48 research outputs found

    MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation

    Get PDF
    Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation

    Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

    Full text link
    [EN] To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.This work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (T.W.) and the Natural Sciences and Engineering Research Council of Canada (T.W.), as well as by the Brain in Action CREATE-IRTG program (M.O. and T.W.), and by grant LPDS 2012-08 from the Deutsche Akademie der Naturforscher Leopoldina (S.W.). Imaging data provided by the Duke Center for In Vivo Microscopy, an NIH Biomedical Technology Resource (NIHP41EB015897, 1S10OD010683-01). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. The authors would like to thank Hongying Wang for technical supportOemisch, M.; Westendorff, S.; Azimi, M.; Hassani, SA.; Ardid-Ramírez, JS.; Tiesinga, P.; Womelsdorf, T. (2019). Feature-specific prediction errors and surprise across macaque fronto-striatal circuits. Nature Communications. 10:1-15. https://doi.org/10.1038/s41467-018-08184-9S11510Farashahi, S., Rowe, K., Aslami, Z., Lee, D. & Soltani, A. Feature-based learning improves adaptability without compromising precision. Nat. Commun. 8, 1768 (2017).Hikosaka, O., Ghazizadeh, A., Griggs, W. & Amita, H. Parallel basal ganglia circuits for decision making. J. Neural Transm. 1–15 (2017). https://doi.org/10.1007/s00702-017-1691-1Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic Interaction between reinforcement learning and attention in multidimensional environments. Neuron 93, 451–463 (2017).Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015).Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction. Vol. 135. Cambridge: MIT Press (1998).Gottlieb, J. Attention, learning and the value of information. Neuron 76, 281–295 (2012).Pearce, J. & Hall, G. A model for Pavlovian learning: variation in the effectiveness of conditioned but not unconditioned stimuli. Psychol. Rev. 87, 532–552 (1980).Daddaoua, N., Lopes, M. & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci. Rep. 6, 1–15 (2016).Dayan, P., Kakade, S. & Montague, P. R. Learning and selective attention. Nat. Neurosci. 3, 1218–1223 (2000).Hassani, S. A. et al. A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque. Sci. Rep. 7, 1–19 (2017).Wilson, R. C. & Niv, Y. Inferring relevance in a changing world. Front. Hum. Neurosci. 5, 1–14 (2012).Kruschke, J. K. & Hullinger, R. A. Evolution of attention in learning. Comput. Models Condition. (2010). https://doi.org/10.1017/CBO9780511760402.002Asaad, W. F., Lauro, P. M., Perge, J. A. & Eskandar, E. N. Prefrontal neurons encode a solution to the credit assignment problem. J. Neurosci. 37, 3311–3316 (2017).Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).Dias, R., Robbins, T. W. & Roberts, A. C. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 380, 69–72 (1996).Glimcher, P. W. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc. Natl Acad. Sci. USA 108 Suppl, 15647–15654 (2011).Bichot, N. P., Heard, M. T., DeGennaro, E. M. & Desimone, R. A source for feature-based attention in the prefrontal cortex. Neuron 88, 832–844 (2015).Kaping, D., Vinck, M., Hutchison, R. M., Everling, S. & Womelsdorf, T. Specific contributions of ventromedial, anterior cingulate, and lateral prefrontal cortex for attentional selection and stimulus valuation. PLoS Biol. 9, e1001224 (2011).Alexander, W. H. & Brown, J. W. Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Comput. 27, 2354–2410 (2015).Sutton, R. S. & Barto, A. G. Reinforcement Learning: an Introduction. 322 (1998). https://doi.org/10.1109/TNN.1998.712192Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comput. 