Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

Corrigendum: Interaction between habits as action sequences and goal-directed behavior under time pressure.

[This corrects the article DOI: 10.3389/fnins.2022.996957.].

The effects of probabilistic context inference on motor adaptation.

Humans have been shown to adapt their movements when a sudden or gradual change to the dynamics of the environment are introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Humans are also able to adapt to multiple changes in dynamics presented separately, and to be able to switch between adapted movements on the fly. Such switching relies on contextual information which is often noisy or misleading, affecting the switch between known adaptations. Recently, computational models for motor adaptation and context inference have been introduced, which contain components for context inference and Bayesian motor adaptation. These models were used to show the effects of context inference on learning rates across different experiments. We expanded on these works by using a simplified version of the recently-introduced COIN model to show that the effects of context inference on motor adaptation and control go even further than previously shown. Here, we used this model to simulate classical motor adaptation experiments from previous works and showed that context inference, and how it is affected by the presence and reliability of feedback, effect a host of behavioral phenomena that had so far required multiple hypothesized mechanisms, lacking a unified explanation. Concretely, we show that the reliability of direct contextual information, as well as noisy sensory feedback, typical of many experiments, effect measurable changes in switching-task behavior, as well as in action selection, that stem directly from probabilistic context inference.

Shorter planning depth and higher response noise during sequential decision-making in old age.

Forward planning is crucial to maximize outcome in complex sequential decision-making scenarios. In this cross-sectional study, we were particularly interested in age-related differences of forward planning. We presumed that especially older individuals would show a shorter planning depth to keep the costs of model-based decision-making within limits. To test this hypothesis, we developed a sequential decision-making task to assess forward planning in younger (age < 40 years; n = 25) and older (age > 60 years; n = 27) adults. By using reinforcement learning modelling, we inferred planning depths from participants' choices. Our results showed significantly shorter planning depths and higher response noise for older adults. Age differences in planning depth were only partially explained by well-known cognitive covariates such as working memory and processing speed. Consistent with previous findings, this indicates age-related shifts away from model-based behaviour in older adults. In addition to a shorter planning depth, our findings suggest that older adults also apply a variety of heuristical low-cost strategies.

Network instability dynamics drive a transient bursting period in the developing hippocampus in vivo.

Spontaneous correlated activity is a universal hallmark of immature neural circuits. However, the cellular dynamics and intrinsic mechanisms underlying network burstiness in the intact developing brain are largely unknown. Here, we use two-photon Ca2+ imaging to comprehensively map the developmental trajectories of spontaneous network activity in the hippocampal area CA1 of mice in vivo. We unexpectedly find that network burstiness peaks after the developmental emergence of effective synaptic inhibition in the second postnatal week. We demonstrate that the enhanced network burstiness reflects an increased functional coupling of individual neurons to local population activity. However, pairwise neuronal correlations are low, and network bursts (NBs) recruit CA1 pyramidal cells in a virtually random manner. Using a dynamic systems modeling approach, we reconcile these experimental findings and identify network bi-stability as a potential regime underlying network burstiness at this age. Our analyses reveal an important role of synaptic input characteristics and network instability dynamics for NB generation. Collectively, our data suggest a mechanism, whereby developing CA1 performs extensive input-discrimination learning prior to the onset of environmental exploration.

Revealing human sensitivity to a latent temporal structure of changes.

Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry.

Computational Theories of Alcohol Use Disorder: Mapping Learning and Choice Mechanisms on Symptoms.

Alcohol use disorder (AUD) is characterized by a combination of symptoms including excessive craving, loss of control, and progressive neglect of alternative pleasures. A mechanistic understanding of what drives these symptoms is needed to improve diagnostic stratification and to develop new treatment and prevention strategies for AUD. To date, there is no consensus regarding a unifying mechanistic framework that accounts for the different symptoms of AUD. Reinforcement learning (RL) and economic choice theories may be key to elucidating the underlying processes of symptom development and maintenance in AUD. These algorithms may account for the different behavioral and physiological phenomena and are suited to dissect mechanisms linked to different symptoms of AUD. We here review different RL and economic choice models and how they map onto three symptoms of AUD: (1) cue-induced craving, (2) neglect of alternative rewards, and (3) consumption despite adverse consequences. For each symptom and theory, we describe findings from animal and human studies. In humans, we focus on empirical studies that investigated RL models in the context of treatment outcome in AUD. The review indicates important gaps to be addressed in the future by highlighting the challenges in transferring findings from RL and economic choice studies to clinical application. We also critically evaluate the potential and pitfalls of a symptom-oriented approach and highlight the importance of elucidating the role of learning and decision-making processes across diagnostic boundaries.

