Responses of pulvinar neurons reflect a subject's confidence in visual categorization

Yutaka Komura, Akihiko Nikkuni, Noriko Hirashima, Teppei Uetake & Aki Miyamoto
Nature Neuroscience (2013) doi:10.1038/nn.3393
Received 26 December 2012 Accepted 05 April 2013 Published online 12 May 2013


When we recognize a sensory event, we experience a confident feeling that we certainly know the perceived world 'here and now'. However, it is unknown how and where the brain generates such 'perceptual confidence'. Here we found neural correlates of confidence in the primate pulvinar, a visual thalamic nucleus that has been expanding markedly through evolution. During a categorization task, the majority of pulvinar responses did not correlate with any 'perceptual content'. During an opt-out task, pulvinar responses decreased when monkeys chose 'escape' options, suggesting less confidence in their perceptual categorization. Functional silencing of the pulvinar increased monkeys' escape choices in the opt-out task without affecting categorization performance; this effect was specific to the contralateral visual target. These data were supported by a theoretical model of confidence, indicating that pulvinar activities encode a subject's certainty of visual categorization and contribute to perceptual confidence.


Dopaminergic Control of Motivation and Reinforcement Learning: A Closed-Circuit Account for Reward-Oriented Behavior

Kenji Morita, Mieko Morishima, Katsuyuki Sakai, and Yasuo Kawaguchi
J. Neurosci. 2013;33 8866-8890

Humans and animals take actions quickly when they expect that the actions lead to reward, reflecting their motivation. Injection of dopamine receptor antagonists into the striatum has been shown to slow such reward-seeking behavior, suggesting that dopamine is involved in the control of motivational processes. Meanwhile, neurophysiological studies have revealed that phasic response of dopamine neurons appears to represent reward prediction error, indicating that dopamine plays central roles in reinforcement learning. However, previous attempts to elucidate the mechanisms of these dopaminergic controls have not fully explained how the motivational and learning aspects are related and whether they can be understood by the way the activity of dopamine neurons itself is controlled by their upstream circuitries. To address this issue, we constructed a closed-circuit model of the corticobasal ganglia system based on recent findings regarding intracortical and corticostriatal circuit architectures. Simulations show that the model could reproduce the observed distinct motivational effects of D1- and D2-type dopamine receptor antagonists. Simultaneously, our model successfully explains the dopaminergic representation of reward prediction error as observed in behaving animals during learning tasks and could also explain distinct choice biases induced by optogenetic stimulation of the D1 and D2 receptor-expressing striatal neurons. These results indicate that the suggested roles of dopamine in motivational control and reinforcement learning can be understood in a unified manner through a notion that the indirect pathway of the basal ganglia represents the value of states/actions at a previous time point, an empirically driven key assumption of our model.


Stimulus Value Signals in Ventromedial PFC Reflect the Integration of Attribute Value Signals Computed in Fusiform Gyrus and Posterior Superior Temporal Gyrus

Seung-Lark Lim, John P. O'Doherty, and Antonio Rangel
J. Neurosci. 2013;33 8729-8741


Tシャツの「色の価値(好み)」は紡錘状回(fusiform gyrus;視覚情報を処理)で、「柄(ハングル文字)の価値」は後部上側頭回(posterior superior temporal gyrus;文字の意味などを処理)で保持されており、その二つを統合した最終的な「Tシャツの価値」は腹内側前頭前野(vmPFC)にコードされている。

We often have to make choices among multiattribute stimuli (e.g., a food that differs on its taste and health). Behavioral data suggest that choices are made by computing the value of the different attributes and then integrating them into an overall stimulus value signal. However, it is not known whether this theory describes the way the brain computes the stimulus value signals, or how the underlying computations might be implemented. We investigated these questions using a human fMRI task in which individuals had to evaluate T-shirts that varied in their visual esthetic (e.g., color) and semantic (e.g., meaning of logo printed in T-shirt) components. We found that activity in the fusiform gyrus, an area associated with the processing of visual features, correlated with the value of the visual esthetic attributes, but not with the value of the semantic attributes. In contrast, activity in posterior superior temporal gyrus, an area associated with the processing of semantic meaning, exhibited the opposite pattern. Furthermore, both areas exhibited functional connectivity with an area of ventromedial prefrontal cortex that reflects the computation of overall stimulus values at the time of decision. The results provide supporting evidence for the hypothesis that some attribute values are computed in cortical areas specialized in the processing of such features, and that those attribute-specific values are then passed to the vmPFC to be integrated into an overall stimulus value signal to guide the decision.