17, 2176–2214 (2005).Rombouts, J. O., Bohte, S. M. & Roelfsema, P. R. How attention can create synaptic tags for the learning of working memories in sequential tasks. PLoS Comput. Biol. 11, 1–34 (2015).Balcarras, M., Ardid, S., Kaping, D., Everling, S. & Womelsdorf, T. Attentional selection can be predicted by reinforcement learning of task-relevant stimulus features weighted by value-independent stickiness. J. Cogn. Neurosci. 28, 333–349 (2016).Smith, A. C. et al. Dynamic analysis of learning in behavioral experiments. J. Neurosci. 24, 447–461 (2004).Kennerley, S. W., Behrens, T. E. J. & Wallis, J. D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nat. Neurosci. 14, 1581–1589 (2011).Asaad, W. F. & Eskandar, E. N. Encoding of both positive and negative reward prediction errors by neurons of the primate lateral prefrontal cortex and caudate nucleus. J. Neurosci. 31, 17772–17787 (2011).Hayden, B. Y., Heilbronner, S. R., Pearson, J. M. & Platt, M. L. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 31, 4178–4187 (2011).Schultz, W. Dopamine reward prediction error coding. Dialogues Clin. Neurosci. 18, 23–32 (2016).Izquierdo, A., Brigman, J. L., Radke, A. K., Rudebeck, P. H. & Holmes, A. The neural basis of reversal learning: an updated perspective. Neuroscience 345, 12–26 (2017).Fiorillo, C. D., Tobler, P. N. & Schultz, W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299, 1898–1902 (2003).Schultz, W. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27 (1998).Ardid, S. et al. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J. Neurosci. 35, 2975–2991 (2015).Berke, J. D. Uncoordinated firing rate changes of striatal fast-spiking interneurons during behavioural task performance. J. Neurosci. 28, 10075–10080 (2008).Lansink, C. S., Goltstein, P. M., Lankelma, J. V. & Pennartz, C. M. A. Fast-spiking interneurons of the rat ventral striatum: temporal coordination of activity with principal cells and responsiveness to reward. Eur. J. Neurosci. 32, 494–508 (2010).Kawaguchi, Y. Physiological, morphological, and histochemical characterization of three classes of interneurons in rat neostriatum. J. Neurosci. 13, 4908–4923 (1993).Shen, C. et al. Anterior cingulate cortex cells identify process-specific errors of attentional control prior to transient prefrontal-cingulate inhibition. Cereb. Cortex 25, 2213–2228 (2015).Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016).Quilodran, R., Rothé, M. & Procyk, E. Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57, 314–325 (2008).Kennerley, S. W., Dahmubed, A. F., Lara, A. H. & Wallis, J. D. Neurons in the frontal lobe encode the value of multiple decision variables. J. Cogn. Neurosci. 21, 1162–1178 (2009).Womelsdorf, T., Johnston, K., Vinck, M. & Everling, S. Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors. Proc. Natl Acad. Sci. 107, 5248–5253 (2010).Oemisch, M., Westendorff, S., Everling, S. & Womelsdorf, T. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J. Neurosci. 35, 13076–13089 (2015).Voloh, B., Valiante, T. A., Everling, S. & Womelsdorf, T. Theta-gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts. Proc. Natl Acad. Sci. USA 112, 8457–8462 (2015).Westendorff, S., Kaping, D., Everling, S. & Womelsdorf, T. Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior. J. Neurophysiol. 116, 796–811 (2016).Womelsdorf, T. & Everling, S. Long-range attention networks: circuit motifs underlying endogenously controlled stimulus selection. Trends Neurosci. 38, 682–700 (2015).Medalla, M. & Barbas, H. Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control. Neuron 61, 609–620 (2009).Antzoulatos, E. G. & Miller, E. K. Increases in functional connectivity between prefrontal cortex and striatum during category learning. Neuron 83, 216–225 (2014).Womelsdorf, T., Ardid, S., Everling, S. & Valiante, T. A. Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control. Curr. Biol. 1–9 (2014). https://doi.org/10.1016/j.cub.2014.09.046Hunt, L. T. & Hayden, B. Y. A distributed, hierarchical and recurrent framework for reward-based choice. Nat. Rev. Neurosci. 18, 172–182 (2017).Kable, J. W. & Glimcher, P. W. The neurobiology of decision: consensus and controversy. Neuron 63, 733–745 (2009).Badre, D. & Nee, D. E. Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22, 170–188 (2018).Tian, J. et al. Distributed and mixed information in monosynaptic inputs to dopamine neurons. Neuron 1374–1389 (2016). https://doi.org/10.1016/j.neuron.2016.08.018den Ouden, H. E. M., Kok, P. & de Lange, F. P. How prediction errors shape perception, attention, and motivation. Front. Psychol. 3, 1–12 (2012).Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).Genovesio, A., Wise, S. P. & Passingham, R. E. Prefrontal—parietal function: from foraging to foresight. Trends Cogn. Sci. 18, 72–81 (2014).Donahue, C. H. & Lee, D. Dynamic routing of task-relevant signals for decision making in dorsolateral prefrontal cortex. Nat. Neurosci. 18, 1–9 (2015).Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).Berke, J. D. Functional properties of striatal fast-spiking interneurons. Front. Syst. Neurosci. 5, 1–7 (2011).Hennequin, G., Agnes, E. J. & Vogels, T. P. Inhibitory plasticity: balance, control, and codependence. Annu. Rev. Neurosci. 40, 557–579 (2017).Wilson, F. A., O’Scalaidhe, S. P. & Goldman-Rakic, P. S. Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. Proc. Natl Acad. Sci. 91, 4009–4013 (1994).Lee, K. et al. Parvalbumin interneurons modulate striatal output and enhance performance during associative learning. Neuron 93, 1451–1463.e4 (2017).Vogels, T. P., Sprekeler, H., Zenke, F., Clopath, C. & Gerstner, W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569–1573 (2011).Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N. & Wills, A. J. Attention and associative learning in humans: an integrative review. Psychol. Bull. 142, 1111–1140 (2016).Courville, A. C., Daw, N. D. & Touretzky, D. S. Bayesian theories of conditioning in a changing world. Trends Cogn. Sci. 10, 294–300 (2006).Gottlieb, J., Hayhoe, M., Hikosaka, O. & Rangel, A. Attention, reward, and information seeking. J. Neurosci. 34, 15497–15504 (2014).Rusch, T., Korn, C. W. & Gläscher, J. A two-way street between attention and learning. Neuron 93, 256–258 (2017).Takahashi, Y. K., Langdon, A. J., Niv, Y. & Schoenbaum, G. Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum. Neuron 91, 182–193 (2016).Watabe-Uchida, M., Eshel, N. & Uchida, N. Neural circuitry of reward prediction error. Annu. Rev. Neurosci. 40, 373–394 (2017).Krauzlis, R. J., Bollimunta, A., Arcizet, F. & Wang, L. Attention as an effect not a cause. Trends Cogn. Sci. 18, 457–464 (2014).Lovejoy, L. P. & Krauzlis, R. J. Inactivation of primate superior colliculus impairs covert selection of signals for perceptual judgments. Nat. Neurosci. 13, 261–266 (2010).Rasmussen, D., Voelker, A. & Eliasmith, C. A neural model of hierarchical reinforcement learning. PLoS ONE 12, e0180234 (2017).Fusi, S., Asaad, W. F., Miller, E. K. & Wang, X. J. A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. Neuron 54, 319–333 (2007).Roelfsema, P. R. & Holtmaat, A. Control of synaptic plasticity in deep cortical networks. Nat. Rev. Neurosci. 19, 166–180 (2018).Calabrese, E. et al. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage 117, 408–416 (2015).Bakker, R., Tiesinga, P. & Kötter, R. The scalable brain atlas: instant web-based access to public brain atlases and related content. Neuroinformatics 13, 353–366 (2015)