Forward planning driven by context-dependant conflict processing in anterior cingulate cortex.

Cognitive control and forward planning in particular is costly, and therefore must be regulated such that the amount of cognitive resources invested is adequate to the current situation. However, knowing in advance how beneficial forward planning will be in a given situation is hard. A way to know the exact value of planning would be to actually do it, which would ab initio defeat the purpose of regulating planning, i.e. the reduction of computational and time costs. One possible solution to this dilemma is that planning is regulated by learned associations between stimuli and the expected demand for planning. Such learning might be based on generalisation processes that cluster together stimulus states with similar control relevant properties into more general control contexts. In this way, the brain could infer the demand for planning, based on previous experience with situations that share some structural properties with the current situation. Here, we used a novel sequential task to test the hypothesis that people use control contexts to efficiently regulate their forward planning, using behavioural and functional magnetic resonance imaging data. Consistent with our hypothesis, reaction times increased with trial-by-trial conflict, where this increase was more pronounced in a context with a learned high demand for planning. Similarly, we found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, and this increase was more pronounced in a context with generally high demand for planning. Taken together, the results indicate that the dACC integrates representations of planning demand at different levels of abstraction to regulate planning in an efficient and situation-appropriate way.

Interaction between habits as action sequences and goal-directed behavior under time pressure.

Human behavior consists in large parts of action sequences that are often repeated in mostly the same way. Through extensive repetition, sequential responses become automatic or habitual, but our environment often confronts us with events to which we have to react flexibly and in a goal-directed manner. To assess how implicitly learned action sequences interfere with goal-directed control, we developed a novel behavioral paradigm in which we combined action sequence learning through repetition with a goal-directed task component. So-called dual-target trials require the goal-directed selection of the response with the highest reward probability in a fast succession of trials with short response deadlines. Importantly, the response primed by the learned action sequence is sometimes different from that required by the goal-directed task. As expected, we found that participants learned the action sequence through repetition, as evidenced by reduced reaction times (RT) and error rates (ER), while still acting in a goal-directed manner in dual-target trials. Specifically, we found that the learned action sequence biased choices in the goal-directed task toward the sequential response, and this effect was more pronounced the better individuals had learned the sequence. Our novel task may help shed light on the acquisition of automatic behavioral patterns and habits through extensive repetition, allows to assess positive features of habitual behavior (e.g., increased response speed and reduced error rates), and importantly also the interaction of habitual and goal-directed behaviors under time pressure.

An empirical evaluation of active inference in multi-armed bandits.

A key feature of sequential decision making under uncertainty is a need to balance between exploiting-choosing the best action according to the current knowledge, and exploring-obtaining information about values of other actions. The multi-armed bandit problem, a classical task that captures this trade-off, served as a vehicle in machine learning for developing bandit algorithms that proved to be useful in numerous industrial applications. The active inference framework, an approach to sequential decision making recently developed in neuroscience for understanding human and animal behaviour, is distinguished by its sophisticated strategy for resolving the exploration-exploitation trade-off. This makes active inference an exciting alternative to already established bandit algorithms. Here we derive an efficient and scalable approximate active inference algorithm and compare it to two state-of-the-art bandit algorithms: Bayesian upper confidence bound and optimistic Thompson sampling. This comparison is done on two types of bandit problems: a stationary and a dynamic switching bandit. Our empirical evaluation shows that the active inference algorithm does not produce efficient long-term behaviour in stationary bandits. However, in the more challenging switching bandit problem active inference performs substantially better than the two state-of-the-art bandit algorithms. The results open exciting venues for further research in theoretical and applied machine learning, as well as lend additional credibility to active inference as a general framework for studying human and animal behaviour.

Neuronal Sequence Models for Bayesian Online Inference.

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

A limited role of NKCC1 in telencephalic glutamatergic neurons for developing hippocampal network dynamics and behavior.

