Biology subjects

Brody, C. D.

Publications and source records attributed to Brody, C. D..

9 recordsLinked to original sources

bioRxiv2018

An Analysis of Decision Under Risk in Rats

Prospect Theory is the predominant behavioral economic theory describing decision-making under risk. It accounts for near universal aspects of human choice behavior whose prevalence may reflect fundamental neural mechanisms. We now apply Prospect Theorys framework to rodents, using a task in which rats chose between guaranteed and probabilistic rewards. Like humans, rats distorted probabilities and showed diminishing marginal sensitivity, in which they were less sensitive to differences in larger rewards. They exhibited reference dependence, in which the valence of outcomes (gain or loss) was determined by an internal reference point reflecting reward history. The similarities between rats and humans suggest conserved neural substrates, and enable application of powerful molecular/circuit tools to study mechanisms of psychological phenomena from behavioral economics.

neuroscience↗

bioRxiv2018

Causal contribution and dynamical encoding in the striatum during evidence accumulation

A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicates that cortical regions prominently associated with accumulating evidence, such as posterior parietal cortex and the frontal orienting fields, are not necessary for computing it. Which, then, are the regions responsible? Regions directly involved in evidence accumulation should satisfy the criteria of being necessary for accumulation-based decision-making behavior, having a graded neural encoding of accumulated evidence and causal contributing throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy these criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation.

neuroscience↗

bioRxiv2018

Value Representations in Orbitofrontal Cortex Drive Learning, but 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.

neuroscience↗

bioRxiv2018

Collicular circuits for flexible sensorimotor routing

Historically, cognitive processing has been thought to rely on cortical areas such as prefrontal cortex (PFC), with the outputs of these areas modulating activity in lower, putatively simpler spatiomotor regions, such as the midbrain superior colliculus (SC). Using a rat task in which subjects switch rapidly between task contexts that demand changes in sensorimotor mappings, we report a surprising role for the SC in non-spatial cognitive processes. Before spatial response choices could be formed, neurons in SC encoded task context more strongly than neurons in PFC, and bilateral SC silencing impaired behavioral performance. Once spatial choices could begin to be formed, SC neurons encoded the choice faster than PFC, while bilateral SC silencing no longer impaired choices. A set of dynamical models of the SC replicates our findings. Our results challenge cortically-focused views of cognition, and suggest that ostensibly spatiomotor structures can play central roles in non-spatiomotor cognitive processes.

neuroscience↗

bioRxiv2018

Controlling for temporal discounting shifts rats from geometric to human-like arithmetic bisection

How our brains measure the passage of time is still largely open for debate. One behavioral task commonly used to study how durations are perceived is the Temporal Bisection Task, in which subjects categorize time durations as either \"short\" or \"long.\" The duration equally likely to be categorized as short or long is known as the bisection point. It has been consistently demonstrated that for humans, the bisection point is near the arithmetic mean of the longest and shortest durations the subject was trained on. In contrast, for non-human subjects it has been consistently found near the geometric mean. This difference implies that humans may process or represent temporal durations differently than other species. Here we present a behavioral model that reconciles the differences by demonstrating that rats performance on this task is driven not only by their noisy estimates of duration, but also by the temporally-discounted value of future rewards. The model correctly predicts shifts in the bisection point induced by unequal rewards and explains otherwise-paradoxical psychometric reversals documented three decades ago. Furthermore, as predicted by the model, we found that modifying the Temporal Bisection Task to eliminate the temporally-discounted reward component shifted the rats bisection point from the geometric mean to the arithmetic mean, thus bringing the rat results into line with the human results. We therefore propose that humans and rats (and perhaps other non-human subjects as well) process temporal information similarly, and that the difference between them in the Temporal Bisection Task may be simply due to rats weighing temporal discounting of future rewards more strongly than humans.

animal behavior and cognition↗

bioRxiv2017

Rats optimally accumulate and discount evidence in a dynamic environment

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.

neuroscience↗

bioRxiv2017

Posterior parietal cortex represents sensory stimulus history and is necessary for its effects on behavior

Many models of cognition and of neural computations posit the use and estimation of prior stimulus statistics1-4: it has long been known that working memory and perception are strongly impacted by previous sensory experience, even when that sensory history is irrelevant for the current task at hand. Nevertheless, the neural mechanisms and brain regions necessary for computing and using such priors are unknown. Here we report that the posterior parietal cortex (PPC) is a critical locus for the representation and use of prior stimulus information. We trained rats in an auditory Parametric Working Memory (PWM) task, and found that rats displayed substantial and readily quantifiable behavioral effects of sensory stimulus history, similar to those observed in humans5,6 and monkeys7. Earlier proposals that PPC supports working memory8,9 predict that optogenetic silencing of this region would impair behavior in our working memory task. Contrary to this prediction, silencing PPC significantly improved performance. Quantitative analyses of behavior revealed that this improvement was due to the selective reduction of the effects of prior sensory stimuli. Electrophysiological recordings showed that PPC neurons carried far more information about sensory stimuli of previous trials than about stimuli of the current trial. Furthermore, the more information about previous trial sensory history in the neural firing rates of a given rats PPC, the greater the behavioral effect of sensory history in that rat, suggesting a tight link between behavior and PPC representations of stimulus history. Our results indicate that the PPC is a central component in the processing of sensory stimulus history, and open a window for neurobiological investigation of long-standing questions regarding how perception and working memory are affected by prior sensory information.

neuroscience↗

bioRxiv2016

Dorsal hippocampus plays a causal role in model-based planning

Planning can be defined as a process of action selection that leverages an internal model of the environment. Such models provide information about the likely outcomes that will follow each selected action, and their use is a key function underlying complex adaptive behavior. However, the neural mechanisms supporting this ability remain poorly understood. In the present work, we adapt for rodents recent advances from work on human planning, presenting for the first time a task for animals which produces many trials of planned behavior per session, allowing the experimental toolkit available for use in trial-by-trial tasks for rodents to be applied to the study of planning. We take advantage of one part of this toolkit to address a perennially controversial issue in planning research: the role of the dorsal hippocampus. Although prospective representations in the hippocampus have been proposed to support model-based planning, intact planning in hippocampally damaged animals has been observed in a number of assays. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide the first causal evidence directly linking dorsal hippocampus with planning behavior. The results reported, and the methods introduced, open the door to new and more detailed investigations of the neural mechanisms of planning, in the hippocampus and throughout the brain.

neuroscience↗

bioRxiv2016

Identifying Model-Based and Model-Free Patterns in Behavior on Multi-Step Tasks

Recent years have seen a surge of research into the neuroscience of planning. Much of this work has taken advantage of a two-step sequential decision task developed by Daw et al. (2011), which gives the ability to diagnose whether or not subjects behavior is the result of planning. Here, we present simulations which suggest that the techniques most commonly used to analyze data from this task may be confounded in important ways. We introduce a new analysis technique, which suffers from fewer of these issues. This technique also presents a richer view of behavior, making it useful for characterizing patterns in behavior in a theory-neutral manner. This allows it to provide an important check on the assumptions of more theory-driven analysis such as agent-based model-fitting.

neuroscience↗