A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Human VMPFC encodes early signatures of confidence in perceptual decisions

Choice confidence, an individual’s internal estimate of judgment accuracy, plays a critical role in adaptive behaviour, yet its neural representations during decision formation remain underexplored. Here, we recorded simultaneous EEG-fMRI while participants performed a direction discrimination task and rated their confidence on each trial. Using multivariate single-trial discriminant analysis of the EEG, we identified a stimulus-independent component encoding confidence, which appeared prior to subjects’ explicit choice and confidence report, and was consistent with a confidence measure predicted by an accumulation-to-bound model of decisionmaking. Importantly, trial-to-trial variability in this electrophysiologically-derived confidence signal was uniquely associated with fMRI responses in the ventromedial prefrontal cortex (VMPFC), a region not typically associated with confidence for perceptual decisions. Furthermore, activity in the VMPFC was functionally coupled with regions of the frontal cortex linked to perceptual decision-making and metacognition. Our results suggest that the VMPFC holds an early confidence representation arising from decision dynamics, preceding and potentially informing metacognitive evaluation

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

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

Normative computations, uncertainty biases, and lifespan differences

Learning often takes place in environments with uncertainty about current and future outcomes. To behave adaptively in these circumstances, people need to learn beliefs from past experiences, based on which they can predict future outcomes. In my dissertation, I examine: (1) Normative computations that should determine learning under uncertainty. (2) Uncertainty biases that lead to deviations from normative learning. (3) Age-related differences in learning under uncertainty that are characteristic across the lifespan. Here, the term normative computations from the field of computational neuroscience refers to computations that provide an optimal solution to a learning and decision-making problem. My dissertation studies draw on computational models that implement normative computations and formally define uncertainty. Based on these models, the studies systematically investigated to what degree younger adults and people across the lifespan consider uncertainty when learning from their experiences. I begin by illustrating that adaptive behavior consists of several related steps, including a representation of the environment, decision making, and learning (Introduction). Based on this, I present a framework that decomposes uncertainty into three forms: perceptual uncertainty, expected uncertainty, and unexpected uncertainty (Normative computations). Perceptual uncertainty is related to sensory information processing, expected uncertainty arises from outcome variability, and unexpected uncertainty is the consequence of changes in the environment. For each form, I describe how individuals should learn under uncertainty based on normative computations. I then show that biases, that is, deviations from a normative consideration of uncertainty, are characteristic of human learning behavior (Uncertainty biases). Finally, I motivate why capturing these biases in computational models of cognition can improve our understanding of age-related lifespan differences in learning under uncertainty (Lifespan differences). The first dissertation study (Bruckner et al., 2020a) examined which normative computations should guide learning under perceptual uncertainty, to which degree humans regulate learning accordingly, and how past perceptual choices bias this process. The second study (Nassar et al., 2016) investigated expected and unexpected uncertainty in younger and older adults, particularly how biases in the consideration of uncertainty explain age-related learning differences. The third study (Bruckner et al., 2020b) built upon this and examined the role of simplified learning strategies across the lifespan. Finally, the fourth study (Van den Bos et al., 2018) was an opinion paper on how applying computational cognitive models advances our understanding of age-related lifespan differences in learning and