Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice - PubMed (original) (raw)
Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice
Onyekachi Odoemene et al. J Neurosci. 2018.
Abstract
The ability to manipulate neural activity with precision is an asset in uncovering neural circuits for decision-making. Diverse tools for manipulating neurons are available for mice, but their feasibility remains unclear, especially when decisions require accumulating visual evidence. For example, whether mice' decisions reflect leaky accumulation is unknown, as are the relevant/irrelevant factors that influence decisions. Further, causal circuits for visual evidence accumulation are poorly understood. To address this, we measured decisions in mice judging the fluctuating rate of a flash sequence. An initial analysis (>500,000 trials, 29 male and female mice) demonstrated that information throughout the 1000 ms trial influenced choice, with early information most influential. This suggests that information persists in neural circuits for ∼1000 ms with minimal accumulation leak. Next, in a subset of animals, we probed strategy more extensively and found that although animals were influenced by stimulus rate, they were unable to entirely suppress the influence of stimulus brightness. Finally, we identified anteromedial (AM) visual area via retinotopic mapping and optogenetically inhibited it using JAWS. Light activation biased choices in both injected and uninjected animals, demonstrating that light alone influences behavior. By varying stimulus-response contingency while holding stimulated hemisphere constant, we surmounted this obstacle to demonstrate that AM suppression biases decisions. By leveraging a large dataset to quantitatively characterize decision-making behavior, we establish mice as suitable for neural circuit manipulation studies. Further, by demonstrating that mice accumulate visual evidence, we demonstrate that this strategy for reducing uncertainty in decision-making is used by animals with diverse visual systems.SIGNIFICANCE STATEMENT To connect behaviors to their underlying neural mechanism, a deep understanding of behavioral strategy is needed. This understanding is incomplete for mice. To surmount this, we measured the outcome of >500,000 decisions made by 29 mice trained to judge visual stimuli and performed behavioral/optogenetic manipulations in smaller subsets. Our analyses offer new insights into mice' decision-making strategies and compares them with those of other species. We then disrupted neural activity in a candidate neural structure and examined the effect on decisions. Our findings establish mice as suitable for visual accumulation of evidence decisions. Further, the results highlight similarities in decision-making strategies across very different species.
Keywords: decision-making.
Copyright © 2018 the authors 0270-6474/18/3810143-13$15.00/0.
Figures
Figure 1.
A dataset of over half a million trials demonstrates mice can be trained to make stable and reliable decisions about visual stimuli. A, Task schematic and trial structure of the three-port choice task. The mouse initiated trials and stimulus delivery by poking its nose into the center port. Mice reported whether stimuli were low-rate (left port) or high-rate (right port). Mice waited at the center port for at least 1100 ms, with the stimulus delivered after a variable delay (10–100 ms). At the end of the 1000 ms stimulus period, an auditory “Go” tone was played. Correct choices to the left or right were rewarded with a small drop of water (2 μl), incorrect choices were followed by a 2–3 s timeout. B, Percentage correct on easiest stimulus conditions (4 and 20 flashes/s) plotted across total trials experienced by the mouse. Individual mice: gray traces and average: black trace, 29 mice. Colors arbitrarily selected to facilitate distinguishing subjects. C, Psychometric function fit for individual mouse from single session (494 trials; Error bars indicate Wilson binomial confidence intervals), and (D) data from 29 mice averaged across multiple sessions (537,288 trials). Individual mice, Colored traces; average, black trace. E, Parameter values from the model fit for each mouse in the cohort. Error bars indicate SEM calculated via bootstrapping (Palamedes Toolbox). Blue points indicate the 11 animals that were used for additional manipulations (11/11 were used for optogenetic or control experiments and 10/11 were used for the brightness manipulation).
Figure 2.
Decisions in mice and rats reflect evidence presented throughout the trial. A, Schematics indicating possible shapes of psychophysical kernels and the strategies they reveal. B, Psychophysical kernel based on pooled data from 29 mice (537,288 trials). C, D, Psychophysical kernels from example mice that typify early weighting and flat weighting. E, Psychophysical kernels from two rats (26,890 trials). Gray traces, Individual subjects; black trace, average. Values were >0 throughout the trial for almost all subjects, demonstrating that stimuli presented throughout the 1000 ms duration influence the animal's eventual choice. F, Scatter plot relating the weight of evidence early versus late in the trial. Error bars indicate SEM. Dashed line: x = y. G, Effect of previous decision outcome (success/failure) on current choice.