    Dose-dependent dissociation of pro-cognitive effects of donepezil on attention and cognitive flexibility in rhesus monkeys

    Get PDF
    BACKGROUND Donepezil exerts pro-cognitive effects by non-selectively enhancing acetylcholine (ACh) across multiple brain systems. Two brain systems that mediate pro-cognitive effects of attentional control and cognitive flexibility are the prefrontal cortex and the anterior striatum which have different pharmacokinetic sensitivities to ACh modulation. We speculated that these area-specific ACh profiles lead to distinct optimal dose-ranges for donepezil to enhance the cognitive domains of attention and flexible learning. METHODS To test for dose-specific effects of donepezil on different cognitive domains we devised a multi-task paradigm for nonhuman primates (NHPs) that assessed attention and cognitive flexibility. NHPs received either vehicle or variable doses of donepezil prior to task performance. We measured donepezil intracerebral and how strong it prevented the breakdown of ACh within prefrontal cortex and anterior striatum using solid-phase-microextraction neurochemistry. RESULTS The highest administered donepezil dose improved attention and made subjects more robust against distractor interference, but it did not improve flexible learning. In contrast, only a lower dose range of donepezil improved flexible learning and reduced perseveration, but without distractor-dependent attentional improvement. Neurochemical measurements confirmed a dose-dependent increase of extracellular donepezil and decreases in choline within the prefrontal cortex and the striatum. CONCLUSIONS The donepezil dose for maximally improving attention differed from the dose range that enhanced cognitive flexibility despite the availability of the drug in two major brain systems supporting these functions. These results suggest that in our small cohort of adult monkeys donepezil traded improvements in attention for improvements in cognitive flexibility at a given dose range.National Institute of Mental Healt

    Der Einfluss von räumlicher Aufmerksamkeit auf die Struktur rezeptiver Felder im superior-temporalen Kortex des Rhesusaffen

    No full text
    Der Einfluss von räumlicher Aufmerksamkeit auf die Struktur rezeptiver Felder im superior-temporalen Kortex des RhesusaffenSelektive Aufmerksamkeit führt zu einer multiplikativen Verstärkung von Einzellzellaktivität im sensorischen Cortex. Modulation durch Aufmerksamkeit ist besonders ausgeprägt, wenn zwei verschiedene Reize das gleiche rezeptive Felde (RF) aktivieren, aber nur einer beachtet wird. Dieser Effekt kann entweder (1) auf der räumlichen Veränderung des RF basieren, das um den beachteten Reiz schrumpft und so den Einfluss des irrelevanten Reizen filtert, oder der Effekt kann (2) durch multiplikative Verstärkung der neuronalen Aktivität erklärt werden, ohne Verschiebung und Grössenveränderung eines RF. Die vorliegende Arbeit untersucht diese Hypothesen direkt durch extrazelluläre Ableitungen in Area MT des Rhesusaffen, indem die Struktur rezeptiver Felder quantitativ gemessen wird, während Aufmerksamkeit auf einen von zwei Reizen innerhalb des RF ausgerichtet ist, oder auf einen Reiz ausserhalb des RF.Unser Experiment kontrolliert die Ausrichtung von Aufmerkmsamkeit mit einem räumlichen Hinweisreiz auf einen von drei Reizen mit sich bewegenden Zufallspunkten, in denen eine Richtungsänderung detektiert werden muss. Zwei dieser Reize werden immer innerhalb des RF einer isolierten Einzelzelle dargeboten, während zeitgleich das RF mit einem weiteren, verhaltensirrelevanten Reiz quantitiativ gemessen wird, der an den Schnittpunkten eines virtuellen, ellipsoiden Netzes dargeboten wurde.Unsere Ergebnisse zeigen, dass (1) die neuronale räumliche Sensitivität (das RF) systematisch und räumlich spezifisch zu dem Fokus der Aufmerksamkeit verschoben ist und lediglich (2) eine geringe Schrumpfung der RF. Zudem können wir zeigen, dass (3) sich RF um so mehr verschieben, je weiter sie zum beachteten Reiz innerhalb des RF liegen und dass (4) die Verschiebung während der fokussierten Aufmerksamkeit konstant nachweisbar ist und nicht durch transiente Helligkeitseffekte hervorgerufen wird.Diese Ergebnisse weisen erstmals eine hohe räumlich-spezifische Plastizität neuronaler RF nach, die durch selektive Aufmerksamkeit innerhalb neuronaler RF induziert wird - unter komplett randomisierter Darbietung der Bedingungen, mit Reizen an identischer visueller Exzentrizität und gleicher Aufgabenschwierigkeit der experimentellen Bedingungen. Obgleich die räumliche RF Verschiebung eine nicht-multiplikative Modulation sensorischer Aktivität reflektiert, kann sie durch multiplikative Verstärkung erklärt werden, unter der Annahme, dass Aufmerksamkeit die Antworten von präsynaptisch, afferenten Neuronen moduliert, deren RF kleiner sind und mit der Grösse einzelner zu beachtender Reize übereinstimmt. Die Erhöhung neuronaler räumlicher Sensitivität in der Nähe des Aufmerksamkeitsfokus könnte das neuronale Korrelat verschiedener perzeptuell-psychophysischer Phenomene sein, wie z.B. (1) der Verteilung von fazilitativen Effekten nahe - und inhibitorischen Effekte fern vom Aufmerksamkeitsfokus, (2) von erhöhter räumlicher Auflösung und (3) von Verzerrungen in der Raumwahrnehmung. AUf der Ebene von Zellpopulationen entspricht die RF Verschiebung einer Rekrutierung von Zellen (einer Erhöhung kortikaler Vergrösserung der Repräsentation) nahe am Aufmerksamkeitsfokus