Interactions Between the Nucleus Accumbens and Auditory Cortices Predict Music Reward Value

Valorie N. Salimpoor, Iris van den Bosch, Natasa Kovacevic, Anthony Randal McIntosh, Alain Dagher, Robert J. Zatorre
Science 12 April 2013: Vol. 340 no. 6129 pp. 216-219

「側坐核(nucleus accumbens)と聴覚野の機能的結合から、その音楽に対する好みを予測できる」ことが分かった。

We used functional magnetic resonance imaging to investigate neural processes when music gains reward value the first time it is heard. The degree of activity in the mesolimbic striatal regions, especially the nucleus accumbens, during music listening was the best predictor of the amount listeners were willing to spend on previously unheard music in an auction paradigm. Importantly, the auditory cortices, amygdala, and ventromedial prefrontal regions showed increased activity during listening conditions requiring valuation, but did not predict reward value, which was instead predicted by increasing functional connectivity of these regions with the nucleus accumbens as the reward value increased. Thus, aesthetic rewards arise from the interaction between mesolimbic reward circuitry and cortical networks involved in perceptual analysis and valuation.


Rapid Brain Responses Independently Predict Gain Maximization and Loss Minimization during Economic Decision Making

Rene San Martin, Lawrence G. Appelbaum, John M. Pearson, Scott A. Huettel, and Marty G. Woldorff
J. Neurosci. 2013;33 7011-7019

Success in many decision-making scenarios depends on the ability to maximize gains and minimize losses. Even if an agent knows which cues lead to gains and which lead to losses, that agent could still make choices yielding suboptimal rewards. Here, by analyzing event-related potentials (ERPs) recorded in humans during a probabilistic gambling task, we show that individuals' behavioral tendencies to maximize gains and to minimize losses are associated with their ERP responses to the receipt of those gains and losses, respectively. We focused our analyses on ERP signals that predict behavioral adjustment: the frontocentral feedback-related negativity (FRN) and two P300 (P3) subcomponents, the frontocentral P3a and the parietal P3b. We found that, across participants, gain maximization was predicted by differences in amplitude of the P3b for suboptimal versus optimal gains (i.e., P3b amplitude difference between the least good and the best gains). Conversely, loss minimization was predicted by differences in the P3b amplitude to suboptimal versus optimal losses (i.e., difference between the worst and the least bad losses). Finally, we observed that the P3a and P3b, but not the FRN, predicted behavioral adjustment on subsequent trials, suggesting a specific adaptive mechanism by which prior experience may alter ensuing behavior. These findings indicate that individual differences in gain maximization and loss minimization are linked to individual differences in rapid neural responses to monetary outcomes.


Double Dissociation between the Anterior Cingulate Cortex and Nucleus Accumbens Core in Encoding the Context versus the Content of Pavlovian Cocaine Cue Extinction

Mary M. Torregrossa, Jessica Gordon, and Jane R. Taylor
J. Neurosci. 2013;33 8370-8377

One strategy proposed to treat addictive disorders is to extinguish the association between environmental stimuli (cues) and actions associated with drug use to reduce relapse. The context specificity of extinction learning, however, impairs the ability of addicts to generalize extinction training to the drug-taking context. We previously reported that the NMDA receptor partial agonist D-cycloserine administered after pavlovian extinction of cocaine cues in the nucleus accumbens core (NAc) reduced cue-induced renewal. Nevertheless, it was unclear whether this was due to disrupted contextual encoding of extinction or enhanced extinction consolidation. Thus, we examined the effect of the NMDA receptor antagonist D-AP5 on context encoding versus cue extinction learning. We also determined the role of the anterior cingulate cortex (ACC) in encoding the cue extinction memory or the context, due to its projections to NAc, and hypothesized the role in conflict monitoring and contextual modulation of decision making. Using rats, we observed that NMDA receptor antagonism in the NAc did not alter context encoding but did interfere with acquisition of the cue extinction memory, i.e., learning, conversely inactivation of the ACC reduced the contextual encoding of extinction but did not interfere with the acquisition or expression of extinction. The observed effects were not present in the absence of cue extinction training. Additionally, the contextual memory did not appear to be consolidated in the ACC as neither postsession inactivation nor protein synthesis inhibition impaired context-appropriate responding. These results have implications for overcoming the context specificity of extinction to treat psychiatric disorders including addiction.


