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A recent study might reconcile the ideas of distinct encoding- and consolidationdependent dopamine mechanisms (Stanek et al blood pressure up and down discount coumadin online visa. Postencoding manipulations have also been used to infer reward effects on consolidation, particularly how reward interacts with postencoding sleep or interference. Furthermore, in line with the evidence that postencoding wakeful rest enhances consolidation (Dewar, Alber, Butler, Cowan, & Della Sala, 2012), a recent unpublished study from our laboratory demonstrated that wakeful rest during a postencoding period was necessary to show the effects of reward on memory in an immediate memory test (Gruber & Ranganath, in preparation). Conforming with the idea that different dopaminergic properties can enhance memory on different timescales, these latter findings suggest that wakeful rest might facilitate early consolidation effects on salient memories. During postencoding rest periods, individual variation in resting- state functional connectivity between the hippocampus and the representational cortical areas of the encoded material correlated with the magnitude of reward-related memory enhancements for such material (Murty, Tompary, Adcock, & Davachi, 2017). The findings suggest a potential mechanism of prioritized systems consolidation for rewarded material (Murty et al. In addition, using multivoxel pattern analyses, postencoding increases in the spontaneous reactivation of high-reward hippocampal representations correlated with the magnitude of later reward-related memory enhancements (Gruber et al. The findings are in line with prioritized hippocampal consolidation mechanisms for highreward information. Concluding Remarks We reviewed the current evidence on how emotionand reward-related information is prioritized during dif ferent stages of memory formation. Several models have been proposed to explain how neuromodulators, such as norepinephrine and dopamine, contribute to the prioritization of salient information in memory. One dominant model- synaptic tag-and- capture- proposes that new memory tags capture plasticityrelated products that are available during or shortly after encoding (Redondo & Morris, 2011; Viola, Ballarini, Martinez, & Moncada, 2014), resulting in memory benefits for salient information and other information encoded around the same time. It remains to be seen whether synaptic tag- and- capture models can also explain some of the memory-impairing. This can lead to changes in the efficacy of encoding or consolidation for prioritized over nonprioritized information. Both affective and motivational states involve changes in arousal that could engage both the noradrenergic and dopaminergic pathways. Disentangling these contributions to episodic memory modulation is a key challenge for future research. Another open question is how the effects of neuromodulators on encoding processes interact with neuromodulatory effects on consolidation processes. Most studies suggest that the observed consolidation effects are independent of encoding-related processes (Gruber et al. However, other evidence indicates that the effects of postencoding arousal depend on processes engaged during encoding (Bennion, Mickley Steinmetz, Kensinger, & Payne, 2013; Dunsmoor et al. It remains to be seen whether similar interactions support the memory prioritization of rewarding events. Finally, although we focused only on the effects of negative emotion and reward on encoding and consolidation, these factors may have an additional impact on memory retrieval (Bowen, Kark, & Kensinger, 2017; Wolosin, Zeithamova, & Preston, 2013). Future research must consider the cumulative and interacting effects of neuromodulators on multiple memory processes. Topographic organization of projections from the amygdala to the visual cortex in the macaque monkey. Lesions of the human amygdala impair enhanced perception of emotionally salient events. Dorsolateral prefrontal cortex drives mesolimbic dopaminergic regions to initiate motivated behav ior. Opposing effects of negative emotion on amygdalar and hippocampal memory for items and associations. Enhanced human memory consolidation with post-learning stress: Interaction with the degree of arousal at encoding. Positive affect versus reward: Emotional and motivational influences on cognitive control. Value-based modulation of memory encoding involves strategic engagement of fronto-temporal semantic processing regions. Intrinsic functional connectivity between amygdala and hippocampus during rest predicts enhanced memory under stress. Emotional learning selectively and retroactively strengthens memories for related events. Dopamine D2-like receptor activation wipes out preferential consolidation of high over low reward memories during human sleep. Afferent modulation of dopamine neuron firing differentially regulates tonic and phasic dopamine transmission. States of curiosity modulate hippocampus- dependent learning via the dopaminergic circuit. Persistence of amygdala-hippocampal connectivity and multivoxel correlation structures during awake rest after fear learning predicts long-term expression of fear. Two routes to emotional memory: Distinct neural processes for valence and arousal. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. Arousal-mediated memory consolidation: Role of the medial temporal lobe in humans. Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures. Sharing a context with other rewarding events increases the probability that neutral events will be recollected. The effect of anticipation and the specificity of sex differences for amygdala and hippocampus function in emotional memory. Norepinephrine ignites local hot spots of neuronal excitation: How arousal amplifies selectivity in perception and memory. Differential effects of stress-induced cortisol responses on recollection and familiarity-based recognition memory. The amygdala modulates the consolidation of memories of emotionally arousing experiences. Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence. Emotional valence influences the neural correlates associated with remembering and knowing. Consolidation power of extrinsic rewards: Reward cues enhance long-term memory for irrelevant past events. Enriched encoding: Reward motivation organizes cortical networks for hippocampal detection of unexpected events. Distinct medial temporal lobe network states as neural contexts for motivated memory Gruber and Ritchey: Episodic Memory Modulation 261 formation. Selectivity in postencoding connectivity with high-level visual cortex is associated with reward-motivated memory. Motivated encoding selectively promotes memory for future inconsequential semantically-related events. Affective judgments of faces modulate early activity (approximately 160 ms) within the fusiform gyri. Level of processing modulates the neural correlates of emotional memory formation. Stress as a mnemonic filter: Interactions between medial temporal lobe encoding processes and post- encoding stress. Dissociable medial temporal pathways for encoding emotional item and context information.

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While there is no comprehensive model of num ber representation pulse pressure change during exercise best 5 mg coumadin, each model is consistent with some aspect of the behavioral and neural data from humans and animals. A neural network model by Dehaene and Changeux (1993) takes a set of spatially distributed objects and represents its numerosity as an analog estimate (fig ure 70. Objects in the location map are normalized for size and location in activity levels on the map-larger objects do not elicit greater activity than smaller objects. Activity in the location map is summed up, with larger numbers of objects causing greater activation than smaller numbers. Finally, summation clusters project to ordered numerosity Cantlon: the Nature of Human Mathematical Cognition 821 detectors that respond to preferred numerosities and exhibit the central excitation and lateral inhibition of nonpreferred numerosities. Activation decreases pro portionally with increasing numerical distance between the preferred and actual number. This model is sup ported by neural data from monkeys showing tuning neurons that behave like numerosity detectors (Nieder & Miller, 2004) and summation neurons that are con ceptually similar to summation clusters (Roitman, Bran non, & Platt, 2007). Empirical support for the other components of the model, such as the normalized loca tion map, the lateral inhibition, and the processing hierarchy, is currently lacking. This model is further limited because it only accounts for spatially distrib uted sets, not temporally distributed sets, and it is not crossmodal. Deeplearning networks engage in unsupervised learning over large amounts of input stimuli to form abstract representations that allow the future predic tion of those stimuli in the environment. A deep learning network by Stoianov and Zorzi (2012) was presented with tens of thousands of images of dot arrays that varied in number, spatial configuration, and size. The results showed that numerical representations emerge from the abstraction of visual arrays by a pro cess that spontaneously normalizes variability in the spatial features of objects and sets. Hannagan, Nieder, Viswanathan, and Dehaene (2018) provided a mathematical description of number coding based on the population coding properties of neurons. In their model, each number is encoded by a sparse, normalized vector, and the vectors for consecu tive numbers are iteratively linked because numerical codes are generated through multiplication by a fixed random matrix. Activating a particular number code n requires iterating through the whole sequence of vec tors from 0 to n. Number coding neurons are con ceived of as a vectorbased population of interrelated codes intrinsically linked by the successor function, S(n) = n + 1. This model suggests that ordered numeri cal representation could emerge spontaneously from simple constraints on neural processes. The neural network, deeplearning, and mathemati cal models are not mutually exclusive, as each explains a slightly different part or scale of the processing struc ture. These models reflect progress in formalizing a description of numerosity representation, but it remains unclear how these distinct explanations will be inte grated and elaborated to explain the whole phenome non of numerical representation. Human Uniqueness Humans have a sense of the discrete and logical proper ties of numbers that goes beyond the nonverbal "numer osity" cognition of nonhuman animals. Significant conceptual change occurs in human children as a conse quence of learning verbal counting- qualitative change that could not be achieved simply by mapping words to preverbal representations of numerosities (Carey, 2004). According to Carey (2004), the linguistic form of number, the verbal count list, "transcends the representational power" of any nonlinguistic precursors. Language appears to play a central role in transform ing primitive numeric concepts into a discrete, logical grammar-this is unsurprising because language is generally central to all human concepts. Yet human groups with or without grammatical number (singular/ plural) and lexical number (quantity words) can reason about quantities nonverbally, and some human groups communicate concepts of quantity that surpass their lexicon using body parts, gestures, or material repre sentations (Ferrigno et al. The concept of discrete, labeled cardinal numbers thus seems somewhat independent of verbal counting in humans. However, since all humans have language, the role of generative labeling (in general) could be a necessary precursor to counting. Precise ordered representations of numbers have not been observed in humans who lack symbolic counting sys tems, and no nonhuman animal has been trained suc cessfully to count despite multiple attempts, suggesting that uniquely human cognition, possibly generative labeling, is necessary to acquire counting (Matsuzawa, 2009; but see Pepperberg & Carey, 2012). Some evidence suggests that