decision making. In the following, I briefly summarize the results of the dissertation studies mentioned above. In Bruckner et al. (2020a), we showed that perceptual uncertainty often corrupts learning because of misinterpreted perceptual information. Learning behavior under perceptual uncertainty should be more cautious than in perceptually clear situations to avoid such misinterpretations. We found that humans consider perceptual uncertainty during learning. However, we also identified learning biases driven by previous perceptual choices, which led to a less cautious regulation of learning. In Nassar et al. (2016), our results suggested that age-related learning differences are related to the adjustment to expected uncertainty. In particular, we found that older adults (60 to 80 years) exhibit a bias to underestimate uncertainty about their beliefs compared to younger adults (20 to 30 years). This form of uncertainty underestimation leads to less flexible learning behavior compared to younger adults. In Bruckner et al. (2020b), we found that age-related impairments in learning under uncertainty often arise because children (7 to 11 years) and older adults resort to simplified learning strategies that lead to more repetitive responding (perseveration) and stronger environmental influences on behavior (environmental control) compared to younger adults. Finally, in Van den Bos et al. (2018), we argued that computational cognitive models are an essential tool to gain a better understanding of age-related learning and decision-making differences. In particular, we illustrated both promises of the application of computational models to study age-related behavioral differences (concerning risk-taking, strategy selection, and reinforcement learning) and potential pitfalls. After discussing the implications of these studies (General discussion and future directions), I propose a cognitive model of learning under uncertainty based on the new insights of my studies and previous work in the literature (Uncertainty in the cycle of adaptive behavior). In summary, the dissertation highlights that learning is a dynamic process that is influenced by multiple forms of uncertainty. People take uncertainty into account during learning but show inherent uncertainty biases that substantially change across the lifespan.Adaptives Verhalten verlangt eine ständige Verarbeitung von neuen Ereignissen sowie eine Reaktion auf diese. In der Psychologie und den Neurowissenschaften wird dies als Lern- und Entscheidungsprozess bezeichnet. Solche Prozesse finden in der Regel in Situationen statt, in denen Unsicherheit über aktuelle und zukünftige Ereignisse herrscht. Um sich in derartigen Situationen erfolgreich zurechtfinden zu können, muss man aus den Erfahrungen der Vergangenheit Vorhersagen über zukünftige Ereignisse ableiten. Die Dissertation behandelt folgende Themen: (1) Normative Berechnungen, die dem Lernen unter Unsicherheit zugrunde liegen sollten. (2) Verzerrungen, die bei der Berücksichtigung von Unsicherheit zu Abweichungen vom normativen Lernen führen. (3) Altersrelatierte Unterschiede über die Lebensspanne, die beim Lernen unter Unsicherheit charakteristisch sind. Der Begriff normative Berechnungen aus dem Forschungsfeld Computational Neuroscience bezieht sich in dieser Dissertation auf Berechnungen, die zu einer optimalen Lösung eines Lern- und Entscheidungsproblems führen. Meine Dissertationsstudien basieren auf Computermodellen, die normative Berechnungen implementieren und Unsicherheit formal definieren. Anhand dieser Modelle wird systematisch untersucht, inwieweit Menschen im jüngeren Erwachsenenalter und über die Lebensspanne Unsicherheit berücksichtigen, um aus ihren Erfahrungen zu lernen. Zu Beginn der Dissertation wird demonstriert, dass adaptives Verhalten aus mehreren Schritten besteht, von der Repräsentation der Umgebung über die Entscheidungsfindung bis hin zu Lernprozessen (Introduction). Auf dieser Grundlage stelle ich zunächst ein Modell vor, das Unsicherheit in drei Formen unterteilt: Perzeptuelle Unsicherheit, erwartete Unsicherheit und unerwartete Unsicherheit (Normative computations). Perzeptuelle Unsicherheit hängt mit der Verarbeitung sensorischer Informationen zusammen, erwartete Unsicherheit ergibt sich aus der Variabilität von Ereignissen und unerwartete Unsicherheit ist die Folge von Veränderungen in