Selective and graded coding of reward uncertainty by neurons in the primate anterodorsal septal region

Ilya E Monosov & Okihide Hikosaka
Nature Neuroscience (2013) doi:10.1038/nn.3398
Received 04 October 2012 Accepted 06 April 2013 Published online 12 May 2013

彦坂ラボからNat. Neurosci. サル電気生理。「Septal region」は報酬の不確実性をコードしている(報酬確率が50%の時に最も活動し、0/100%の時には活動しない)。一方で罰の不確実性はコードしていない。 http://www.nature.com/neuro/journal/vaop/ncurrent/full/nn.3398.html

Natural environments are uncertain. Uncertainty of emotional outcomes can induce anxiety and raise vigilance, promote and signal the opportunity for learning, modulate economic choice and regulate risk-seeking. Here we demonstrate that a subset of neurons in the anterodorsal region of the primate septum (ADS) are primarily devoted to processing uncertainty in a highly specific manner. Those neurons were selectively activated by visual cues indicating probabilistic delivery of reward (for example, 25%, 50% and 75% reward) and did not respond to cues indicating certain outcomes (0% and 100% reward). The average ADS uncertainty response was graded with the magnitude of reward uncertainty and selectively signaled uncertainty about rewards rather than punishments. The selective and graded information about reward uncertainty encoded by many neurons in the ADS may underlie modulation of uncertainty of value- and sensorimotor-related areas to regulate goal-directed behavior.


Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task

Andrew M. Wikenheiser, David W. Stephens, and A. David Redish
PNAS May 14, 2013 vol. 110 no. 20 8308-8313

Laboratory studies of decision making often take the form of two-alternative, forced-choice paradigms. In natural settings, however, many decision problems arise as stay/go choices. We designed a foraging task to test intertemporal decision making in rats via stay/go decisions. Subjects did not follow the rate-maximizing strategy of choosing only food items associated with short delays. Instead, rats were often willing to wait for surprisingly long periods, and consequently earned a lower rate of food intake than they might have by ignoring long-delay options. We tested whether foraging theory or delay discounting models predicted the behavior we observed but found that these models could not account for the strategies subjects selected. Subjects’ behavior was well accounted for by a model that incorporated a cost for rejecting potential food items. Interestingly, subjects’ cost sensitivity was proportional to environmental richness. These findings are at odds with traditional normative accounts of decision making but are consistent with retrospective considerations having a deleterious influence on decisions (as in the “sunk-cost” effect). More broadly, these findings highlight the utility of complementing existing assays of decision making with tasks that mimic more natural decision topologies.


Rational integration of noisy evidence and prior semantic expectations in sentence interpretation

Edward Gibson, Leon Bergen, and Steven T. Piantadosi
PNAS May 14, 2013 vol. 110 no. 20 8051-8056

Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Indeed, like many cognitive systems, language processing mechanisms may even be “well designed”–in this case for the task of recovering intended meaning from noisy utterances. In particular, comprehension mechanisms may be sensitive to the types of information that an idealized statistical comprehender would be sensitive to. Here, we evaluate four predictions about such a rational (Bayesian) noisy-channel language comprehender in a sentence comprehension task: (i) semantic cues should pull sentence interpretation towards plausible meanings, especially if the wording of the more plausible meaning is close to the observed utterance in terms of the number of edits; (ii) this process should asymmetrically treat insertions and deletions due to the Bayesian “size principle”; such nonliteral interpretation of sentences should (iii) increase with the perceived noise rate of the communicative situation and (iv) decrease if semantically anomalous meanings are more likely to be communicated. These predictions are borne out, strongly suggesting that human language relies on rational statistical inference over a noisy channel.


Hippocampal place-cell sequences depict future paths to remembered goals

Brad E. Pfeiffer & David J. Foster
Nature 497, 74–79 (2 May 2013) | doi:10.1038/nature12112

Effective navigation requires planning extended routes to remembered goal locations. Hippocampal place cells have been proposed to have a role in navigational planning, but direct evidence has been lacking. Here we show that before goal-directed navigation in an open arena, the rat hippocampus generates brief sequences encoding spatial trajectories strongly biased to progress from the subject’s current location to a known goal location. These sequences predict immediate future behaviour, even in cases in which the specific combination of start and goal locations is novel. These results indicate that hippocampal sequence events characterized previously in linearly constrained environments as ‘replay’ are also capable of supporting a goal-directed, trajectory-finding mechanism, which identifies important places and relevant behavioural paths, at specific times when memory retrieval is required, and in a manner that could be used to control subsequent navigational behaviour.


Limits in decision making arise from limits in memory retrieval

Gyslain Giguère and Bradley C. Love
PNAS May 7, 2013 vol. 110 no. 19 7613-7618

Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people’s memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people’s test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers.