simple symbolic count ing and arithmetic abilities partly draw on nonverbal numerosity estimation mechanisms developmentally (Dillon et al. The symbolic number code for representing Arabic numerals engages the fusiform and lingual gyri of the ventral stream. Thus, while there is overlap between numerosity estimation and symbolic mathematics in the brain, particularly in parietal cor tex, they are distinct processes. For example, a strong neural predictor of higher mathematical ability in older children is hippocampal volume and the func tional connectivity of the hippocampus to the rest of the cortex (Supekar et al. Human symbolic counting and arithmetic are critically associ ated with primitive numerical cognition throughout the life span, although uniquely human demands on mathematical reasoning require semantic, linguistic, and logical processes that go beyond primitive mecha nisms and remain to be explained. Yet whatever unique cognition humans acquire, the study of numerical cog nition shows how a mechanism that began with simple set comparisons now grounds human mathematical thinking throughout development and serves as an important anchor to human rationality. Origins of the brain net works for advanced mathematics in expert mathemati cians. Agerelated changes in the activation of the intraparietal sulcus during nonsymbolic magnitude processing: An eventrelated functional mag netic resonance imaging study. Quantity judgments of auditory and visual stimuli by chimpanzees (Pan troglodytes). Rhesus macaques (Macaca mulatta) monitor uncer tainty during numerosity judgments. Format dependent representations of symbolic and non symbolic numbers in the human cortex as revealed by multivoxel pattern analyses. Conclusion Human numerical cognition at birth includes the per ception of object sets in space, time, and across modali ties as expressing a numerical quantity. The ability to conceive of quantity to make relative comparisons appears to be evolutionarily primitive across species. The natural functions of this mechanism include for aging efficiency but also comparisons of social group sizes. Processing demands such as crossmodal pro cessing and objectbased decisionmaking could have played an important role in the algorithmic and neural implementation of numerical cognition. The neural basis of numerical cognition appears to be conserved across primates in intraparietal cortex, at least in terms of basic mechanisms like summation neurons and Cantlon: the Nature of Human Mathematical Cognition 823 Cantlon, J. Neural activity during natural viewing of Sesame Street statistically predicts test scores in early childhood. Open questions and a proposal: A critical review of the evidence on infant numerical abilities. Evidence for segregated corti cocortical networks linking sensory and limbic areas with the frontal lobe. Numerical processing in the human parietal cortex during experimental and natural conditions. Cerebral pathways for calcu lation: Double dissociation between rote verbal and quanti tative knowledge of arithmetic. Cognitive science in the field: A pre school intervention durably enhances intuitive but not formal mathematics. Evidence of amodal representation of small numbers across visuo tactile modalities in 5month old infants. Foraging on patchily distributed prey by a cichlid fish (Teleostei, Cichli dae): A test of the ideal free distribution theory. Indi vidual differences in nonverbal number acuity correlate with maths achievement. Through neural stimulation to behav ior manipulation: A novel method for analyzing dynamical cognitive models. Parallels in stimulus driven oscillatory brain responses to 824 Concepts and Core Domains numerosity changes in adults and sevenmonth old infants. Symbolic estrangement: Evidence against a strong association between numerical symbols and the quantities they represent. Roaring and numerical assessment in contests between groups of female lions, Panthera leo. Functional and struc tural alterations of the intraparietal sulcus in a developmen tal dyscalculia of genetic origin. Supramodal numerosity selectivity of neu rons in primate prefrontal and posterior parietal cortices.

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The second nonexclusive possibility is that praxis depends on the transformation of visual information into a more abstract kinematic trajectory shape representation (see Wong hypertension 150 70 coumadin 2mg with mastercard, Haith, & Krakauer, 2015). Nevertheless, they perform normally when they actually reach out and grasp the shapes under visual guidance, suggesting that they may make use of visual information to perform adjustments of hand aperture and orientation based on target attributes (Buxbaum, Johnson-Frey, & Bartlett-Williams, 2005). Interestingly, these correction attempts are only successful if action recognition is intact. It is thus likely that current visual information is used in the detection of mismatches between produced actions and stored visual representations of how actions should look, similar to an "external route" by which conduction aphasics use auditory feedback to repair errors. On some accounts, anticipation of future body states depends upon kinesthetic/proprioceptive processing and serves as a signal to the motor system to correct errors early in production or even before they are produced. Deficits in the translation of visual frames of reference into kinesthetic/proprioceptive goals may underlie the inability of apraxics to imitate viewed actions or to predict future states of their bodies (which in turn may rely upon simulation- see Krakauer & Shadmehr, 2007). Another interpretation focuses on a deficit in transforming information from extrinsic. An alternative but not mutually exclusive framework suggests that at least some apraxic deficits may result from the inability to specify an abstract kinematic trajectory that can be used to inform motor planning. Critically, the same hand laterality task is uniquely associated with the imitation of meaningless movements in apraxia. Mechanical knowledge abilities are typically evaluated in situations involving the use of novel tools to achieve a certain action goal or the use of familiar tools in an unconventional way. One possibility is that "mechanical knowledge" rests upon the ability to predict how a tool and/or the body should be moved to achieve a target visual or kinesthetic/proprioceptive state (see Buxbaum, 2017). More recently, Cisek (2007) posited that visual information is transformed via the parietal lobe into parallel representations of potential actions, which compete with one another in the frontoparietal cortex. This competition is biased on the basis of task demands and goals by prefrontal regions, the basal ganglia, and the ventral stream, and a single response is selected when neural activity reaches a threshold emerging from the dynamics between competing neuronal populations (Cisek & Kalaska, 2010). While there is evidence that multiple motor plans are not concurrently activated. Studies with healthy participants suggest that action plans for functional tool use may interfere with plans appropriate to picking up tools to move them. Consider, for example, a television remote control, which is picked up and held with a "clench" but used with a "poke" (we term such tools conflict objects). Long-lasting interference is observed when a task requiring the demonstration of how to use a conflict object precedes a task requiring the demonstration of how to move it. The long-lasting interference indicates that use representations may exert competition even when task-irrelevant. Additional data from eye-tracking studies indicate that when instructed to click on a picture of a target tool. These and other data suggest that the implicit activation of (and competition between) tool-related actions is abnormal in parietal apraxia and that these implicit abnormalities inform overt action errors. Prefrontal/premotor node: context-, goal-, and task-related biasing and action selection the classical apraxia literature has long noted a subtype of apraxia characterized by difficulty selecting and sequencing actions, particularly as task complexity increases. In general, the frontal node of the praxis network appears critical for representing high-level action goals. The frontal node plays a critical role in action selection during action observation as well. A recent neuroimaging study revealed increased activity in the bilateral ventrolateral premotor cortex during the observation of actions involving objects with numerous action representations compared to objects with few action representations (Schubotz et al. Additional behavioral data indicate that manipulable objects flexibly evoke different action representations. Contextual factors such as the visual scene surrounding the object, object position in space, and task constraints largely determine whether grasp-to-use or grasp-to-move gestures may be more rapidly accessed. Consistent with a much broader literature on cognitive control, the frontal node of the praxis network appears critical for monitoring action per for mance and detecting mismatches between highlevel goals and produced outcomes. Learning and Relearning of Actions in Apraxia Despite the fact that apraxia has substantial consequences for caregiver burden and quality of life, there are surprisingly few studies of learning or relearning in patients with the disorder. One experimental study is relevant to the claim that apraxia is a disorder of the ventrodorsal stream. Left-hemisphere stroke patients (some with apraxia) were trained to learn novel gestures for using novel tools. Apraxics performed more poorly overall than nonapraxics, but apraxics whose lesions were more ventral, sparing the dorsodorsal system, performed better on both the production and matching tasks with tools for which the gesture was highly consistent with object structure (afforded). Patients whose lesions impinged on the dorsodorsal stream did not benefit from affordances. This supports our earlier suggestion that the integrity of representations of tool use gestures can be distinguished from the use of affordances derived from object structure (Barde, Buxbaum, & Moll, 2007). In a more clinical vein, evidence-based rehabilitation approaches for patients with limb apraxia are rare (see Buxbaum et al. Gesture training and guided training of the activities of daily living are among the few most promising approaches. For example, apraxics can be trained to produce object-related gestures to tools, videos, and pictures over many repeated trials, and there is some evidence of transfer effects to untrained activities of daily living. Neurostimulation may be another promising approach to the rehabilitation of apraxia. Additional demonstrations of the effectiveness of neurostimulation in apraxia rehabilitation will be of considerable interest. Future Directions and Conclusions Traditional accounts of apraxia focused largely on the characterization of subtypes based on error types and patterns of per for mance with imitation, pantomime, single tools, and multiple objects. Increasing statistical sophistication in lesion analysis approaches as well as evidence from functional neuroimaging and neurostimulation studies has enabled a more nuanced appreciation of the distributed left-hemisphere network critical for skilled actions. Among the insights from this more recent work is a greater understanding of the dorsal-ventral and posterior-anterior functional gradients in the praxis system, the relationship of the left ventrodorsal stream to the classically defined (dorso-) dorsal and ventral visual pathways, and the integrated roles of the three major nodes of the praxis network. With these points in mind, we will close with three considerations for future research. The first is that, rather than attempting to label subtypes of apraxia based purely on error types, the study of apraxic symptoms should be more closely aligned with increasingly detailed neurocognitive models of the roles of the temporal, parietal, and frontal regions in action representation and selection. The classical disorder associated with the loss of a movement "idea" is