matched the current rule (and would therefore be selected). These results extend our understanding of the executive functions of the OFC. They also allow us to directly compare OFC with complementary datasets we previously collected in ventral (VS) and dorsal (DS) striatum. Although both effects are observed in all three areas, the timing of responses aligns OFC more closely with DS than with VS.

neuroscience↗

bioRxiv2016

Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles

Classification of neurons into clusters based on their response properties is an important tool for gaining insight into neural computations. However, it remains unclear to what extent neurons fall naturally into discrete functional categories. We developed a Bayesian method that models the tuning properties of neural populations as a mixture of multiple types of task-relevant response patterns. We applied this method to data from several cortical and striatal regions in economic choice tasks. In all cases, neurons fell into only two clusters: one mixed-selectivity cluster containing all task-sensitive cells and another of no selectivity (i.e. pure noise) cells. The single cluster of task-sensitive cells argues against robust categorical tuning in these areas. The no selectivity cells were unanticipated; their identification allows for improved measurement of ensemble effects. Our findings provide a valuable tool for analysis of neural data and place strong constraints on neurocomputational models of choice and control.

neuroscience↗