Pupil Dynamics Reflect Behavioral Choice and Learning in a Go/NoGo Tactile Decision-Making Task in Mice

Lee, Christian R.; Margolis, David J.

doi:10.3389/fnbeh.2016.00200

Cited by 53 publications

(75 citation statements)

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“…To determine the effect of S1-or M1-corticostriatal input on sensorimotor behavior, we assessed how optogenetic stimulation of these regionally specific corticostriatal inputs affected performance of a whisker-dependent texture discrimination task in head-fixed mice ( Figure 3A-C ) [54,56,59] . In this Go/NoGo task, mice receive water reward for correct licking in response to the rough texture (Hit trial) and no water reward for licking in response to the smooth texture (False Alarm trial).…”

Section: Input-specific Effects On Behavioral Performancementioning

confidence: 99%

“…Following the implant surgery, mice were allowed to recover for an additional week prior to handling. A custom head post was fitted to each mouse using methods similar to those previously described [54,56,59,81] . Briefly, mice were anesthetized with isoflurane (4% induction, 0.8-1.5% maintenance) and mounted onto a stereotaxic frame (Stoelting) with a feedback controlled heating blanket maintained at 36℃ (FHC) on the base.…”

Section: Optical Cannula Implantation Surgerymentioning

confidence: 99%

“…Head-fixed mice were trained to utilize their whiskers to discriminate between two textures presented to the whiskers on a motorized stage in a random order based on custom-written code in LabVIEW (National Instruments). This software controlled a linear stage and stepper motor similar to previously described [59,84] . The stepper motor rotated arms holding the two textures.…”

Section: Go/no-go Tactile Discrimination Taskmentioning

confidence: 99%

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Opposing Influence of Sensory and Motor Cortical Input on Striatal Circuitry and Choice Behavior

Lee

Yonk

Wiskerke

et al. 2018

Preprint

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The striatum is the main input nucleus of the basal ganglia and is a key site of sensorimotor integration. While the striatum receives extensive excitatory afferents from the cerebral cortex, the influence of different cortical areas on striatal circuitry and behavior is unknown. Here we find that corticostriatal inputs from whisker-related primary somatosensory (S1) and motor (M1) cortex differentially innervate projection neurons and interneurons in the dorsal striatum, and exert opposing effects on sensory-guided behavior. Optogenetic stimulation of S1-corticostriatal afferents in ex vivo recordings produced larger postsynaptic potentials in striatal parvalbumin (PV)-expressing interneurons than D1-or D2-expressing spiny projection neurons (SPNs), an effect not observed for M1-corticostriatal afferents. Critically, in vivo optogenetic stimulation of S1-corticostriatal afferents produced task-specific behavioral inhibition, which was bidirectionally modulated by striatal PV interneurons. Optogenetic stimulation of M1 afferents produced the opposite behavioral effect. Thus, our results suggest opposing roles for sensory and motor cortex in behavioral choice via distinct influences on striatal circuitry. Results Optogenetic activation of S1 or M1 corticostriatal afferents differentially engage striatal neuronsWe used optogenetic activation of corticostriatal afferents from S1 or M1 with channelrhodopsin-2 (ChR2) combined with ex vivo whole cell current clamp recordings of identified striatal neurons to determine the striatal circuitry engaged by corticostriatal input from different sources ( Figure 1A ). We chose to investigate the anterior dorsal striatum because of prominent overlap between S1 and M1 inputs in this region ( Supplemental Figure 1 ) [3,4] . Striatal D1-SPNs, D2-SPNs and PV interneurons were identified using tdTomato-expressing reporter mice and verified through their intrinsic properties in response to hyperpolarizing and depolarizing current injection (see Methods) ( Figure 1B-D ). We used current clamp recordings in the absence of inhibitory synaptic blockers, as opposed to voltage clamp recordings, to more closely mirror the natural physiological activity of striatal circuitry in response to S1 or M1 inputs. Activation of ChR2-expressing S1 corticostriatal afferents with a single, full-field light pulse (2.5 ms, 460-500 nm; through the 40x microscope objective) induced a depolarizing postsynaptic potential (PSP) in D1-SPNs, D2-SPNs and PV interneurons ( Figure 1E-G ). PSP amplitude was larger in PV interneurons (12.81 ± 2.37 mV, n = 9 neurons from 8 mice) compared to D1-SPNs (2.73 ± 0.71 mV, n = 17 neurons from 10 mice, p = 0.000008) or D2-SPNs (3.23 ± 0.95 mV, n = 10 neurons from 6 mice, p = 0.0001) (F (2, 33) = 17.64, p = 0.000006; Figure 1H ). Suprathreshold responses were sometimes encountered in PV interneurons, but only rarely in D1-or D2-SPNs. Measurable PSPs were found in all recorded neurons, suggesting that S1 innervates D1-, D2-SPNs and PV interneurons with high probability, but tha...