ideational apraxia. Confusingly, however, the diagnosis of ideational apraxia is often based on sequencing, omissions, and substitution errors on multistep, multiple objects tasks. Such errors are also very commonly observed in righthemisphere stroke and bilateral pathologies and may result from executive deficits. We suggest that a combination of action recognition impairments, along with specific hand posture deficits. Classical ideomotor apraxia is associated with a failure of intact "ideas" to inform motor planning. Some of these functions, too, are associated with specific aspects of a distributed "idea" of movement. Although broadly consistent with descriptions of impaired multistep action per for mance in classical frontal apraxia/action disorganization syndrome, recent evidence suggests that such failures occur even on tool pantomime tasks. Critically, the regions of the praxis network described here are densely interconnected, such that even relatively discrete lesions to one region may be expected to have upstream and downstream consequences. Moreover, larger middle cerebral artery strokes rarely respect discrete architectonic boundaries. Additionally, functional connectivity analyses are beginning to reveal details of the white matter pathways connecting frontal, temporal, and parietal regions, along with a greater role for the right hemisphere than is usually assumed. Future studies of the neuroanatomic substrates of various components of the apraxia syndrome and changes in these substrates with learning and experience will increasingly benefit from network connectivity approaches. The second consideration for future research is that characterizations of the computations of the ventrodorsal stream such as those outlined here parallel recent computational and theoretical models of the dorsal language pathway in the left hemisphere. Approximately 70% of aphasics are also apraxic while approximately 90% of apraxics exhibit aphasia (Weiss et al. And like the ventrodorsal action stream, the dorsal language stream is specialized for the prediction and selection of the spatiotemporal aspects of action (in this case, speech-related actions), perhaps in a relatively abstract format. Moreover, lesions to this stream may give rise to conduction aphasia, characterized in part by deficits in phonological selection and phonological short-term memory, paralleling the selection and memory-buffering functions observed in the limb action domain. Consideration of the mechanisms that are shared and distinct to each syndrome continues to be an important direction for future research. Finally, a third consideration for future research is that studies of the rehabilitation of apraxia lag far 566 Intention, Action, Control behind in number, sophistication, and rigor as compared to rehabilitation research in other domains.

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Selective recall of positive and negative feedback blood pressure and stress buy discount coumadin on-line, self- control behav iors, and depression. Neural prediction errors reveal a risk- sensitive reinforcementlearning process in the human brain. Toward an objective characterization of an anhedonic phenotype: A signal- detection approach. Ventral striatum response during reward and punishment reversal learning in unmedicated major depressive disorder. Association of neural and emotional impacts of reward prediction errors with major depression. Distinguishing defensive pessimism from depression: Negative expectations and positive coping mechanisms. Neural networks and psychopathology: Connectionist models in practice and research. Lateral inhibition and cognitive masking: A neuropsychological theory of attention. Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. In this article we describe how prefrontal function may have evolved from rodents to monkeys and humans by progressively implementing increasingly sophisticated inferential, selective, and creative processes that gradually optimize adaptive behav ior in uncertain, changing, and open- ended environments. We outline how this evolution may have contributed to endowing humans with unique, high-level cognitive faculties like language and reasoning. For instance, consider two actions A and B, which in a given situation lead to water and food, respectively. However, this behav ior is certainly maladaptive when the animal becomes hungry rather than thirsty. These associations form an internal model, referred to as a selective model, that guides behav ior without learning and using action- outcome associations per se. Overcoming this limitation thus requires learning an internal model, referred to as a predictive model, that encodes action- outcome associations in response to stimuli. This model simply learns the statistical occurrences of actual outcomes given actions and current states. Learning selective and predictive models in parallel allows for selecting actions based on stimuli and action outcomes, respectively. The limitation becomes problematic when, in addition to presenting recurrent situations, the environment is open- ended by constantly featuring new situations that were never experienced in the past and may even become recurrent in the future. With no additional mechanisms identifying recurrent and new situations, learning new contingencies erases what was previously learned and consequently prevents the exploitation of the partially recurrent nature of the environment. The animal will then gradually build an extended repertoire of discrete dimensions, or mental sets, that will ideally correspond to the various situations the animal has encountered. However, this mathematical solution is computationally intractable (Collins & Koechlin, 2012) because (1) probabilistic inferences bear upon a number of mental sets that indef initely grow with time; (2) as creating mental sets is a nonparametric, discrete (all- or-none) event, optimality requires the flexibility to constantly revise, in a backward fashion, the history of set creation whenever new observations are made (in other words, reparameterize a nonparametric event). We therefore consider a mental set to primarily encompass the selective and predictive model that has learned the contingencies of the situation associated with the creation of this mental set. Such mental sets are thus fully equipped to drive adaptive behav ior in a given situation and correspond to the psychological notion of task sets (Rogers & Monsell, 1995). This inferential buffer corresponds to the psychological notion of capacity-limited working memory (Cowan, 2005; Risse & Oberauer, 2010). The Rodent Prefrontal Cortex: Executive Control as Factual Reactive Inference the minimal inferential capability corresponds to an inferential buffer monitoring only one task set-that is, the one guiding ongoing behav ior and learning current behavioral contingencies and referred to as the actor. This inference relies on evaluating the actor ability to predict actual action outcomes. Adaptive behav ior thus derives from either adjusting selective and predictive models while perseverating with the same actor or switching to a new actor for guiding subsequent behav ior. Arbitrating between these two alternatives is based on inferring actor reliability-that is, the posterior probability that the current situation remains the same or, equivalently, that the current external contingencies match those the actor has learned (Koechlin, 2014). Updating online actor reliability according to actual action outcomes involves forward Bayesian inferences comparing the likelihood of actual action outcomes according to the actor predictive model to their likelihood according to any potential predictive models (Koechlin, 2014). The latter cannot be exactly computed, but following the maximal entropy principle (Jaynes, 1957), this likelihood is estimated as the equiprobability of action outcomes produced by the actor (Koechlin, 2014). Actor reliability t in every trial t serves to arbitrate between staying versus switching away from the current actor. While the actor remains more likely reliable than unreliable (t > 1 - t), the current situation is likely to remain unchanged. When, conversely, the actor becomes unreliable (t < 1 - t) following the last action outcome, the situation has likely changed. A new actor is then optimally built from the whole repertoire of previously learned task sets stored in long-term memory (corresponding to all previously formed actors). This reliability corresponds to the likelihood of the current action outcome in all previously encountered situations (deriving from the new actor predictive model mixing all previously learned predictive models) compared to that in a presumably new situation. The latter is actually equal to 1 because in this situation only one outcome has been observed so far. As a result, the new actor guiding behav ior is initially inferred as being unreliable (0 < 1 - 0). This promotes actor learning by preventing a switch away from the new actor while it remains unreliable. When the new actor becomes reliable, thanks to learning, the system then returns to the exploitation mode described above. Switching away from the actor and creating a new one may occur again when this actor becomes unreliable. Consequently, the lower the initial actor reliability, the more likely the situation was never encountered and the longer the exploration will last: initially, the new actor less likely matches the new situation and, consistently, more trials are required for the new actor to learn the new external contingencies. Given its intrinsic computational constraints (forward, factual, and reactive inferences only), this executive system implements an optimal adaptive process in environments featuring both new and recurrent situations. The system especially exhibits two key functional properties consistent with empirical data. When following variations in external contingencies, the actor becomes unreliable, and a new one is created. B, Diagram showing inferential and inhibition processes composing the rodent prefrontal function (square: task sets stored in long-term memory). C, Diagram showing the transitions between exploitation and exploration periods corresponding to creating a new actor task set p. When it subsequently becomes reliable, it is consolidated in long-term memory as an additional task set. A new exploitation period starts with again the possibility to subsequently create new actors. Second, the long-term repertoire of task sets expands whenever new actors are created so that the reoccurrence frequency of external situations has a major influence on shaping new actors. Whenever external situations reoccur, indeed, new actors are created with selective and predictive models again learning the associated external contingencies, thereby replicating in the repertoire the selective and predictive models previously learned from previous occurrences: the more external situations reoccur, the more these models are then replicated in long-term memory and, consequently, the more they contribute to the formation of new actors. This executive system thus exhibits a basic feature of Dirichlet processes (Teh et al. Collins and Koechlin (2012) showed that this executive system accounts for increased human per for mance in recurrent situations. Actor reliability thus derives from the likelihood of actual action outcomes projected onto these ordinal dimensions (see above). Additionally, actor reliability reflects the confidence in perseverating with the same task set-that is, repeating the same response to the same situation/stimuli. First, new actor task sets are created only in reaction to experiencing action outcomes, which may be detrimental with adverse outcomes. Second, and perhaps more problematically, actor creation ignores the context in which stored task sets were learned and mainly relies on the reoccurrence frequency of encountered situations. The resulting executive system thus exhibits proactive inferences in arbitrating between adjusting versus creating actors. For making such proactive inferences, the actor comprises and learns an additional internal model, which we refer to as the contextual model.