    Changes of Attention during Value-Based Reversal Learning Are Tracked by N2pc and Feedback-Related Negativity

    No full text
    Previously learned reward values can have a pronounced impact, behaviorally and neurophysiologically, on the allocation of selective attention. All else constant, stimuli previously associated with a high value gain stronger attentional prioritization than stimuli previously associated with a low value. The N2pc, an ERP component indicative of attentional target selection, has been shown to reflect aspects of this prioritization, by changes of mean amplitudes closely corresponding to selective enhancement of high value target processing and suppression of high value distractor processing. What has remained unclear so far is whether the N2pc also reflects the flexible and repeated behavioral adjustments needed in a volatile task environment, in which the values of stimuli are reversed often and unannounced. Using a value-based reversal learning task, we found evidence that the N2pc amplitude flexibly and reversibly tracks value-based choices during the learning of reward associated stimulus colors. Specifically, successful learning of current value-contingencies was associated with reduced N2pc amplitudes, and this effect was more apparent for distractor processing, compared with target processing. In addition, following a value reversal the feedback related negativity(FRN), an ERP component that reflects feedback processing, was amplified and co-occurred with increased N2pc amplitudes in trials following low-value feedback. Importantly, participants that showed the greatest adjustment in N2pc amplitudes based on feedback were also the most efficient learners. These results allow further insight into how changes in attentional prioritization in an uncertain and volatile environment support flexible adjustments of behavior

    Symmetric and Asymmetric Causal Neural Firing Interactions across Striatum, Anterior Cingulate and Prefrontal Cortex during Cognitive Control

    No full text
    Generally, successful performance of complex cognitive tasks depends on the function of interacting regions, including anterior cingulate cortex (ACC), lateral prefrontal cortex (LPFC) and ventral striatum (VS). During task performance, markers of communication amongst regions in the cognitive control network are frequently observed. Typically, however, these markers are agnostic with respect to the direction in which information passes through the system – although firing rate correlations and enhanced synchrony between regions are suggestive of causal interactions, these approaches are not sufficient for determining the influence of on region on another. In this manuscript, we apply a novel approach to investigate the causal dynamics of regions in the cognitive control network during correct and incorrect performance. Using experiment-averaged time courses recorded from ACC, LPFC, and VS, we identify neurons within each region exhibiting task-sensitive response dynamics and calculate Granger causality for each pair of neurons during salient task events. Cluster analysis of causality time courses identifies significant, bidirectional causality between regions following stimulus and feedback onset
    corecore