The dual-path hypothesis for the emergence of anosognosia in Alzheimer's disease.

Although neurocognitive models have been proposed to explain anosognosia in Alzheimer's disease (AD), the neural cascade responsible for its origin in the human brain remains unknown. Here, we build on a mechanistic dual-path hypothesis that brings error-monitoring and emotional processing systems as key elements for self-awareness, with distinct impacts on the emergence of anosognosia in AD. Proceeding from the notion of anosognosia as a dimensional syndrome, varying between a lack of concern about one's own deficits (i.e., anosodiaphoria) and a complete lack of awareness of deficits, our hypothesis states that (i) unawareness of deficits would result from primary damage to the error-monitoring system, whereas (ii) anosodiaphoria would more likely result from an imbalance between emotional processing and error-monitoring. In the first case, a synaptic failure in the error-monitoring system, in which the anterior and posterior cingulate cortices play a major role, would have a negative impact on error (or deficits) awareness, preventing patients from becoming aware of their condition. In the second case, an impairment in the emotional processing system, in which the amygdala and the orbitofrontal cortex play a major role, would prevent patients from monitoring the internal milieu for relevant errors (or deficits) and assigning appropriate value to them, thus biasing their impact on the error-monitoring system. Our hypothesis stems on two scientific premises. One comes from preliminary results in AD patients showing a synaptic failure in the error-monitoring system along with a decline of awareness for cognitive difficulties at the time of diagnosis. Another comes from the somatic marker hypothesis, which proposes that emotional signals are critical to adaptive behavior. Further exploration of these premises will be of great interest to illuminate the foundations of self-awareness and improve our knowledge of the underlying paths of anosognosia in AD and other brain disorders.

Detecting Anosognosia from the Prodromal Stage of Alzheimer's Disease.

BACKGROUND: Though not originally developed for this purpose, the Healthy Aging Brain Care Monitor (HABC-M) seems a valuable instrument for assessing anosognosia in Alzheimer's disease (AD). OBJECTIVES: Our study aimed at 1) investigating the validity of the HABC-M (31 items), and its cognitive, psychological, and functional subscales, in discriminating AD patients from controls; 2) exploring whether the HABC-M discrepancy scores between the self-reports of patients/controls in these different domains and the respective ratings provided by their caregivers/informants correlate with an online measure of self-awareness; 3) determining whether the caregiver burden level, also derived from the HABC-M, could add additional support for detecting anosognosia. METHODS: The HABC-M was administered to 30 AD patients and 30 healthy controls, and to their caregivers/informants. A measure of online awareness was established from subjects' estimation of their performances in a computerized experiment. RESULTS: The HABC-M discrepancy scores distinguished AD patients from controls. The cognitive subscale discriminated the two groups from the prodromal AD stage, with an AUC of 0.88 [95% CI: 0.78;0.97]. Adding the caregiver burden level raised it to 0.94 [0.86;0.99]. Significant correlations between the HABC-M and online discrepancy scores were observed in the patients group, providing convergent validity of these methods. CONCLUSIONS: The cognitive HABC-M (six items) can detect anosognosia across the AD spectrum. The caregiver burden (four items) may corroborate the suspicion of anosognosia. The short-hybrid scale, built from these 10 items instead of the usual 31, showed the highest sensitivity for detecting anosognosia from the prodromal AD stage, which may further help with timely diagnosis.

Computational models of adaptive behavior and prefrontal cortex.

The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus-action associations through rewards; (2) predictive models learning stimulus- and/or action-outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

Additively Combining Utilities and Beliefs: Research Gaps and Algorithmic Developments.

Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computational resources. In this article, we review a suboptimal strategy - additively combining reward magnitude and reward probability attributes of options for value-based decision making. In addition, we present computational intricacies of a recently developed model (named MIX model) representing an algorithmic implementation of the additive strategy in sequential decision-making with two options. We also discuss its opportunities; and conceptual, inferential, and generalization issues. Furthermore, we suggest future studies that will reveal the potential and serve the further development of the MIX model as a general model of value-based choice making.

Imprecise neural computations as a source of adaptive behaviour in volatile environments.

In everyday life, humans face environments that feature uncertain and volatile or changing situations. Efficient adaptive behaviour must take into account uncertainty and volatility. Previous models of adaptive behaviour involve inferences about volatility that rely on complex and often intractable computations. Because such computations are presumably implausible biologically, it is unclear how humans develop efficient adaptive behaviours in such environments. Here, we demonstrate a counterintuitive result: simple, low-level inferences confined to uncertainty can produce near-optimal adaptive behaviour, regardless of the environmental volatility, assuming imprecisions in computation that conform to the psychophysical Weber law. We further show empirically that this Weber-imprecision model explains human behaviour in volatile environments better than optimal adaptive models that rely on high-level inferences about volatility, even when considering biologically plausible approximations of such models, as well as non-inferential models like adaptive reinforcement learning.

Neural mechanisms resolving exploitation-exploration dilemmas in the medial prefrontal cortex.