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Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task blood pressure chart young adults discount coumadin 5 mg online. Responses of neurons in inferior temporal cortex during memory- guided visual search. Rapid sequences of population activity patterns dynamically encode task- critical spatial information in parietal cortex. Alpha-band oscillations enable spatially and temporally resolved tracking of covert spatial attention. Inferotemporal neurons distinguish and retain behaviorally relevant features of visual stimuli. Circuitry of primate prefrontal cortex and regulation of behav ior by representational memory. In Handbook of Physiology, the Ner vous System, Higher Functions of the Brain (pp. The reliability of retro- cues determines the fate of noncued visual working memory representations. Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. A brief thought can modulate activity in extrastriate visual areas: Top- down effects of refreshing just- seen visual stimuli. Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Characterizing the dynamics of mental representations: the temporal generalization method. Prefrontal cortical unit activity and delayed alternation per for mance in monkeys. Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. Flexible control of mutual inhibition: A neural model of twointerval discrimination. Orienting attention in visual working memory reduces interference from memory probes. Attention effects during visual short-term memory maintenance: Protection or prioritization Dynamic population coding of category information in inferior temporal and prefrontal cortex. Activity of neurons in anterior inferior temporal cortex during a short-term memory task. Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection. Dif ferent states in visual working memory: When it guides attention and when it does not. A dual mechanism underlying alpha lateralization in attentional orienting to mental representation. Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions. In search of the focus of attention in working memory: 13 years of the retro- cue effect. Shape- specific preparatory activity mediates attention to Nobre and Stokes: Memory and Attention: the Back and Forth 299 targets in human visual cortex. Short- term memory trace in rapidly adapting synapses of inferior temporal cortex. Temporal expectations guide dynamic prioritization in visual working memory through attenuated oscillations. Forgotten but not gone: Retro- cue costs and benefits in a double- cueing paradigm suggest multiple states in visual short-term memory. Frontoparietal and cingulo- opercular networks play dissociable roles in control of working memory. Neural mechanisms of dual-task interference and cognitive capacity limitation in the prefrontal cortex. Anticipatory biasing of visuospatial attention indexed by retinotopically specific-band electroencephalography increases over occipital cortex. Mechanisms of persistent activity in cortical circuits: Possible neural substrates for working memory. A cognitive neuroscience approach illustrates how attentional processes are best understood not simply as a control homunculus; rather, they are bidirectionally influencing and influenced by prior experience. It therefore becomes very useful to place attention and memory dynamics into a developmental context. From very early in infancy, we are equipped with exquisite attentional skills whose improvement is coupled with the increased effectiveness of control networks. Later in childhood, both behavioral and neural indices suggest similarities and differences in how children and young adults deploy attentional control to optimize maintenance in short-term memory. Influences of attention on encoding into memory are also apparent through the effects that highly salient social, attentional biases have on learning and later recall from longer-term memory. At the same time, attentional effects on memory are not unidirectional: previously learned information and resistance to distraction during learning guide later attentional deployment, both in adulthood and in childhood. In conclusion, assessing attentional development and its dynamics points to the bidirectional influences between attention and memory. Placing Interactions between Attention and Memory into a Developmental Time Frame Multiple attentional control mechanisms influence processing by the adult attentive brain, within the remit of perception and short-term memory all the way to encoding into and recall from long-term memory. Starting from influences on perception, classic neurocognitive models of adult attention detail the mechanisms by which top- down biases from ongoing task goals play a key role in resolving the competition arising in complex visual input (Desimone & Duncan, 1995; Kastner & Ungerleider, 2000). Other classic neurocognitive models also emphasize both interactions and distinctions between goal- driven and input- driven influences on attentional selection in the adult brain (Corbetta & Shulman, 2002), as well as how overlapping but separable attention mechanisms govern behav ior in space through spatial orienting, in time through alerting processes and over goals through executive attention (Petersen & Posner, 2012; Posner & Petersen, 1990). Despite differences in the level at which each of these proposals operate and their many exciting new mechanistic foci (Buschman & Kastner, 2015; Halassa & Kastner, 2017), core to these neurocognitive models is the concept of attention as a set of biases resolving competition in a complex visual environment and therefore constraining further processing into memory. Increasingly, views of how the adult attentive brain operates have been modified to incorporate influences on attention by the contents of working goals or long-term memories (Chun, Golomb, & Turk-Browne, 2011; Gazzaley & Nobre, 2012). It is, in particular, the interface between attention and these internally held representations that will be the focus of the current chapter. In the first section, I detail the role of attention in shaping short- and long-term memory from infancy into childhood, with a focus on both changing and stable mechanisms, whereas the second section highlights growing evidence of how the contents of short-term and longer-term representations influence attention deployment across development. Attentional Influences on Short-Term and Long-Term Memory over Development Before delving into attentional influences on memory, it is worth describing, briefly, the amazing changes that characterize attention mechanisms from infancy into adulthood. From the first months of life, changes in attention are indexed by the way in which infants increasingly control their eye movements. While referring the interested reader to fuller reviews on the neural basis of attention development in infancy. Second, even though attention orienting can dissociate from eye movements (covert attention), even in adults there is a 301 high degree of overlap in neural correlates supporting overt and covert orienting. However, and finally, it is very difficult to study covert attention in infants, as this normally requires observers to follow explicit instructions. Indeed, many aspects of oculomotor control show dramatic improvements between birth and 4 months (Johnson, 1994). The engagement and efficiency of these circuits improves staggeringly and steadily from infancy into adulthood. For example, the ability to inhibit overt orienting toward salient peripheral stimuli emerges from 3 or 4 months of age (Johnson, 1995), but it continues to develop over early childhood and well into adulthood, as indexed by the increasing accuracy in producing antisaccades (Luna, Velanova, & Geier, 2008). Alongside the control of overt eye movements, infants between 4 and 6 months of age become increasingly able to orient covert attention to stimuli in the environment, as indexed by the benefits that peripheral visual cues accrue to their orienting (Hood, 1993; Johnson, Posner, & Rothbart, 1994). In neural terms these gradual changes in the control of the overt and covert orienting of attention have long been accounted for by increasing frontoparietal control on subcortical mechanisms. Early electrophysiological evidence pertaining to eye movements indicated that the infant brain before 1 year of age deploys frontoparietal mechanisms when preparing eye movements. Developments in methods such as near infrared spectroscopy have more recently also pinpointed a role for classic control nodes in frontal and parietal cortex from early during the first year of life, when young infants direct attention to higher-level representations that might guide their actions (Werchan, Collins, Frank, & Amso, 2016).