NKCC1 is the primary transporter mediating chloride uptake in immature principal neurons, but its role in the development of in vivo network dynamics and cognitive abilities remains unknown. Here, we address the function of NKCC1 in developing mice using electrophysiological, optical, and behavioral approaches. We report that NKCC1 deletion from telencephalic glutamatergic neurons decreases in vitro excitatory actions of γ-aminobutyric acid (GABA) and impairs neuronal synchrony in neonatal hippocampal brain slices. In vivo, it has a minor impact on correlated spontaneous activity in the hippocampus and does not affect network activity in the intact visual cortex. Moreover, long-term effects of the developmental NKCC1 deletion on synaptic maturation, network dynamics, and behavioral performance are subtle. Our data reveal a neural network function of NKCC1 in hippocampal glutamatergic neurons in vivo, but challenge the hypothesis that NKCC1 is essential for major aspects of hippocampal development.

Dispositional individual differences in cognitive effort investment: establishing the core construct.

BACKGROUND: Individuals tend to avoid effortful tasks, regardless of whether they are physical or mental in nature. Recent experimental evidence is suggestive of individual differences in the dispositional willingness to invest cognitive effort in goal-directed behavior. The traits need for cognition (NFC) and self-control are related to behavioral measures of cognitive effort discounting and demand avoidance, respectively. Given that these traits are only moderately related, the question arises whether they reflect a common core factor underlying cognitive effort investment. If so, the common core of both traits might be related to behavioral measures of effort discounting in a more systematic fashion. To address this question, we aimed at specifying a core construct of cognitive effort investment that reflects dispositional differences in the willingness and tendency to exert effortful control. METHODS: We conducted two studies (N = 613 and N = 244) with questionnaires related to cognitive motivation and effort investment including assessment of NFC, intellect, self-control and effortful control. We first calculated Pearson correlations followed by two mediation models regarding intellect and its separate aspects, seek and conquer, as mediators. Next, we performed a confirmatory factor analysis of a hierarchical model of cognitive effort investment as second-order latent variable. First-order latent variables were cognitive motivation reflecting NFC and intellect, and effortful self-control reflecting self-control and effortful control. Finally, we calculated Pearson correlations between factor scores of the latent variables and general self-efficacy as well as traits of the Five Factor Model of Personality for validation purposes. RESULTS: Our findings support the hypothesized correlations between the assessed traits, where the relationship of NFC and self-control is specifically mediated via goal-directedness. We established and replicated a hierarchical factor model of cognitive motivation and effortful self-control that explains the shared variance of the first-order factors by a second-order factor of cognitive effort investment. CONCLUSIONS: Taken together, our results integrate disparate literatures on cognitive motivation and self-control and provide a basis for further experimental research on the role of dispositional individual differences in goal-directed behavior and cost-benefit-models.

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales.

Cognitive control is typically understood as a set of mechanisms that enable humans to reach goals that require integrating the consequences of actions over longer time scales. Importantly, using routine behaviour or making choices beneficial only at short time scales would prevent one from attaining these goals. During the past two decades, researchers have proposed various computational cognitive models that successfully account for behaviour related to cognitive control in a wide range of laboratory tasks. As humans operate in a dynamic and uncertain environment, making elaborate plans and integrating experience over multiple time scales is computationally expensive. Importantly, it remains poorly understood how uncertain consequences at different time scales are integrated into adaptive decisions. Here, we pursue the idea that cognitive control can be cast as active inference over a hierarchy of time scales, where inference, i.e., planning, at higher levels of the hierarchy controls inference at lower levels. We introduce the novel concept of meta-control states, which link higher-level beliefs with lower-level policy inference. Specifically, we conceptualize cognitive control as inference over these meta-control states, where solutions to cognitive control dilemmas emerge through surprisal minimisation at different hierarchy levels. We illustrate this concept using the exploration-exploitation dilemma based on a variant of a restless multi-armed bandit task. We demonstrate that beliefs about contexts and meta-control states at a higher level dynamically modulate the balance of exploration and exploitation at the lower level of a single action. Finally, we discuss the generalisation of this meta-control concept to other control dilemmas.

Stochastic Motion Stimuli Influence Perceptual Choices in Human Participants.