Everyday life often requires arbitrating between pursuing an ongoing action plan by possibly adjusting it versus exploring a new action plan instead. Resolving this so-called exploitation-exploration dilemma involves the medial prefrontal cortex (mPFC). Using human intracranial electrophysiological recordings, we discovered that neural activity in the ventral mPFC infers and tracks the reliability of the ongoing plan to proactively encode upcoming action outcomes as either learning signals or potential triggers to explore new plans. By contrast, the dorsal mPFC exhibits neural responses to action outcomes, which results in either improving or abandoning the ongoing plan. Thus, the mPFC resolves the exploitation-exploration dilemma through a two-stage, predictive coding process: a proactive ventromedial stage that constructs the functional signification of upcoming action outcomes and a reactive dorsomedial stage that guides behavior in response to action outcomes.

Temporal chunking as a mechanism for unsupervised learning of task-sets.

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data on a subject-by-subject basis confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

Human Decision-Making beyond the Rational Decision Theory.

Two recent studies (Farashahi et al. and Rouault et al.) provide compelling evidence refuting the Subjective Expected Utility (SEU) hypothesis as a ground model describing human decision-making. Together, these studies pave the way towards a new model that subsumes the notion of decision-making and adaptive behavior into a single account.

Prefrontal mechanisms combining rewards and beliefs in human decision-making.

In uncertain and changing environments, optimal decision-making requires integrating reward expectations with probabilistic beliefs about reward contingencies. Little is known, however, about how the prefrontal cortex (PFC), which subserves decision-making, combines these quantities. Here, using computational modelling and neuroimaging, we show that the ventromedial PFC encodes both reward expectations and proper beliefs about reward contingencies, while the dorsomedial PFC combines these quantities and guides choices that are at variance with those predicted by optimal decision theory: instead of integrating reward expectations with beliefs, the dorsomedial PFC built context-dependent reward expectations commensurable to beliefs and used these quantities as two concurrent appetitive components, driving choices. This neural mechanism accounts for well-known risk aversion effects in human decision-making. The results reveal that the irrationality of human choices commonly theorized as deriving from optimal computations over false beliefs, actually stems from suboptimal neural heuristics over rational beliefs about reward contingencies.

The Neuro-Computational Architecture of Value-Based Selection in the Human Brain.

Current neural models of value-based decision-making consider choices as a 2-stage process, proceeding from the "valuation" of each option under consideration to the "selection" of the best option on the basis of their subjective values. However, little is known about the computational mechanisms at play at the selection stage and its implementation in the human brain. Here, we used drift-diffusion models combined with model-based functional magnetic resonance imaging, effective connectivity, and multivariate pattern analysis to characterize the neuro-computational architecture of value-based decisions. We found that 2 key drift-diffusion computations at the selection stage, namely integration and choice readout, engage distinct brain regions, with the dorsolateral prefrontal cortex integrating a decision value signal computed in the ventromedial prefrontal cortex, and the posterior parietal cortex reading out choice outcomes. Our findings suggest that this prefronto-parietal network acts as a hub implementing behavioral selection through a distributed drift-diffusion process.

Managing competing goals - a key role for the frontopolar cortex.

Humans are set apart from other animals by many elements of advanced cognition and behaviour, including language, judgement and reasoning. What is special about the human brain that gives rise to these abilities? Could the foremost part of the prefrontal cortex (the frontopolar cortex), which has become considerably enlarged in humans during evolution compared with other animals, be important in this regard, especially as, in primates, it contains a unique cytoarchitectural field, area 10? The first studies of the function of the frontopolar cortex in monkeys have now provided critical new insights about its precise role in monitoring the significance of current and alternative goals. In human evolution, the frontopolar cortex may have acquired a further role in enabling the monitoring of the significance of multiple goals in parallel, as well as switching between them. Here, we argue that many other forms of uniquely human behaviour may benefit from this cognitive ability mediated by the frontopolar cortex.

Rewards and Cognitive Control in the Human Prefrontal Cortex.

The human prefrontal cortex (PFC) subserves cognitive control, that is, the ability to form behavioral strategies that coordinate actions and thoughts in relation to internal goals. Cognitive control involves the medial and lateral PFC but we still poorly understand how these regions guide strategy selection according to expected rewards. We addressed this issue using neuroimaging, computational modeling and model-based analyses of information flows between medial and lateral PFC. We show here that the (dorsal) medial PFC encodes and conveys to lateral PFC reward expectations driving strategy selection, while strategy selection originates in lateral PFC and propagates backward to medial PFC. This functional loop through lateral PFC enables strategy selection to further comply with learned rules encoded in lateral PFC rather than with reward expectations conveyed from medial PFC. Thus, the medial and lateral PFC are functionally coupled in cognitive control for integrating expected rewards and learned rules in strategy selection.

The Importance of Falsification in Computational Cognitive Modeling.