Figure 3.
Stimulus brightness influences rate decisions. A, Schematic of the uniform brightness manipulation experiment. The intensity of individual flashes was varied such that all flashes were dimmer or brighter than normal on 5% of randomly selected trials. B, Left, Stimulus spaces and decision planes (gray lines). Right, Predicted psychometric functions. Each row reflects a candidate way in which the stimulus in each condition would influence decisions given the strategy indicated in the label. Top, Stimulus rate. Middle, Stimulus brightness. Bottom, Hybrid strategy in which both features are used. C, Measured psychometric functions. Left, Eight mice: 108,547 trials. Right, Two rats: 26,201 trials. Points, Subjects' responses; solid line, four-parameter cumulative normal psychometric function fit to the data. Error bars indicate Wilson binomial 95% confidence intervals. D, Schematic of uncorrelated brightness manipulation experiment. The intensity of individual flashes was scaled inversely with the flash rate on 5% of randomly selected trials. All sequences have the same cumulative brightness, independent of flash rate. E, Same as B but for the manipulation in D. F, Measured psychometric functions. Left, Two mice: 6326 trials. Right, Two rats: 9946 trials. Points, Subjects' responses; solid line, four-parameter cumulative normal psychometric function fit to the data. Error bars indicate Wilson binomial 95% confidence intervals.
Figure 4.
Retinotopic Mapping allows precise localization of visual areas for subsequent manipulation. A, Altitude and (B) azimuth phase maps. C, Visual field sign map with labeled visual areas. D, Visual area borders overlaid on photograph of skull.
Figure 5.
Long wavelength laser stimulation biases decisions in control animals. A, Experimental configuration. Mice were injected with AAV-GFP and implanted with a fiber in right hemisphere area AM. B, Psychometric function without masking red light (2 mice; 2011 Laser-off trials, 610 Laser-on trials). Irradiance was 32 mW/mm2. C, Psychometric performance with masking red light (2 mice) with easiest flash rate conditions (2699 Laser-off trials, 823 Laser-on trials). D, Same as C but for sessions including multiple flash rates (2866 Laser-off trials, 903 Laser-on trials). Irradiance was 64 mW/mm2.
Figure 6.
JAWS Photoinhibition of visual area AM. A, Schematic of experimental configuration of AM photoinhibition experiment. Group A mice trained on the contingency: High-Rate, go LEFT and Group B mice trained on the reverse contingency: High-Rate, go RIGHT. Both groups of mice were injected with JAWS virus and implanted with an optical fiber on the left hemisphere. Photoinhibition occurred on 25% of trials during the stimulus period. B, Predicted behavioral outcomes of AM photoinhibition. Top, Predictions for neural manipulation alone. Left, If AM inhibition drives a high-rate bias, the outcomes would be similar for the two groups. Right, If AM inhibition drives a bias toward the ipsilateral side, Groups A and B would show biases in opposite directions because the high-rate side differs for Groups A and B. Bottom, Predictions for neural manipulation alongside a bias driven by the presence of visible light (as in Fig. 5). Left, If AM inhibition drives a high-rate bias, both groups would again exhibit the same bias. Right, If AM inhibition drives an ipsilateral bias, Groups A and B would again show biases in opposite directions; potentially with a very weak effect for Group B because the red light and neural manipulations are in opposition. C, Left, Decisions for Group A (left, 4 mice; n = 5722 Laser OFF trials and n = 1958 Laser ON trials) and at laser power irradiance of 64 mW/mm2. Circles represent the subject's behavioral response during laser OFF (black) and laser ON (red) trials. Solid line represents the psychometric function fit to cumulative normal. Error bars represent Wilson binomial (95%) confidence intervals. Right, Bias values for individual animals during baseline sessions (blue points) as well as inactivation sessions which included stimulation (red) and control (black) trials. Black solid lines indicate unbiased performance; dashed lines indicate the mean bias across subjects for the corresponding color. D, Same as C but for Group B (right, 3 mice; n = 4404 Laser OFF trials and n = 1381 Laser ON trials). E, Psychophysical kernels for Group A (37,025 Laser off trials; 12,952 Laser on trials). F, Psychophysical kernels for Group B (32,936 Laser off trials; 12,423 Laser on trials). Shading indicates SEM.
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