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Second blood pressure medication harmful discount coumadin 1mg with amex, the injury causing the amputation/stroke will also profoundly impair the motor abilities of the individual, promoting the learning of adaptive strategies and therefore changing input/output synchronization patterns. We therefore suggest that map changes should neither be attributed to categorical changes in cortical reorganization nor given causal behavioral relevance. Acknowledgments We thank Lisa Quarrell for artwork and Victoria Root and Andrew Pruszynski for helpful comments. Behavioral and neurophysiological effects of delayed training following a small ischemic infarct in primary motor cortex of squirrel monkeys. Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation. The representation of the tail in the motor cortex of primates, with special reference to spider monkeys. Tactile perception in blind braille readers: A psychophysical study of acuity and hyperacuity using gratings and dot patterns. Intracortical connectivity of archtectonic fields in the somatic sensory, motor and parietal cortex of monkeys. Large- scale reorganization of the somatosensory cortex following spinal cord injuries is due to brainstem plasticity. Reorganization of the primary motor cortex of adult macaque monkeys after sensory loss resulting from partial spinal cord injuries. Contribution of the monkey corticomotoneuronal system to the control of force in precision grip. Reassessing cortical reorganization in the primary sensorimotor cortex following arm amputation. Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult squirrel monkeys. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Cortical map plasticity improves learning but is not necessary for improved per for mance. Remodelling of hand representation in adult cortex determined by timing of tactile stimulation. Sensorimotor finger- specific information in the cortex of the congenitally blind. They integrate multimodal information from the entire neocortex, thalamus, limbic regions, and dopaminergic midbrain nuclei. The basal ganglia output nuclei can powerfully regulate behav ior either by modulating neuronal activity in frontal cortical regions indirectly through a thalamocortical pathway or directly by projections to midbrain/brain stem premotor regions. Dysfunction of the basal ganglia, occurring in diseases in humans or induced experimentally in animal models, leads to profound behavioral impairments, the most consistent being a reduction in speed and extent of movement. However, the specific computational aspect(s) of the basal ganglia that relate to the control of behav ior remains the subject of considerable debate. Here, informed by features of the evolutionary conserved anatomy of the basal ganglia and the analysis of the behavioral deficits in human disease, we propose that a key computational function of the basal ganglia is to control low-level parameters of movements. As a result, the basal ganglia provide a critical circuit in translating both implicit and explicit aspects of voluntary decisions into action. We also review a series of neurophysiological studies across multiple vertebrate species that begin to elucidate the circuit-level implementation mechanisms of basal ganglia function. Functional Anatomy of Basal Ganglia the basal ganglia are a collection of interconnected subcortical nuclei that are found essentially unchanged in their pattern of connectivity, identified cell types, and neurochemical markers in all vertebrate species, ranging from the lamprey to the primate (Grillner, Robertson, & Stephenson-Jones, 2013). This conservation across species covers approximately 500 million years of vertebrate evolution. Our goal in this section is to provide a brief overview of the conserved functional architecture of the basal ganglia. The basal ganglia transform descending inputs from the telencephalon and then project them via a reentrant pathway through the thalamus back to the cortex and through convergent, feedforward pathways to subcortical premotor structures, such as the reticular areas of the midbrain, the deep layers of the superior colliculus, and the mesencephalic locomotor region (figure 44. There is a dramatic reduction in cell number as one progresses through the internal circuitry of the basal ganglia (figure 44. In mammals, afferent inputs to the basal ganglia arise from excitatory projection neurons of the neocortex, thalamus, amygdala, and hippocampus. Three primary classes of excitatory projection neurons in cortex provide input to basal ganglia: superficial intratelencephalic neurons of layer 2/3, deep intratelencephalic neurons of layer 5a, and collaterals from corticofugal projection neurons of layer 5b (Dudman & Gerfen, 2015). In mammals, the caudate and putamen (collectively referred to as the striatum in rodents due to its perforation by massive tracts of the internal capsule) receives all three types of cortical input. Deeper structures in the basal ganglia, such as the subthalamic nucleus and substantia nigra, receive corticofugal (layer 5b) input via collaterals but do not receive direct input from either intratelencephalic population. Compared to the subthalamic nucleus and substantia nigra, the striatum contains the greatest number of neurons by a few orders of magnitude (Hardman et al. The organization of the major pathways of the dorsal basal ganglia is indicated by arrows for predominantly excitatory projections, lines for inhibitory projections, and an open circle for dopaminergic, modulatory projections. Detailed reviews of internal circuitry and topographical organization can be found in Dudman and Gerfen (2015). The striatum is composed of approximately 95% inhibitory projection neurons that fall into two distinct cell types distinguished by their neurochemical markers, dopamine receptor expression, neuropeptide expression, and efferent projections. In the dorsal striatum, these cell types and the functional pathways they constitute are generally referred to as the direct and indirect projection pathways to primary basal ganglia output nuclei (figure 44. However, the advent of methods that combine cell type identification with the recording of activity in behaving rodents (Cui et al. The apparent contradiction between these results might result from the inherent limitations of perturbation and correlative experiments. Second, the algorithmic meaning of patterns of activity that correlate with certain aspects of behav ior can remain obscure, even when complemented with perturbation experiments (Krakauer, Ghazanfar, GomezMarin, MacIver, & Poeppel, 2017). Insight into the 528 Intention, Action, Control function of the striatal circuits (and, more generally, of the basal ganglia) may be gained through a critical examination and comparison of the behavioral alterations associated with various forms of its dysfunction, in daily life activities or experimental setups. Individuals with bilateral lesions of the striatopallidal complex are characterized by an extreme lack of spontaneous mental activity (psychic akinesia) that can be reversed upon external stimulation (Laplane & Dubois, 2001). In addition, when tested with an incentivized handgrip squeezing task, these individuals display a complete incapacity to scale the vigor of their squeeze with the magnitude of a monetary reward (figure 44. Importantly, their capacity to squeeze with increasing strength was identical to control patients in an instructed version of the task. These findings, along with the fact that patients do not display tremor or rigidity, suggest that the basal ganglia are not required for the execution of movements per se but for their invigoration. Thus, dopamine likely provides the motivational signal that is translated into invigorated output from the basal ganglia. This discovery, largely overlooked at the time (Lees, Selikhova, Andrade, & Duyckaerts, 2008), was later explained by the fact that the aforementioned motor deficits are caused by dopamine depletion in the striatum following the progressive degeneration of dopaminergic nigrostriatal neurons (Hornykiewicz, 2006). If this was the case, further perturbation or lesioning of the basal ganglia should worsen movement deficits. This supports the view that damaged basal ganglia do not lead to a fundamental deficit in movement control but a modulatory deficit due to blunted motivation or perhaps an implicit overestimation of the energetical cost of moving (Baraduc, Thobois, Gan, Broussolle, & Desmurget, 2013) Reduced Vigor Is Also Present in Experimentally Induced Disorders of the Basal Ganglia the idea that an important function of the dopaminergic projection to the striatum is to scale movement extent and speed according to motivational factors is supported by pharmacological manipulations of the dopaminergic system in the ventral striatum that alter the amount of work animals are willing to exert to obtain rewards (Salamone, Correa, Farrar, & Mingote, 2007). Recordings of spiking activity in the dorsal striatum of control mice showed that such activity in a majority of neurons was modulated at an early movement phase and correlated with movement speed. Such modulations, compatible with a representation of movement vigor, were markedly altered in MitoPark mice. Importantly, both the behavioral and neuronal abnormalities were reversed by dopamine replacement therapy. A set of studies investigating reaching movements in nonhuman primates showed that inactivation/lesioning of the globus pallidus consistently slowed down movements and induced hypometria but preserved reaction time and movement accuracy (Desmurget & Turner, 2008; Horak & Anderson, 1984), consistent with a selective contribution of the basal ganglia to movement vigor.