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Section: Input-specific Effects On Behavioral Performancementioning

confidence: 99%

Section: Optical Cannula Implantation Surgerymentioning

confidence: 99%

Section: Go/no-go Tactile Discrimination Taskmentioning

confidence: 99%

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Opposing Influence of Sensory and Motor Cortical Input on Striatal Circuitry and Choice Behavior

Lee

Yonk

Wiskerke

et al. 2018

Preprint

Self Cite

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“…In the current experiments, we examined how the pupil response elicited by cues during learning was linked to changes in error‐driven attention predicted by the Pearce‐Hall theory. Across a wide range of experimental paradigms, pupil size has been previously reported to be linked not only to learning (Eldar, Cohen, & Niv, ; Kahneman & Peavler, ; Lee & Margolis, ; Nassar et al, ; Reinhard & Lachnit, ; Reinhard, Lachnit, & Koenig, ) and memory (Brocher & Graf, ; Naber, Fraessle, Ruitshäuser, & Einhäuser, ; van Rijn, Dalenberg, Borst, & Sprenger, ), but also to attentional orienting (Corneil & Munoz, ; Geva, Zivan, Warsha, & Olchik, ; Lynn, ; Sokolov, ; Wang & Munoz, ) and the processing of uncertainty and error (Jepma & Nieuwenhuis, ; Nassar et al, ; O'Reilly et al, ; Preuschoff, t' Hart, & Einhäuser, ; Richer & Beatty, ; Satterthwaite et al, ). In extension of these previous findings, the current article examines how changes in pupil size during learning correspond to changes in error and error‐driven attention.…”

Section: Introductionmentioning

confidence: 99%

Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory

2017

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The attentional learning theory of Pearce and Hall (1980) predicts more attention to uncertain cues that have caused a high prediction error in the past. We examined how the cue-elicited pupil dilation during associative learning was linked to such errordriven attentional processes. In three experiments, participants were trained to acquire associations between different cues and their appetitive (Experiment 1), motor (Experiment 2), or aversive (Experiment 3) outcomes. All experiments were designed to examine differences in the processing of continuously reinforced cues (consistently followed by the outcome) versus partially reinforced, uncertain cues (randomly followed by the outcome). We measured the pupil dilation elicited by the cues in anticipation of the outcome and analyzed how this conditioned pupil response changed over the course of learning. In all experiments, changes in pupil size complied with the same basic pattern: During early learning, consistently reinforced cues elicited greater pupil dilation than uncertain, randomly reinforced cues, but this effect gradually reversed to yield a greater pupil dilation for uncertain cues toward the end of learning. The pattern of data accords with the changes in prediction error and error-driven attention formalized by the Pearce-Hall theory. K E Y W O R D Sassociative learning, attention, human fear conditioning, pupil dilation, reward | IN TRO DUCT IO NAssociative learning theories assume that repeated observations of the co-occurrence of two external events will lead to the formation of an association that links the representation of both events in memory (Pearce & Bouton, 2001;Rescorla, 1988). However, the association between cues and a significant outcome like reward (or punishment) often is acquired in an uncertain environment, where the contingency between cues and the outcome is not perfect but rather probabilistic. From this perspective, the learning process involves two important variables: (a) the expectancy of the outcome given the cue, and (b) the uncertainty of that expectation. For example, if three different cues were followed by reward on 0%, 50%, and 100% of their past occurrences, these cues differ with respect to reward expectancy and uncertainty. The 0% cue elicits low reward expectancy and low uncertainty. The 100% cue elicits high reward expectancy and low uncertainty. The 50% cue is partially reinforced and elicits an intermediate reward expectancy but high uncertainty whether reward will occur or not. From the perspective of associative learning theories, this uncertainty results from the fact that the 50% cue in the past had been associated with a higher prediction error, whereas in contrast this error in predicting the omission or occurrence of the outcome was small for the 0% cue and the 100% cue.