In the study of perceptual decision making, it has been widely assumed that random fluctuations of motion stimuli are irrelevant for a participant's choice. Recently, evidence was presented that these random fluctuations have a measurable effect on the relationship between neuronal and behavioral variability, the so-called choice probability. Here, we test, in a behavioral experiment, whether stochastic motion stimuli influence the choices of human participants. Our results show that for specific stochastic motion stimuli, participants indeed make biased choices, where the bias is consistent over participants. Using a computational model, we show that this consistent choice bias is caused by subtle motion information contained in the motion noise. We discuss the implications of this finding for future studies of perceptual decision making. Specifically, we suggest that future experiments should be complemented with a stimulus-informed modeling approach to control for the effects of apparent decision evidence in random stimuli.

Abstract rules drive adaptation in the subcortical sensory pathway.

The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The long-standing mechanistic explanation for this adaptive behaviour is that neural activity decreases with increasing regularities in the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here, we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that abstract expectations can drive the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of abstract processing in a subcortical sensory pathway. They indicate that the neural representation of the outside world is altered by our prior beliefs even at initial points of the processing hierarchy.

Dispositional cognitive effort investment and behavioral demand avoidance: Are they related?

Individuals tend to avoid cognitive demand, yet, individual differences appear to exist. Recent evidence from two studies suggests that individuals high in the personality traits Self-Control and Need for Cognition that are related to the broader construct Cognitive Effort Investment are less prone to avoid cognitive demand and show less effort discounting. These findings suggest that cost-benefit models of decision-making that integrate the costs due to effort should consider individual differences in the willingness to exert mental effort. However, to date, there are almost no replication attempts of the above findings. For the present conceptual replication, we concentrated on the avoidance of cognitive demand and used a longitudinal design and latent state-trait modeling. This approach enabled us to separate the trait-specific variance in our measures of Cognitive Effort Investment and Demand Avoidance that is due to stable, individual differences from the variance that is due to the measurement occasion, the methods used, and measurement error. Doing so allowed us to test the assumption that self-reported Cognitive Effort Investment is related to behavioral Demand Avoidance more directly by relating their trait-like features to each other. In a sample of N = 217 participants, we observed both self-reported Cognitive Effort Investment and behavioral Demand Avoidance to exhibit considerable portions of trait variance. However, these trait variances were not significantly related to each other. Thus, our results call into question previous findings of a relationship between self-reported effort investment and demand avoidance. We suggest that novel paradigms are needed to emulate real-world effortful situations and enable better mapping between self-reported measures and behavioral markers of the willingness to exert cognitive effort.

Modeling Dynamic Allocation of Effort in a Sequential Task Using Discounting Models.

Most rewards in our lives require effort to obtain them. It is known that effort is seen by humans as carrying an intrinsic disutility which devalues the obtainable reward. Established models for effort discounting account for this by using participant-specific discounting parameters inferred from experiments. These parameters offer only a static glance into the bigger picture of effort exertion. The mechanism underlying the dynamic changes in a participant's willingness to exert effort is still unclear and an active topic of research. Here, we modeled dynamic effort exertion as a consequence of effort- and probability-discounting mechanisms during goal reaching, sequential behavior. To do this, we developed a novel sequential decision-making task in which participants made binary choices to reach a minimum number of points. Importantly, the time points and circumstances of effort allocation were decided by participants according to their own preferences and not imposed directly by the task. Using the computational model to analyze participants' choices, we show that the dynamics of effort exertion arise from a combination of changing task needs and forward planning. In other words, the interplay between a participant's inferred discounting parameters is sufficient to explain the dynamic allocation of effort during goal reaching. Using formal model comparison, we also inferred the forward-planning strategy used by participants. The model allowed us to characterize a participant's effort exertion in terms of only a few parameters. Moreover, the model can be adapted to a number of tasks used in establishing the neural underpinnings of forward-planning behavior and meta-control, allowing for the characterization of behavior in terms of model parameters.

Representation of Perceptual Evidence in the Human Brain Assessed by Fast, Within-Trial Dynamic Stimuli.

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. Another recently established approach to investigate the representation of decision evidence in brain signals is to correlate the within-trial fluctuations of decision evidence with the measured signals. We here report results of this approach for a two-alternative forced choice reaction time experiment measured using magnetoencephalography (MEG) recordings. Our results show: (1) that decision evidence is most strongly represented in the MEG signals in three consecutive phases and (2) that posterior cingulate cortex is involved most consistently, among all brain areas, in all three of the identified phases. As most previous work on perceptual decision making in the brain has focused on parietal and motor areas, our findings therefore suggest that the role of the posterior cingulate cortex in perceptual decision making may be currently underestimated.