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Second hypertension jnc 7 order genuine coumadin on line, neurons projecting to frontal areas could influence decision-related activities in prefrontal cortex. A third, tantalizing possibility is the existence of a local decision mechanism within amygdala circuits (Grabenhorst, Hernadi, & Schultz, 2012). This suggestion is consistent with the observed explicit value-tochoice conversions in individual amygdala neurons (figure 53. Although amygdala inactivation does not necessarily cause choice deficits when reward values are stable (Wellman, Gale, & Malkova, 2005), the amygdala does make essential contributions in situations requiring dynamic valuations (Murray & Rudebeck, 2013). Consistently, amygdala lesions impair prefrontal value coding and behavioral choices during reinforcement learning (Rudebeck et al. The decision signals and planning activities in amygdala neurons may also inform our understanding of amygdala dysfunction in human psychiatric conditions, including mood disorders. These conditions have an impact on the motivation to plan for and pursue distant rewards. To better understand amygdala functions in health and disease, it will be important to establish whether amygdala neuronal circuits directly implement a local decisionmaking mechanism. The representation of hierarchical rank was observed in the same neuronal ensembles that encoded the rewards associated with nonsocial stimuli (figure 53. A representation of reward value was not sufficient for representing hierarchical rank, as the orbitofrontal and anterior cingulate cortices lacked representations of hierarchical rank despite representing reward values. Information about hierarchical rank, which is intimately related to an assessment of the social value of individuals, is therefore linked in the amygdala to representations of rewards associated with nonsocial stimuli. These neuronal response properties and amygdala responses that jointly signal our own reward and a conspecific reward (Chang et al. Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Responses of amygdala neurons to positive reward predicting stimuli depend on background reward (contingency) rather than stimulusreward pairing (contiguity). Neural mechanisms of social Reward and Social Information Investigation of the role of the amygdala in decisionmaking is in its infancy, as is the study of how the amygdala participates in interactions between the emotional and cognitive variables that drive many forms of behavior. However, the amygdala does not only contribute to decision-making by virtue of its role in cognitive and emotional processing. Increasingly, the amygdala is recognized as playing a role in processing social stimuli, especially those of faces (Gothard et al. The amygdala receives prominent input from the parts of the inferotemporal cortex that represent faces, and a long history of work has shown that amygdala neurons respond to images of faces (Gothard et al. A key question is whether the amygdala processes social information in distinct neural circuits as compared to nonsocial stimuli that are related to rewards. Strikingly, when the responses to faces 638 Reward and Decision-Making decision-making in the primate amygdala. Outcome selective effects of intertrial reinforcement in a Pavlovian appetitive conditioning paradigm with rats. Neural representations of unconditioned stimuli in basolateral amygdala mediate innate and learned responses. Primate amygdala neurons evaluate the progress of self- defined economic choice sequences. Planning activity for internally generated reward goals in monkey amygdala neurons. Differential involvement of the basolateral amygdala and mediodorsal thalamus in instrumental action selection. The primate amygdala represents the positive and negative value of visual stimuli during learning. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Neurophysiology and functions of the primate amygdala, and the neural basis of emotion. Amygdala contributions to stimulusreward encoding in the macaque medial and orbital frontal cortex during learning. The primate amygdala in social perception-insights from electrophysiological recordings and stimulation. Distinct roles for the amygdala and orbitofrontal cortex in representing the relative amount of expected reward. Emotion, cognition, and mental state representation in amygdala and prefrontal cortex. Neural basis for economic saving strategies in human amygdalaprefrontal reward circuits. This topic is of interest to scientists and stakeholders keen on identifying the mechanisms that underlie adolescent behav ior in order to best support young people as they transition from childhood into adulthood. The study of cognitive development during this dynamic period also provides a unique opportunity to elucidate the relationship between neurobiology and behav ior using a comparative approach. To these ends, our chapter reviews existing literature on the adolescent development of inputs to the dorsal and ventral striatum and develops three central ideas. First, the striatum is a structure that plays a central role in generating behav ior by integrating glutamatergic and neuromodulatory inputs from other structures in a manner similar to a ballot box. Second, because cortical and limbic structures send convergent and coactive inputs to the striatum, the developmental changes in the relative ratio of these inputs will influence the neural computations that arise from the striatum. Third, the distinct developmental trajectories of inputs to the dorsal and ventral striatum likely contribute to a complex series of changes in behavioral patterns in learning and decision-making that are unique to adolescents. Second, inputs to the striatum undergo changes during adolescence that influence the neural computations that arise from this structure. Third, changes in striatal computations may orchestrate developmental changes in reward processing, feedback-based learning, and decision-making, forming part of a normative developmental process. To date, much has been written about the potentially detrimental aspects of reward-based decision-making in adolescents. Here, we take the opportunity to examine positive aspects of this function at this time and introduce a framework by which interventions may be informed by developmental science. Development of Inputs to the Dorsal Striatum and the Ballot Box Metaphor the striatal regions of the basal ganglia (also referred to as the caudate and the putamen in primates) are critical hubs for learning, reward processing, and decisionmaking. The medial portions of the dorsal striatum (caudate) are implicated in flexible decision-making and action selection (Smith, Surmeier, Redgrave, & Kimura, 2011; Yartsev et al. The ventral portions of the striatum are implicated in motivation, reward, and reinforcement (Averbeck & Costa, 2017; Haber, 2011; Roitman, Wheeler, & Carelli, 2005; Yin & Knowlton, 2006). Although normative and adaptive, these changes have significant implications for long-term outcomes, particularly as adolescents begin to explore their changing social and psychological landscape and make independent decisions. Over the past two decades, longitudinal imaging and computational neuroscience methods have provided increasingly sophisticated insights into the structure and function of the developing brain. Here we focus on how developmental changes in the convergence and relative influence of inputs to the basal ganglia can influence adolescent learning and decision-making. The first is that cortical- striatal and limbic- striatal circuits play a central role in reward and decision-making (Averbeck & Costa, 2017; Friedman et al. It is important to note that the striatum is more than a mere relay station for these inputs. Some have likened the striatum and its neural computations to a "ballot box" in which dif ferent inputs to the striatum have a chance to "vote" for or against an action via inputs to different cell types within the striatum (Krauzlis, Bollimunta, Arcizet, & Wang, 2014; McHaffie, Stanford, Stein, Coizet, & Redgrave, 2005; Redgrave, Prescott, & Gurney, 1999). The ballot box model highlights the importance of (1) identifying the distinct afferent sources (voters) that drive activity in the striatum and (2) understanding how global activity in the striatum. A prominent framework for understanding the maturation of cognitive control during adolescence is the dual- systems model, which suggests that the delayed maturation of prefrontal cortex relative to subcortical regions (including the striatum) produces an imbalance that promotes sensation seeking and risk-taking in adolescents (Shulman et al. While the dual- systems model separates the prefrontal cortex and subcortical striatal activity, a fundamental feature of the striatum is that it requires the convergence of many glutamatergic inputs (largely cortical) to drive activity. Therefore, rather than framing adolescent decision-making in terms of striatal "gas" versus prefrontal cortex "brakes," it may be more informative to consider "who" is driving striatal activation at distinct points in development (figure 54. Indeed, there are clear developmental shifts (with both gains and losses) in the strength of dif ferent inputs to striatal subregions: generally, more dorsal cortical regions wax in strength while more limbic and ventral connections wane and yet others exhibit U- shaped trajectories (outlined below). We can interpret these shifts in the framework of the ballot box metaphor: strengthening connections may be "gaining votes" while weakening connections are potentially "losing votes. Recent developmental human brain-imaging work is beginning to make it possible to compare the relative strength of striatal inputs to determine which may play a dominant role in the selection of behav ior at different ages. Here striatal inputs from a cortical "limbic network" associated with affective function were compared to a "frontoparietal network" associated with cognitive control (Larsen et al.

