Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

Latimer, Kenneth W.; Yates, Jacob L.; Meister, Miriam L. R.; Huk, Alexander C.; Pillow, Jonathan W.

doi:10.1126/science.aaa4056

Cited by 298 publications

(355 citation statements)

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“…Consistent with this assumption, the effects of prior probabilities in perceptual decision making have also been shown to be underlain by the activation of the corticostriatal circuit in humans (24). Importantly, recent evidence further suggests that the cortical modulation during decision making can take the form of discrete steps characterized by abrupt transitions within a trial (25), capturing the discrete nature of timed switching behavior (short vs. long) in our task.…”

Section: Discussionsupporting

confidence: 75%

Mice plan decision strategies based on previously learned time intervals, locations, and probabilities

Tosun

Gür

Balcı

2016

Proc. Natl. Acad. Sci. U.S.A.

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Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment.decision making | interval timing | temporal risk assessment | probabilities | mice M any vertebrate species can build temporal expectancies and cluster their anticipatory behaviors around intervals that lead to critical outcomes (1). The resultant timed behaviors have been shown to be sensitive to other crucial elements of environmental statistics, such as the probabilities of different outcomes (2-4). However, how steady-state choice behavior emerges in temporal decision-making tasks that contain probabilistic contingencies remains to be answered. Do animals directly manifest their knowledge of quantities (e.g., time interval, probability) and locations in their timed behavior or do they gradually acquire differential timed response patterns based on reinforcement learning in a fashion stripped of representations? This study aimed to address this fundamental question using a simple temporal decisionmaking task.A class of interval timing paradigms requires the animals to distribute their responses between two (short vs. long latency) options, each of which predicts reward after the corresponding fixed delay. In these cases, the emergent response pattern is first behaviorally investing in the option with a short delay to the reward, and if responding a...

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Section: Discussionsupporting

confidence: 75%

Mice plan decision strategies based on previously learned time intervals, locations, and probabilities

Tosun

Gür

Balcı

2016

Proc. Natl. Acad. Sci. U.S.A.

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“…This belief updating is formally identical to evidence accumulation described by drift diffusion or race-to-bound models (Solway & Botvinick, 2012;Zhang & Maloney, 2012;de Lafuente et al, 2015;Kira et al, 2015) and nicely recapitulates the emergence of a choice as evaluation of options proceeds (Hunt et al, 2012). Furthermore, the separation of timescales implicit in variational updating reproduces the stepping dynamics seen in parietal responses during decision making (Latimer et al, 2015).…”

Section: Electrophysiological Correlates Of Variational Belief Updatingsupporting

confidence: 53%

“…These include repetition suppression (de Gardelle, Waszczuk, Egner, & Summerfield, 2013), violation and omission responses (Bendixen, SanMiguel, & Schroger, 2012), and neuronal responses that are characteristic of the hippocampus, namely, place cell activity (Moser, Rowland, & Moser, 2015), theta-gamma coupling, theta sequences and phase precession (Burgess, Barry, & O'Keefe, 2007;Lisman & Redish, 2009). We also touch on dynamics seen in parietal and prefrontal cortex, such as evidence accumulation and race-to-bound or threshold (Huk & Shadlen, 2005, Gold & Shadlen, 2007Hunt et al, 2012;Solway & Botvinick, 2012;de Lafuente, Jazayeri, & Shadlen, 2015;FitzGerald, Moran, Friston, & Dolan, 2015;Latimer, Yates, Meister, Huk, & Pillow, 2015).…”

Section: Introductionmentioning

confidence: 99%

Active Inference: A Process Theory

et al. 2017

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This is the published version of the paper.This version of the publication may differ from the final published version. behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action. Permanent repository link

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“…Firing rates of neurons in the PPC encode movement intention and the temporal evolution of movement choices (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) as well as decision variables such as expected rewards, the subjective desirability during reward-guided decisions (14)(15)(16)(17)(18), and the certainty in perceptual decisions (19). Decisions are made within a network that extends across many regions of the brain (20)(21)(22)(23)(24)(25), so efficient and flexible mechanisms are required to enable distributed computations.…”

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confidence: 99%

Temporal coding of reward-guided choice in the posterior parietal cortex

Hawellek

Wong

Pesaran

2016

Proc. Natl. Acad. Sci. U.S.A.

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Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks.decision making | phase-of-firing | neural code | saccade | reach T he posterior parietal cortex (PPC) integrates sensory signals for impending actions. Firing rates of neurons in the PPC encode movement intention and the temporal evolution of movement choices (1-13) as well as decision variables such as expected rewards, the subjective desirability during reward-guided decisions (14-18), and the certainty in perceptual decisions (19). Decisions are made within a network that extends across many regions of the brain (20-25), so efficient and flexible mechanisms are required to enable distributed computations. Dynamic and frequency-specific correlations in activity between brain areas are ubiquitous and offer potential physiological mechanisms supporting distributed computations in decision networks (13,21,(26)(27)(28)(29)(30)(31)(32)(33)(34). We hypothesize that the encoding of decisions in the PPC should depend on the temporal structure present in neuronal activity. Previous studies have shown that coherently active ensembles of cells in the PPC predict reaction times (9) and movement choices (13, 21) better than neurons without coherent dynamics. However, whether and how the coding of decisions depends on the temporally structured firing of PPC neurons remains unknown. We examined temporal structure in the encoding of look-reach decisions by PPC neurons. ResultsWe analyzed the activity of 149 units [97 units (31 single units and 66 multiun...

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Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

Cited by 298 publications

References 34 publications

Mice plan decision strategies based on previously learned time intervals, locations, and probabilities

Mice plan decision strategies based on previously learned time intervals, locations, and probabilities

Active Inference: A Process Theory

Temporal coding of reward-guided choice in the posterior parietal cortex

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