Biology subjects

Piet, A. T.

Publications and source records attributed to Piet, A. T..

6 recordsLinked to original sources

bioRxiv2024

A cognitive process model captures near-optimal confidence-guided waiting in rats

Rational decision-makers invest more time pursuing rewards they are more confident they will eventually receive. A series of studies have therefore used willingness to wait for delayed rewards as a proxy for decision confidence. However, interpretation of waiting behavior is limited because it is unclear how environmental statistics influence optimal waiting, and how sources of internal variability influence subjects behavior. We trained rats to perform a confidence-guided waiting task, and derived expressions for optimal waiting that make relevant environmental statistics explicit, including travel time incurred traveling from one reward opportunity to another. We found that rats waited longer than fully optimal agents, but that their behavior was closely matched by optimal agents with travel times constrained to match their own. We developed a process model describing the decision to stop waiting as an accumulation to bound process, which allowed us to compare the effects of multiple sources of internal variability on waiting. Surprisingly, although mean wait times grew with confidence, variability did not, inconsistent with scalar invariant timing, and best explained by variability in the stopping bound. Our results describe a tractable process model that can capture the influence of environmental statistics and internal sources of variability on subjects decision process during confidence-guided waiting.

animal behavior and cognition↗

bioRxiv2021

Stable choice coding during changes of mind

How do we choose the best action in a constantly-changing environment? Many natural decisions unfold in dynamic environments where newer observations carry better information about the present state of the world. Recent work has shown that rats can learn to optimally discount old evidence, updating their provisional decision when the environmental state changes. Provisional decisions are thought to be represented in the Frontal Orienting Fields (FOF), but this has only been tested in static environments where the provisional and final decisions are not easily dissociated. Here, we characterize the representation of accumulated evidence in rat FOF during decision-making in a dynamic environment. We find that FOF encodes evidence throughout decision formation with a temporal gain modulation that rises until the period when the animal may need to act. Using a behavioral model to predict the timing of changes of mind revealed that FOF neurons respond rapidly to these events, representing the new provisional decisions in their firing rates. Our results suggest that the FOF represents provisional decisions even in dynamic, uncertain environments, allowing for rapid motor execution when it is time to act.

neuroscience↗

bioRxiv2019

Interrogating theoretical models of neural computation with deep inference

1A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon - whether behavioral or a pattern of neural activity - and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example inferring conductance parameters in a circuit model of the stomatogastric ganglion. Then, with recurrent neural networks of increasing size, we show that EPI allows precise control over the behavior of inferred parameters, and that EPI scales better in parameter dimension than alternative techniques. In the remainder of this work, we present novel theoretical findings gained through the examination of complex parametric structure captured by EPI. In a model of primary visual cortex, we discovered how connectivity with multiple inhibitory subtypes shapes variability in the excitatory population. Finally, in a model of superior colliculus, we identified and characterized two distinct regimes of connectivity that facilitate switching between opposite tasks amidst interleaved trials, characterized each regime via insights afforded by EPI, and found conditions where these circuit models reproduce results from optogenetic silencing experiments. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

neuroscience↗

bioRxiv2019

Orbitofrontal cortex promotes trial-by-trial learning of risky, but not spatial, biases

Individual choices are not made in isolation but are embedded in a series of past experiences, decisions, and outcomes. The effects of past experiences on choices, often called sequential biases, are ubiquitous in perceptual and value-based decision-making, but their neural substrates are unclear. We trained rats to choose between explicitly cued guaranteed and probabilistic rewards in a task in which outcomes on each trial were independent. Behavioral variability often reflected the effects of previous trials, including increased willingness to take risks following risky wins, and spatial \"win-stay/lose-shift\" biases. Electrophysiological recordings from lateral orbitofrontal cortex (lOFC) revealed strong encoding of reward history and receipt, and optogenetic inhibition of lOFC during choice reporting eliminated rats increased preference for risk on subsequent trials following risky wins, but spared other sequential effects. Our data show that different sequential biases are neurally dissociable, and that the lOFCs role in adaptive behavior promotes learning of more abstract, task-specific biases (here, biases for the risky option), but not spatial ones. These data are consistent with proposals that the OFC represents the animals location in an abstract representation of task states, and highlight the lOFCs role in learning.

neuroscience↗

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

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↗