der Umgebung. Für jede dieser drei Formen beschreibe ich, wie Unsicherheit beim Lernen aufgrund von normativen Berechnungen berücksichtigt werden sollte. Danach zeige ich, dass Verzerrungen, also Abweichungen von den normativen Berechnungen, durch die man sich an Unsicherheit anpasst, charakteristisch für menschliches Lernen sind (Uncertainty biases). Abschließend erfolgt eine Darstellung, die verdeutlicht, warum die Erfassung dieser Verzerrungen mit Computermodellen nützlich ist, um altersrelatierte Unterschiede über die Lebensspanne beim Lernen unter Unsicherheit besser verstehen zu können (Lifespan differences). In der ersten Dissertationsstudie (Bruckner et al., 2020a) wurde untersucht, welche normativen Berechnungen beim Lernen unter perzeptueller Unsicherheit wichtig sind, in welchem Maße jüngere Erwachsene dementsprechend lernen und wie dieser Prozess durch vorherige perzeptuelle Entscheidungen verzerrt wird. In der zweiten Studie (Nassar et al., 2016) wurde Lernen unter erwarteter und unerwarteter Unsicherheit bei jüngeren und älteren Erwachsenen untersucht. Insbesondere wurde hier erforscht, inwiefern Verzerrungen bei der Berücksichtigung dieser Unsicherheiten altersrelatierte Lernunterschiede erklären. Die dritte Studie (Bruckner et al., 2020b) hat darauf aufgebaut und speziell bei Kindern und älteren Erwachsenen untersucht, inwiefern sie auf vereinfachte Lernstrategien zurückgreifen und auf normative Berechnungen verzichten. Die vierte Studie (Van den Bos et al., 2018) hat schließlich beschrieben, wie Computermodelle die Erforschung altersrelatierter Lernunterschiede über die Lebensspanne unterstützen können. Die Ergebnisse der oben genannten Studien werden im Folgenden kurz zusammengefasst. In Bruckner et al. (2020a) konnten wir zeigen, dass perzeptuelle Unsicherheit beim Lernen zu vorschnellen Schlussfolgerungen auf Basis von Fehlinterpretationen einer Wahrnehmung führen kann. Um vorschnelle Schlussfolgerungen zu vermeiden, sollte man sich beim Lernen unter perzeptueller Unsicherheit vorsichtiger verhalten als in perzeptuell eindeutigen Situationen. Wir fanden in dieser Studie heraus, dass Menschen perzeptuelle Unsicherheit beim Lernen berücksichtigen. Zusätzlich stellten wir allerdings eine Verzerrung bei der Berücksichtigung perzeptueller Unsicherheit aufgrund von früheren perzeptuellen Entscheidungen beim Lernen fest, die wiederum zu einer weniger vorsichtigen Anpassung des Lernverhaltens führt. In Nassar et al. (2016) fanden wir Hinweise darauf, dass altersrelatierte Lernunterschiede mit Verzerrungen bei der Anpassung an erwartete Unsicherheit zusammenhängen. Insbesondere stellten wir fest, dass ältere Erwachsene (60 bis 80 Jahre) dazu neigen, die Unsicherheit über ihre Erwartungen im Vergleich zu jüngeren Erwachsenen (20 bis 30 Jahre) zu unterschätzen. Diese Form der Unsicherheitsunterschätzung führt zu einem weniger flexiblen Lernverhalten im Vergleich zu jüngeren Erwachsenen. In Bruckner et al. (2020b) wurde gezeigt, dass altersrelatierte Unterschiede beim Lernen unter Unsicherheit damit zusammenhängen, dass Kinder (7 bis 11 Jahre) und ältere Erwachsene häufig auf vereinfachte Lernstrategien zurückgreifen, was dazu führt, dass Verhalten wiederholt (Perseveration) oder stärker durch die Umgebung beeinflusst wird (externe Kontrolle). Abschließend wurde in Van den Bos et al. (2018) argumentiert, dass Computermodellierung eine wichtige Methode ist, um altersrelatierte Unterschiede beim Lernen und in der Entscheidungsfindung besser zu verstehen. Hier wurden sowohl die Vorteile der Anwendung von Computermodellen zur Erforschung altersrelatierter Verhaltensunterschiede (in Bezug auf Risikobereitschaft, Strategieauswahl und Verstärkungslernen) als auch potenzielle Fallstricke aufgezeigt. Nach der Diskussion der Dissertationsprojekte (General discussion and future directions) stelle ich ein kognitives Modell zum Lernen unter Unsicherheit vor, das auf den neuen Erkenntnissen meiner Studien und früheren Arbeiten aus der Literatur basiert (Uncertainty in the cycle of adaptive behavior). Zusammenfassend legt meine Dissertation dar, dass Lernen ein dynamischer Prozess ist, der von vielfältigen Formen der Unsicherheit beeinflusst wird. Menschen berücksichtigen ihre Unsicherheit beim Lernen, weisen aber charakteristische Unsicherheitsverzerrungen auf, die sich im Laufe der Lebensspanne erheblich verändern