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“…In the current study we show that when in a state of engagement, defined as task responsiveness, large dilations are observed following stimulation, with larger dilations for correct than error responses ( Fig 3A). However, this difference might be reward driven (Lee and Margolis, 2016).…”

Section: Discussionmentioning

confidence: 99%

Pupillary dilations of mice performing a vibrotactile discrimination task reflect task engagement and response confidence

Ganea

Bexter

Guenther

et al. 2018

Preprint

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Pupillometry, the measure of pupil size and reactivity, has been widely used to assess cognitive processes. As such, changes in pupil size have been shown to correlate with arousal, locomotion, cortical state and decision-making processes. In addition, pupillary responses have been linked to the activity of neuromodulatory systems that modulate attention and perception as the noradrenergic and cholinergic systems. Due to the extent of processes reflected by the pupil, we aimed at resolving pupillary responses in context of behavioral state and task performance while recording pupillary transients of mice performing a vibrotactile two-alternative forced choice task (2-AFC). We show that pre-stimulus pupil size differentiates between states of disengagement from task performance versus active engagement. In addition, when actively engaged, post-stimulus, pupillary dilations for correct responses are larger than for error responses with this difference reflecting response confidence. Importantly, in a delayed 2-AFC task version, we show that even though pupillary transients mainly reflect motor output or reward anticipation following the response of the animal, they also reflect animal decision confidence prior to its response. Finally, in a condition of passive engagement, when stimulus has no task relevance with reward provided automatically, pupillary dilations reflect stimulation and reward being reduced relative to a state of active engagement explained by shifts of attention from task variables. Our results provide further evidence for how pupillary dilations reflect cognitive processes in a task relevant context, showing that the pupil reflects response confidence and baseline pupil size encodes attentiveness rather than general arousal. Significance StatementFor the last 60 years, pupillometry has been used to study various cognitive processes. Among which are mental load, arousal and various decision related components, linking pupil dilations to underlying neuromodulatory systems. Our results provide extensive evidence that in addition to reflecting attentiveness under task performance, pupil dilations also reflect the confidence of the subject in his ensuing response. This confidence coding is overlaid within a more pronounced pupil dilation that reflects motor output or other post-decision components such that are related to the response itself but not to the decision. Our results also provide evidence how different behavioral states, imposed by task demands, modulate what the pupil is reflecting, presumably showing what the underlying cognitive network is coding for.

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Pupil Dynamics Reflect Behavioral Choice and Learning in a Go/NoGo Tactile Decision-Making Task in Mice

Cited by 53 publications

References 54 publications

Opposing Influence of Sensory and Motor Cortical Input on Striatal Circuitry and Choice Behavior

Opposing Influence of Sensory and Motor Cortical Input on Striatal Circuitry and Choice Behavior

Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory

Pupillary dilations of mice performing a vibrotactile discrimination task reflect task engagement and response confidence

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