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From visual attention research blood pressure ratio order coumadin overnight, a particular role of prefrontal cortex and the frontal eye field is plausible, both anatomically and conceptually (Buschman & Kastner, 2015). The same holds for the intraparietal sulcus, but the relative hierarchy of the prefrontal and parietal cortex is a matter of ongoing debate. Closely related, it is all too unclear how far the recent progress on correlates of. For visual attention, the frontal and parietal cortices are thought to constitute maps of prioritized space: retinotopic maps in higher- order areas of parietal cortex are primarily sensitive to attentional rather than sensory input (Jerde, Merriam, Riggall, Hedges, & Curtis, 2012). Are there attentional priority maps for auditory processing analogous to the visual system If we consider frequency to be to audition what space is to vision, we would expect tonotopic organization to be retrievable from prefrontal cortex. However, the topographical organization of acoustic features appears to break down beyond auditory cortex. Instead, higherorder nonlinear transformations appear to take place in prefrontal cortex (Averbeck & Romanski, 2006; Plakke & Romanski, 2014). There is, however, preliminary evidence for attentional priority implemented in frontal cortex. What about sensory degradation, which presents listeners with a "systems-level challenge that requires the allocation of executive cognitive resources" (Peelle, 2018) The cingulo- opercular system has been consistently implicated in what has been termed listening effort (Alain, Du, Bernstein, Barten, & Banai, 2018). Most likely, it represents a domain- general system for executive function in difficult perceptual tasks. Recruitment of this cingulo- opercular network is altered in older adults: it is active already under clear listening conditions, and hearing loss leads to higher dependence on the cingulo- opercular system (Erb & Obleser, 2013; see figure 15. Together, these findings suggest a role of the cingulo- opercular system in the expression of alpha oscillations, likely serving the maintenance of auditory attention. Conclusions In this article we have discussed dif ferent neural filter mechanisms for auditory attention. We have also seen how the "central" stage of auditory attention, primary auditory cortex, cannot be viewed in isolation. B, Schematic of cortical layers in A1 and their inputs: bottomup sensory feedforward information enters at deep and middle cortical layers; top- down feedback information arrives at superficial and deep layers (see also figure 15. We here all but ignored fascinating attempts to push the boundaries of how attentional filters shape our listening down into the brain stem and the auditory periphery (Forte, Etard, & Reichenbach, 2017; Gruters et al. Acknowledgments We are indebted to numerous colleagues who, over many years and after many guest lectures on this and closely related topics, have helped shape the ideas laid out here. Special thanks are extended to Maria Chait, Alexandra Bendixen, Molly Henry, Ingrid Johnsrude, and Peter Lakatos. Listening under difficult conditions: An activation likelihood estimation meta- analysis. Probabilistic encoding of vocalizations in macaque ventral lateral prefrontal cortex. Tuning in to sound: Frequency- selective attentional filter in human primary auditory cortex. Thalamic connections of auditory cortex in marmoset monkeys: Lateral belt and parabelt regions. Frequency preference and attention effects across cortical depths in the human primary auditory cortex. Emergence of neural encoding of auditory objects while listening to competing speakers. Homology and specificity of natural sound- encoding in human and monkey auditory cortex. Auditory skills and brain morphology predict individual differences in adaptation to degraded speech. The brain dynamics of rapid perceptual adaptation to adverse listening conditions. Rhythmic sampling within and between objects despite sustained attention at a cued location. The human auditory brainstem response to running speech reveals a subcortical mechanism for selective attention. Parietooccipital approximately 10 Hz activity reflects anticipatory state of visual attention mechanisms. Adaptive changes in cortical receptive fields induced by attention to complex sounds. The eardrums move when the eyes move: A multisensory effect on the mechanics of hearing. Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex. Aging affects the balance of neural entrainment and topdown neural modulation in the listening brain. Frequency modulation entrains slow neural oscillations and optimizes human listening behav ior. Temporal expectations and neural amplitude fluctuations in auditory cortex interactively influence perception. Shaping functional architecture by oscillatory alpha activity: Gating by inhibition. Analysis of slow (theta) oscillations as a potential temporal reference frame for information coding in sensory cortices. Irregular speech rate dissociates auditory cortical entrainment, evoked responses, and frontal alpha. Selective cortical representation of attended speaker in multi-talker speech perception. Tonotopic organization, architectonic fields, and connections of auditory cortex in macaque monkeys. A precluding but not ensuring role of entrained low-frequency oscillations for auditory perception. Layer specific sharpening of frequency tuning by selective attention in primary auditory cortex. Suppressed alpha oscillations predict intelligibility of speech and its acoustic details. Listening effort: How the cognitive consequences of acoustic challenge are reflected in brain and behav ior. Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. Some evidence in support of a relationship between human auditory signaldetection per for mance and the phase of the alpha cycle. Frequency- selective attention in auditory scenes recruits frequency representations throughout human superior temporal cortex. Intrinsic connectivity networks, alpha oscillations, and tonic alertness: A simultaneous electroencephalography/functional magnetic resonance imaging study. Encoding of natural sounds at multiple spectral and temporal resolutions in the human auditory cortex. Layer- specific entrainment of gamma-band neural activity by the alpha rhythm in monkey visual cortex. The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Synchronisation signatures in the listening brain: A perspective from non-invasive neuroelectrophysiology.

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A representational hierarchy is also found in auditory cortex hypertension young age purchase coumadin 1mg with visa, suggesting interest ing similarities to the visual system, in that the robustness to variability. The dif ferent pathways of the network differentially explain neural variance in dif ferent parts of the auditory cortex, illustrating how taskoptimized neural networks can help further our understanding of large- scale functional organization in the brain. Recent work along similar lines has begun to tackle somatosensory systems (Zhuang et al. A functionally driven optimization approach has also been effective at driving progress in modeling the motor system (Lillicrap and Scott 2013; Sussillo et al. This work shows how imposing the computational goal of creating behaviorally useful motor output constrains internal neural network components to match other wise nonobvious features of neurons in motor cortex, and provides a modern computational basis for earlier work on movement efficiency (Flash and Hogan 1985). Unlike work on sensory systems, the goals in motor networks are not representational but instead focus on the generation of dynamic patterns of motor preparation and movement (Churchland et al. These results show that the goaldriven optimization idea has power across a wide range of network architectures and behavioral goal types. Relative to the optimization framework described above, the analysis of tuning curves is essentially an attempt to characterize optimal networks A* in non- optimization-based terms. When a small number of mathematically simple stimulus- domain axes can be found in which the tuning curves of A* have a mathematically simple shape, A* can largely be constructed by a simple closed-form procedure without any reference to learning through iterative optimization. This is to some extent feasible for V1 neurons and perhaps in early cortical areas in other domains, such as primary auditory cortex (Chi, Ru, and Shamma 2005). It is possible that this type of simplification is most helpful for understanding neural responses that arise largely from highly constrained stereotyped genetic developmental programs, rather than those that depend heavily on experience- driven learning (Espinosa and Stryker 2012), or where biophysical constraints- such as metabolic cost or noise reduction-might also impose "simplicity priors" on the neural architecture (Olshausen and Field 1996; Sussillo et al. In general, however, it is not guaranteed that closedform expressions describing the response properties of task- optimized models can be found. Evolution and development are under no general constraint to make their products conform to simple mathematical shapes, especially for intermediate and higher cortical areas removed from the sensory or motor periphery. However, even if such analytical simplifications do not exist, the optimization framework nonetheless, provides a method for generating meta-understanding via characterizing the constraints on the system, rather than analyzing the specific outcome network itself. By varying the architectural class, the computational goal, or the learning rule, and identifying which choices lead to networks that best match the observed neural data, it is possible to learn much about the brain system of interest even if its tuning curves are inscrutable. Understanding multiple optima What happens when multiple optimal network solutions exist For many architecture classes, there may be infinitely many qualitatively very similar networks with the same or substantially similar outputs-for example, those created by applying orthonormal rotations to linear transforms present in the network. Sometimes, however, qualitatively very distinct networks might achieve similar perfor mance levels on a task. The optimization framework does not require a unique best solution to the computational goal to make Yamins: An Optimization-Based Approach 391 useful predictions. If several subclasses of highperforming solutions to a given task are identified, this is equivalent to formulating multiple very qualitatively distinct hypotheses for the neural circuits underlying function in a given brain area. Recent work in modeling rodent whisker trigeminal cortex, in which similar task performance on whisker- driven shape recognition can be achieved by several distinct neural architecture classes, illustrates this idea (Zhuang et al. Comparison of the distinct model types to experimental results, either from detailed behavioral or neural experiments, is then likely to point toward one of these hypotheses as explaining the data better than others. Techniques similar to those used to create the models in the first place can be deployed to generate optimal stimuli for separating the predictions of the multiple models as widely as possible, which would in turn directly inform experimental design. In these cases, the optimization framework serves as an efficient generator of strong hypotheses. In contrast, if most high-performing solutions to a computational goal fall into a comparatively narrower band of variability, the set of model solutions may correspond to actual variability in the real subject population. While many high-level representational properties are shared between these networks, meaningful differences can exist (Li et al. Variation of computational goal, described as a distribution over stimuli in the data set defining the goal task. This idea captures the concept that dif ferent individuals will experience somewhat dif ferent stimulus diets during development and learning. Understanding the computational sources of intraspecific variation is itself an important modeling question for future work (Van Horn, Grafton, and Miller 2008). A contravariance principle Though it may at first seem counterintuitive, the harder the computational goal, the easier the model-to-brain matching problem is likely to be. This is because the set of architectural solutions to an easy goal is large, while the set of solutions to a challenging goal is comparatively smaller. In mathematical terms, the size of the set of optima is contravariant in the difficulty of the optimization problem. A simple thought experiment makes this clear: Imagine if, instead of trying to solve 1,000-way object classification in the real-world ImageNet data set, one simply asked a network to solve the binary discrimination between two simple geometric shapes shown on uniform gray backgrounds. The set of networks that can solve the latter task is much less narrowly constrained than that which solves the former. And given that primates actually do exhibit robust object classification, the more strongly constrained networks that pass the same hard per for mance tests are more likely to be homologous to the real primate visual system. A detailed example of how optimizing a network to achieve high per for mance on a low-variation training set can lead to poor per for mance generalization and neurally inconsistent features is illustrated in Hong et al. The contravariance principle makes a strong prescription for using the optimization framework to design effective computationally driven experiments. Unlike the typical practice in experimental neuroscience but echoing recent theoretical discussions of task dimensionality (Gao et al. In fact, this sort of highly reductive approach is likely to lead to confusing results precisely because the reduced task may admit many spurious solutions. It is more effective to impose the challenging real-world task from the beginning, both in designing training sets for optimizing the neural network models and in designing experimental stimulus sets for making model- data comparisons. Even if the absolute per formance numbers of networks on the harder computational goal are lower, the resulting networks are likely to be better models of the real neural system. In general, the optimization-based approach is likely to be most efficient when the network sizes are just large enough to solve the computational task. Thus, another way to constrain networks while still using a comparatively simple computational goal is to reduce the network size. It remains unknown whether the specific architectural principles discovered in such simplified settings will prove useful for understanding the larger networks needed for achieving more sophisticated computational goals in higher organisms. Major Future Directions the optimization framework suggests a wide variety of important future directions to be explored. Better sensory models Within the domain of the visual system, many substantial differences remain between state- of-the-art models and the real neural system. For neurons throughout the macaque ventral visual stream, the best neural network models are able to explain only approximately 65% of the reliable time- averaged neural responses to static natural stimuli. This neural result is echoed by the fact that while the models are behaviorally consistent with primate and human visual error patterns at the category or object level (Rajalingham, Schmidt, and DiCarlo 2015), they fail to entirely account for error patterns at the finest image-by-image grain (Rajalingham et al. While most modeling efforts have so far focused on the ventral visual pathway, understanding the functional demands that lead to the emergence of multiple visual pathways, or combining constraints at multiple levels. Likewise, little attention has been paid to understanding the physical layout of brain areas. While some of the most robust results in human cognitive neuroscience involve identifying the subregions of visual cortex that selectively respond to certain classes of stimuli-for example, the well-known face, body, and place areas (Downing et al. Learning Though the optimization framework has shown exciting progress at the intersection of machine learning and computational neuroscience, there is a fundamental problem confronting the approach. Typical neural network training uses heavily supervised methods involving huge numbers of high-level semantic labels-for example, category labels for thousands of examples in each of thousands of categories (Deng, Dong, et al. Viewed as technical tools for tuning algorithm parameters, such procedures can be acceptable, although they limit the purview of the method to situations with large existing labeled data sets. As real models of learning in the brain, they are highly unrealistic because, among other reasons, human infants and nonhuman primates simply do not receive millions of category labels during development. There has been a substantial amount of research on unsupervised, semisupervised, and selfsupervised visual-learning methods (Goodfellow et al. Despite these advances, the gap between supervised and unsupervised approaches still remains significant. The discovery of procedures that are computationally power ful but use substantially less labeled data is a key challenge for understanding real biological learning. Modeling integrated agents rather than isolated systems Cognition is not just about the passive parsing of sensory streams or the disembodied generation of motor commands. Humans are agents, interacting with and modifying their environment via a tight visuomotor loop.

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Although the neurobiological pathways differ between these species arrhythmia frequently asked questions discount 5 mg coumadin overnight delivery, the input-referred measure showed that the general pattern of spatially extended, circular receptive fields on the ret ina held for optic nerve fibers across these species. The receptive field is a property of the entire sequence of events from light absorption to phototransduction, through all of the circuitry in the ret ina, and up to the nerve fiber that is actually mea sured. Thus, while the mea surement may sample a single cell, the underlying computations are implemented by populations. The same logic holds for field potentials, which also pool signals over large populations of neurons. Despite the relatively coarse scale of neuronal pooling, these measures are nonetheless highly sensitive to stimulus properties such as position, contrast, and pattern. For this model the stimulus is converted to a binary contrast mask at each time point. In a typical experiment, a contrast pattern, such as a texture or checkerboard, is windowed within a slowly moving aperture, such as a bar, wedge, or ring. For each stimulus presentation, all the pixels inside the aperture are labeled 1 and all the pixels outside as 0 (figure 10. The binary contrast mask is then multiplied point-wise by a circularly symmetric twodimensional Gaussian and summed, yielding one number per time point. The parameters of the Gaussian-its center (x, y) and size - are determined by a search algorithm to minimize differences between the predicted and observed time series. These parameters are expressed in degrees of visual angle, a stimulus-referred quantity. The field of view is the portion of the visual field that elicits a response in any part of a region of interest. It is not surprising that the field of view of V1 spans the visual field, since anywhere you can see requires a V1 representation. Ventrotemporal areas tend to have a smaller field of view, with little representation of the far periphery. This is consistent with the proposal that much of the ventral visual pathway is specialized for recognition processes that depend on inputs from the central visual field (Levy, Hasson, Avidan, Hendler, & Malach, 2001). A similar central bias is observed for face-responsive regions, such as mFus (figure 10. The limited field of view corresponds to the region where face recognition is good. The field of view in face- selective regions appears to be especially small in subjects with developmental prosopagnosia, a deficit in the ability to recognize faces. Because the field of view is an aggregate measure, its properties can vary for multiple reasons. For example, the response to a large contrast pattern tends to be less than the sum of the responses to parts of the pattern presented separately (figure 10. The linear prediction is somewhat too high in V1 and increasingly inaccurate in more anterior visual field maps. This subadditivity across space has been widely observed at the level of single neurons. C, the second- order contrast model operates directly on images rather than on binary contrast masks. The model includes a number of canonical neuronal calculations (filtering, divisive normalization, summation, second- order contrast, and a compressive nonlinearity) organized into two similar stages, each with three steps: linear (L), nonlinear (N), nonlinear (N). The result of the nonlinearity is that the predicted response increases more gradually than the linear prediction. The form of the nonlinearity was shown to be compatible with divisive normalization, a calculation often used to model nonlinear responses in single neurons. More generally, the authors observed that increasingly compressive spatial summation led to an increased tolerance for changes in stimulus size and position, an Winawer and Benson: Population Receptive Field Models n) 123 important property of visual responses and object recognition. It is especially useful for higher-level cortical areas, such as word- and faceresponsive regions (Kay, Weiner, & Grill- Spector, 2015; Le et al. Even for a restricted set of images, such as static, gray- scale, band-passed images, relatively complex models are needed to make accurate predictions (figure 10. One such model consists of a two- stage cascade (Kay, Winawer, Rokem, Mezer, & Wandell, 2013), including contrast energy, divisive normalization, spatial summation, second- order contrast, and a compressive nonlinearity. Importantly, the model solutions help to clarify and quantify differences between visual areas. From Space to Time the visual system must pool signals over time as well as space. Zhou and colleagues employed a compressive model because mixture tests revealed that the cortex exhibits subadditive temporal summation similar to space. Similar to the spatial domain, the subadditive temporal model was more accurate than a linear model. First, responses to long or repeated stimuli were less than the linear prediction from brief stimuli (adaptation). Second, for longer interstimulus intervals, the total response to the two stimuli increased (recovery from adaptation) (figure 10. The patterns of temporal summation in the visual hierarchy parallel those in space: temporal windows become longer in more anterior areas, and the summation becomes increasingly compressive. As a result, responses in later visual areas are less sensitive to the precise duration of a viewed stimulus. This might be thought of as tolerance for timing or duration, analogous to the position and size tolerance in visual responses. The similarity of the findings between spatial and temporal summation suggest that the visual cortex may use a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world. The patterns were viewed either as one pulse (0 to 533 ms) or two pulses of 134 ms each, with a variable interstimulus interval. The R 2 values are the 50% confidence intervals from bootstrapped, cross- validated fits. A, the model takes the stimulus time course as input, which is convolved with an impulse response function. The impulse response function is pa rameterized by a time constant, on the order of approximately 100 ms. These models have been especially useful at capturing differences between foveal visual representations, which are most sensitive to sustained inputs, and peripheral representations, which are most sensitive to visual transients. It is likely that a more complete model of temporal processing will involve elements of both models: a compressive summation over extended temporal windows and two or more channels to capture differential sensitivity to transient and sustained inputs. Many of these results were reviewed recently (Wandell & Winawer and Benson: Population Receptive Field Models 125 Winawer, 2015). These results quantify the manner in which many ventral visual areas involved in recognition have a more centrally limited field of view. The results also show that the field of view of some visual areas depends on stimulus pattern (Le et al. The method is sufficiently reliable that it should prove possible to test hypotheses linking field- of-view measures to visual per for mance in individual subjects. The authors linked these findings to some of the visual deficits reported with schizophrenia and suggested they arise from imbalances of excitation and inhibition in the visual cortex. These findings highlight the fact that receptive field properties depend on the function of the entire visual pathway, from input (optics) to intracortical circuitry. Together, the developments reviewed here demonstrate the power of the computational modeling approach. The discharge of impulses in the optic nerve and its relation to the electric changes in the ret ina.