Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Task

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes

Remember the source: Dissociating frontal and parietal contributions to episodic memory

Event-related fMRI studies reveal that episodic memory retrieval modulates lateral and medial parietal cortices, dorsal middle frontal gyrus (MFG), and anterior pFC. These regions respond more for recognized old than correctly rejected new words, suggesting a neural correlate of retrieval success. Despite significant efforts examining retrieval success regions, their role in retrieval remains largely unknown. Here we asked the question, to what degree are the regions performing memory-specific operations? And if so, are they all equally sensitive to successful retrieval, or are other factors such as error detection also implicated? We investigated this question by testing whether activity in retrieval success regions was associated with task-specific contingencies (i.e., perceived targetness) or mnemonic relevance (e.g., retrieval of source context). To do this, we used a source memory task that required discrimination between remembered targets and remembered nontargets. For a given region, the modulation of neural activity by a situational factor such as target status would suggest a more domain-general role; similarly, modulations of activity linked to error detection would suggest a role inmonitoring and control rather than the accumulation of evidence from memory per se. We found that parietal retrieval success regions exhibited greater activity for items receiving correct than incorrect source responses, whereas frontal retrieval success regions were most active on error trials, suggesting that posterior regions signal successful retrieval whereas frontal regions monitor retrieval outcome. In addition, perceived targetness failed to modulate fMRI activity in any retrieval success region, suggesting that these regions are retrieval specific. We discuss the different functions that these regions may support and propose an accumulator model that captures the different pattern of responses seen in frontal and parietal retrieval success regions

The Human Brain Encodes Event Frequencies While Forming Subjective Beliefs

To make adaptive choices, humans need to estimate the probability of future events. Based on a Bayesian approach, it is assumed that probabilities are inferred by combining a priori, potentially subjective, knowledge with factual observations, but the precise neurobiological mechanism remains unknown. Here, we study whether neural encoding centers on subjective posterior probabilities, and data merely lead to updates of posteriors, or whether objective data are encoded separately alongside subjective knowledge. During fMRI, young adults acquired prior knowledge regarding uncertain events, repeatedly observed evidence in the form of stimuli, and estimated event probabilities. Participants combined prior knowledge with factual evidence using Bayesian principles. Expected reward inferred from prior knowledge was encoded in striatum. BOLD response in specific nodes of the default mode network (angular gyri, posterior cingulate, and medial prefrontal cortex) encoded the actual frequency of stimuli, unaffected by prior knowledge. In this network, activity increased with frequencies and thus reflected the accumulation of evidence. In contrast, Bayesian posterior probabilities, computed from prior knowledge and stimulus frequencies, were encoded in bilateral inferior frontal gyrus. Here activity increased for improbable events and thus signaled the violation of Bayesian predictions. Thus, subjective beliefs and stimulus frequencies were encoded in separate cortical regions. The advantage of such a separation is that objective evidence can be recombined with newly acquired knowledge when a reinterpretation of the evidence is called for. Overall this study reveals the coexistence in the brain of an experience-based system of inference and a knowledge-based system of inference

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) – to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates

Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR0011-09-3-0001, HR0011-09-C-0011

Categorical evidence, confidence and urgency during the integration of multi-feature information

Includes bibliographical references.2015 Summer.The present experiment utilized a temporally-extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Importantly, the design allowed differentiation of three important decision functions: 1) categorical evidence, 2) decisional confidence (the choice-independent probability that a decision will lead to a desirable state), and 3) urgency (a hypothetical signal representing a growing pressure to produce a behavioral response within each trial). In conjunction with model-based fMRI, the temporal evolution of these variables were tracked as participants deliberated about impending choices. The approach allowed investigation of the independent effects of urgency across the brain, and also the investigation of how urgency might modulate representations of categorical evidence and confidence. Representations associated with prediction errors during feedback were also investigated. Many cortical and striatal somatomotor regions tracked the dynamical evolution of categorical evidence, while many regions of the dorsal and ventral attention networks (Corbetta and Shulman, 2002) tracked decisional confidence and uncertainty. Urgency influenced activity in regions known to be associated with flexible control of the speed-accuracy trade-off (particularly the pre- SMA and striatum), and additionally modulated representations of categorical evidence and confidence. The results, therefore, link the urgency signal to two hypothetical mechanisms underling flexible control of decision thresholding (Bogacz et al., 2010): gain modulation of the striatal thresholding circuitry, and gain modulation of the integrated categorical evidence

An interoceptive predictive coding model of conscious presence

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness

Bridging Access to Consciousness, Cognitive Control and Metacognition : toward an application to Schizophrenia

The series of experiments carried out in the framework of my PhD research aimed to shed some light on the relationships between (un)conscious processing, cognitive control and metacognition (second-order judgments). More specifically, it consisted in investigated the issue of whether and how second-order judgments were influenced by some parameters of the corresponding first-order judgment, namely the load of cognitive control and the conscious versus non conscious nature of the information it is contingent on. These experiments, combined with recent empirical evidence and models of second-order judgments (Pleksac and Busemeyer, 2010) are consistent with a partial overlap between the prefrontal regions involved in metacognitive judgments and those involved in cognitive control and access to consciousness. That led us to consider the hypothesis that specific cognitive and metacognitive deficits could explain the loss of sense of authorship observed in schizophrenia.\ua0 To this purpose, an observational study was carried out in patients recovered after at least one psychotic episode. It involved two different populations, one having received a diagnosis of schizophrenia (schizophrenia group), the other of bipolar disorders (bipolar group). The paradigm consisted in a cognitive control task in which the subjects had to self-evaluate after each response (metacognitive task).\ua0 The preliminary results suggest that the clinical groups display very different cognitive and metacognitive profiles. In particular, compared to bipolar group, the schizophrenia group seems to be impaired in both cognitive control and metacognitive tasks. The schizophrenia group also showed correlations between cognitive control impairments and several metacognitive measures. The question of the correlation of these deficits with clinical variables will be considered, as far as they could present an interest for diagnose