Choices change the temporal weighting of decision evidence

Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants’ sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making. NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making

Spatiotemporal neural correlates of confidence in perceptual decision making

In our interactions with the environment, we often make inferences based on noisy or incomplete perceptual information - for example, judging whether the person waving their hand in the distance is someone we know (as opposed to a stranger, greeting the person behind us). Such judgments are accompanied by a sense of confidence, that is, a degree of belief that we are correct, which ultimately determines how we act, adjust our subsequent decisions, or learn from errors. Neuroscience has only recently begun to characterise the representations of confidence in the animal and human brain, however the neural mechanisms and network dynamics supporting these representations are still unclear. The current thesis presents empirical findings from three studies that sought to provide a more complete characterisation of confidence during perceptual decision making, using a combination of electrophysiological and neuroimaging methods. Specifically, Study 1 (Chapter 2) investigated the temporal characteristics of confidence in relation to the perceptual decision. We recorded EEG measurements from human subjects during performance of a face vs. car categorisation task. On some trials, subjects were offered the possibility to opt out of the choice in exchange for a smaller but certain reward (relative to the reward obtained for correct choices), and the choice to use or decline this option reflected subjects’ confidence in their perceptual judgment. Neural activity discriminating between high vs. low confidence trials could be observed peaking approximately 600 ms after stimulus onset. Importantly, the temporal profile of this activity resembled a ramp-like process of evidence accumulation towards a decision, with confidence being reflected in the rate of the accumulation. Our results are in line with the notion that neural representations of confidence may arise from the same process that supports decision formation. Extending on these findings, in Study 2 (Chapter 3) we asked whether rhythmic patterns within the EEG signals may offer additional insights into the neural representations of confidence. Using an exploratory analysis of data from Study 1, we identified confidence-discriminating oscillatory activity in the alpha and beta frequency bands. This was most prominent over the sensorimotor electrodes contralateral to the motor effector that subjects used to indicate choice (i.e., right hand), consistent with a motor preparatory signal. Importantly however, the effect was transient in nature, peaking long before subjects could execute a response, and thus ruling out a direct link with overt motor behaviour. More intriguingly, the observed confidence effect appeared to overlap in time with the non-oscillatory representation of confidence identified in Study 1. In line with the view that motor systems track the evolution of the perceptual decision in preparation for impending action, results from Studies 1 and 2 open the possibility that confidence-related information may also be contained within these signals. Finally, following on from our work in the first study, we next aimed to capitalise on the single-trial neural representations of confidence obtained with EEG, in order to identify potentially correlated activity with high spatial resolution. To this end, in Study 3 (Chapter 4) we recorded simultaneous EEG and fMRI data while subjects performed a speeded motion discrimination task and rated their confidence on a trial-by-trial basis. Analysis of the EEG revealed a confidence-discriminating neural component which appeared prior to participants’ overt choice and was spatiotemporally consistent with our results from the first study. Crucially, we showed that haemodynamic responses in the ventromedial prefrontal cortex (VMPFC) were uniquely explained by trial-to-trial fluctuations in these early confidence-related neural signals. Notably, this activation was additional to what could be explained by subjects’ confidence ratings alone. We speculated that the VMPFC may support an early and/or automatic readout of perceptual confidence, potentially preceding explicit metacognitive appraisal. Together, our results reveal novel insights into the neural representations of perceptual confidence in the human brain, and point to new research directions that may help further disentangle the neural dynamics supporting confidence and metacognition

Evaluating weaknesses of "perceptual-cognitive training" and "brain training" methods in sport: An ecological dynamics critique

The recent upsurge in "brain training and perceptual-cognitive training," proposing to improve isolated processes, such as brain function, visual perception, and decision-making, has created significant interest in elite sports practitioners, seeking to create an "edge" for athletes. The claims of these related "performance-enhancing industries" can be considered together as part of a process training approach proposing enhanced cognitive and perceptual skills and brain capacity to support performance in everyday life activities, including sport. For example, the "process training industry" promotes the idea that playing games not only makes you a better player but also makes you smarter, more alert, and a faster learner. In this position paper, we critically evaluate the effectiveness of both types of process training programmes in generalizing transfer to sport performance. These issues are addressed in three stages. First, we evaluate empirical evidence in support of perceptual-cognitive process training and its application to enhancing sport performance. Second, we critically review putative modularized mechanisms underpinning this kind of training, addressing limitations and subsequent problems. Specifically, we consider merits of this highly specific form of training, which focuses on training of isolated processes such as cognitive processes (attention, memory, thinking) and visual perception processes, separately from performance behaviors and actions. We conclude that these approaches may, at best, provide some "general transfer" of underlying processes to specific sport environments, but lack "specificity of transfer" to contextualize actual performance behaviors. A major weakness of process training methods is their focus on enhancing the performance in body "modules" (e.g., eye, brain, memory, anticipatory sub-systems). What is lacking is evidence on how these isolated components are modified and subsequently interact with other process "modules," which are considered to underlie sport performance. Finally, we propose how an ecological dynamics approach, aligned with an embodied framework of cognition undermines the rationale that modularized processes can enhance performance in competitive sport. An ecological dynamics perspective proposes that the body is a complex adaptive system, interacting with performance environments in a functionally integrated manner, emphasizing that the inter-relation between motor processes, cognitive and perceptual functions, and the constraints of a sport task is best understood at the performer-environment scale of analysis