Coupling social attention to the self forms a network for personal significance

Jie Sui, Pia Rotshtein, and Glyn W. Humphreys
PNAS May 7, 2013 vol. 110 no. 19 7607-7612

Prior social psychological studies show that newly assigned personal significance can modulate high-level cognitive processes, e.g., memory and social evaluation, with self- and other-related information processed in dissociated prefrontal structure: ventral vs. dorsal, respectively. Here, we demonstrate the impact of personal significance on perception and show the neural network that supports this effect. We used an associative learning procedure in which we “tag” a neutral shape with a self-relevant label. Participants were instructed to associate three neutral shapes with labels for themselves, their best friend, or an unfamiliar other. Functional magnetic resonance imaging data were acquired while participants judged whether the shape-label pairs were maintained or swapped. Behaviorally, participants rapidly tagged a neutral stimulus with self-relevance, showing a robust advantage for self-tagged stimuli. Self-tagging responses were associated with enhanced activity in brain regions linked to self-representation [the ventral medial prefrontal cortex (vmPFC)] and to sensory-driven regions associated with social attention [the left posterior superior temporal sulcus (LpSTS)]. In contrast, associations formed with other people recruited a dorsal frontoparietal control network, with the two networks being inversely correlated. Responses in the vmPFC and LpSTS predicted behavioral self-bias effects. Effective connectivity analyses showed that the vmPFC and the LpSTS were functionally coupled, with the strength of coupling associated with behavioral self-biases. The data show that assignment of personal social significance affects perceptual matching by coupling internal self-representations to brain regions modulating attentional responses to external stimuli.


Rescuing cocaine-induced prefrontal cortex hypoactivity prevents compulsive cocaine seeking

Billy T. Chen, Hau-Jie Yau, Christina Hatch, Ikue Kusumoto-Yoshida, Saemi L. Cho + et al.
Nature 496, 359–362 (18 April 2013) doi:10.1038/nature12024

Loss of control over harmful drug seeking is one of the most intractable aspects of addiction, as human substance abusers continue to pursue drugs despite incurring significant negative consequences1. Human studies have suggested that deficits in prefrontal cortical function and consequential loss of inhibitory control2, 3, 4 could be crucial in promoting compulsive drug use. However, it remains unknown whether chronic drug use compromises cortical activity and, equally important, whether this deficit promotes compulsive cocaine seeking. Here we use a rat model of compulsive drug seeking5, 6, 7, 8 in which cocaine seeking persists in a subgroup of rats despite delivery of noxious foot shocks. We show that prolonged cocaine self-administration decreases ex vivo intrinsic excitability of deep-layer pyramidal neurons in the prelimbic cortex, which was significantly more pronounced in compulsive drug-seeking animals. Furthermore, compensating for hypoactive prelimbic cortex neurons with in vivo optogenetic prelimbic cortex stimulation significantly prevented compulsive cocaine seeking, whereas optogenetic prelimbic cortex inhibition significantly increased compulsive cocaine seeking. Our results show a marked reduction in prelimbic cortex excitability in compulsive cocaine-seeking rats, and that in vivo optogenetic prelimbic cortex stimulation decreased compulsive drug-seeking behaviours. Thus, targeted stimulation of the prefrontal cortex could serve as a promising therapy for treating compulsive drug use.


Associative Learning Enhances Population Coding by Inverting Interneuronal Correlation Patterns

James M. Jeanne, Tatyana O. Sharpee, Timothy Q. Gentner
Neuron, Volume 78, Issue 2, 352-363, 24 April 2013

Learning-dependent cortical encoding has been well described in single neurons. But behaviorally relevant sensory signals drive the coordinated activity of millions of cortical neurons; whether learning produces stimulus-specific changes in population codes is unknown. Because the pattern of firing rate correlations between neurons—an emergent property of neural populations—can significantly impact encoding fidelity, we hypothesize that it is a target for learning. Using an associative learning procedure, we manipulated the behavioral relevance of natural acoustic signals and examined the evoked spiking activity in auditory cortical neurons in songbirds. We show that learning produces stimulus-specific changes in the pattern of interneuronal correlations that enhance the ability of neural populations to recognize signals relevant for behavior. This learning-dependent enhancement increases with population size. The results identify the pattern of interneuronal correlation in neural populations as a target of learning that can selectively enhance the representations of specific sensory signals.


Decision Making: From Neuroscience to Psychiatry

Daeyeol Lee
Neuron, Volume 78, Issue 2, 233-248, 24 April 2013

Adaptive behaviors increase the likelihood of survival and reproduction and improve the quality of life. However, it is often difficult to identify optimal behaviors in real life due to the complexity of the decision maker’s environment and social dynamics. As a result, although many different brain areas and circuits are involved in decision making, evolutionary and learning solutions adopted by individual decision makers sometimes produce suboptimal outcomes. Although these problems are exacerbated in numerous neurological and psychiatric disorders, their underlying neurobiological causes remain incompletely understood. In this review, theoretical frameworks in economics and machine learning and their applications in recent behavioral and neurobiological studies are summarized. Examples of such applications in clinical domains are also discussed for substance abuse, Parkinson’s disease, attention-deficit/hyperactivity disorder, schizophrenia, mood disorders, and autism. Findings from these studies have begun to lay the foundations necessary to improve diagnostics and treatment for various neurological and psychiatric disorders.