In the past decade the field of cognitive sciences has seen an exponential growth in the number of computational modeling studies. Previous work has indicated why and how candidate models of cognition should be compared by trading off their ability to predict the observed data as a function of their complexity. However, the importance of falsifying candidate models in light of the observed data has been largely underestimated, leading to important drawbacks and unjustified conclusions. We argue here that the simulation of candidate models is necessary to falsify models and therefore support the specific claims about cognitive function made by the vast majority of model-based studies. We propose practical guidelines for future research that combine model comparison and falsification.

Neural coding of prior expectations in hierarchical intention inference.

The ability to infer other people's intentions is crucial for successful human social interactions. Such inference relies on an adaptive interplay of sensory evidence and prior expectations. Crucially, this interplay would also depend on the type of intention inferred, i.e., on how abstract the intention is. However, what neural mechanisms adjust the interplay of prior and sensory evidence to the abstractness of the intention remains conjecture. We addressed this question in two separate fMRI experiments, which exploited action scenes depicting different types of intentions (Superordinate vs. Basic; Social vs. Non-social), and manipulated both prior and sensory evidence. We found that participants increasingly relied on priors as sensory evidence became scarcer. Activity in the medial prefrontal cortex (mPFC) reflected this interplay between the two sources of information. Moreover, the more abstract the intention to infer (Superordinate > Basic, Social > Non-Social), the greater the modulation of backward connectivity between the mPFC and the temporo-parietal junction (TPJ), resulting in an increased influence of priors over the intention inference. These results suggest a critical role for the fronto-parietal network in adjusting the relative weight of prior and sensory evidence during hierarchical intention inference.

Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.

Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments.

Prefrontal executive function and adaptive behavior in complex environments.

The prefrontal cortex (PFC) subserves higher cognitive abilities such as planning, reasoning and creativity. Here we review recent findings from both empirical and theoretical studies providing new insights about these cognitive abilities and their neural underpinnings in the PFC as overcoming key adaptive limitations in reinforcement learning. We outline a unified theoretical framework describing the PFC function as implementing an algorithmic solution approximating statistically optimal, but computationally intractable, adaptive processes. The resulting PFC functional architecture combines learning, planning, reasoning and creativity processes for balancing exploitation and exploration behaviors and optimizing behavioral adaptations in uncertain, variable and open-ended environments.

An evolutionary computational theory of prefrontal executive function in decision-making.

The prefrontal cortex subserves executive control and decision-making, that is, the coordination and selection of thoughts and actions in the service of adaptive behaviour. We present here a computational theory describing the evolution of the prefrontal cortex from rodents to humans as gradually adding new inferential Bayesian capabilities for dealing with a computationally intractable decision problem: exploring and learning new behavioural strategies versus exploiting and adjusting previously learned ones through reinforcement learning (RL). We provide a principled account identifying three inferential steps optimizing this arbitration through the emergence of (i) factual reactive inferences in paralimbic prefrontal regions in rodents; (ii) factual proactive inferences in lateral prefrontal regions in primates and (iii) counterfactual reactive and proactive inferences in human frontopolar regions. The theory clarifies the integration of model-free and model-based RL through the notion of strategy creation. The theory also shows that counterfactual inferences in humans yield to the notion of hypothesis testing, a critical reasoning ability for approximating optimal adaptive processes and presumably endowing humans with a qualitative evolutionary advantage in adaptive behaviour.

Human cognition. Foundations of human reasoning in the prefrontal cortex.

The prefrontal cortex (PFC) subserves reasoning in the service of adaptive behavior. Little is known, however, about the architecture of reasoning processes in the PFC. Using computational modeling and neuroimaging, we show here that the human PFC has two concurrent inferential tracks: (i) one from ventromedial to dorsomedial PFC regions that makes probabilistic inferences about the reliability of the ongoing behavioral strategy and arbitrates between adjusting this strategy versus exploring new ones from long-term memory, and (ii) another from polar to lateral PFC regions that makes probabilistic inferences about the reliability of two or three alternative strategies and arbitrates between exploring new strategies versus exploiting these alternative ones. The two tracks interact and, along with the striatum, realize hypothesis testing for accepting versus rejecting newly created strategies.

Reasoning, learning, and creativity: frontal lobe function and human decision-making.

The frontal lobes subserve decision-making and executive control--that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior.

Perceptual classification in a rapidly changing environment.

Humans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorization when the environment changes rapidly. Here, we used a combination of computational modeling and functional neuroimaging to understand how humans classify visual stimuli drawn from categories whose mean and variance jumped unpredictably. Models based on optimal learning (Bayesian model) and a cognitive strategy (working memory model) both explained unique variance in choice, reaction time, and brain activity. However, the working memory model was the best predictor of performance in volatile environments, whereas statistically optimal performance emerged in periods of relative stability. Bayesian and working memory models predicted decision-related activity in distinct regions of the prefrontal cortex and midbrain. These findings suggest that perceptual category judgments, like value-guided choices, may be guided by multiple controllers.