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Latest NO BS science

This post will include excerpts of the 3 latest scientific studies, I’ve read, and my hope is to inspire you to further educate yourself, and help me spread the science of ADHD, so we can fight all the ignorance, all the hidden agendas, and all the bullshit, said about ADHD on the Internet!


This post will include excerpts of the 3 latest scientific studies, I’ve read, and my hope is to inspire you to further educate yourself, and help me spread the science of ADHD, so we can fight all the ignorance, all the hidden agendas, and all the bullshit, said about ADHD on the Internet!

This time I looked at these 3:

Attention-Deficit/Hyperactivity Disorder (ADHD) and Emotion Regulation Over the Life Span

Christiansen, H., Hirsch, O., Albrecht, B. et al. Attention-Deficit/Hyperactivity Disorder (ADHD) and Emotion Regulation Over the Life Span. Curr Psychiatry Rep 21, 17 (2019). https://doi.org/10.1007/s11920-019-1003-6

ADHD, mind wandering and the default mode network

Bozhilova, N. S., Michelini, G., Kuntsi, J., & Asherson, P. (2018). Mind wandering perspective on attention-deficit/hyperactivity disorder. Neuroscience and biobehavioral reviews92, 464–476. https://doi.org/10.1016/j.neubiorev.2018.07.010

The theory of constructed emotion

Barrett L. F. (2017). The theory of constructed emotion: an active inference account of interoception and categorization. Social cognitive and affective neuroscience12(11), 1833. https://doi.org/10.1093/scan/nsx060

Source #1

Attention-Deficit/Hyperactivity Disorder (ADHD) and Emotion Regulation Over the Life Span

Christiansen, H., Hirsch, O., Albrecht, B. et al. Attention-Deficit/Hyperactivity Disorder (ADHD) and Emotion Regulation Over the Life Span. Curr Psychiatry Rep 21, 17 (2019). https://doi.org/10.1007/s11920-019-1003-6


Emotion Regulation Deficits (ERDs)
Emotion regulation includes all processes that unfold over time and are related to the different emotions people have, the intensity of emotions, and how emotions are experienced and expressed

Emotion Regulation (ER)
Emotion regulation (ER) refers to attempts to influence emotions in ourselves or others.

Abbreviations for Emotion Regulation Deficits are often: “emotional dysregulation”, “emotional impulsiveness”, “emotional lability”.


Emotions are temporally limited, qualitative states that are associated with changes of feelings, expression, and physiology. 

They differ from stress responses and mood in several ways, but the most prominent difference is that emotions are elicited by specific internal or external activating events.

Emotions require that we direct our attention (consciously or preconsciously) to this activating event, and that we value or appraise it with regard to our goals. 

Finally, emotions promote relevant action urges (approach vs. withdrawal), physiological activation (central and peripheral), and expressive behaviors. 

Thus, emotions unfold over time and are helpful when they appropriately guide sensory processing, enhance decision making, or provide information regarding the best course of action. 

Emotions are potentially harmful when they are of inappropriate intensity, duration, frequency, or type for a particular situation. 

The fact that emotions unfold over time also implies that each emotion is characterized by dynamic features, such as latency of onset, rise gradient, maximal intensity of the response, duration or persistence of an emotion, and slope of recovery. 

Across response levels, those temporal features may differ, and they may also differ among emotions.

When seeking a definition of emotion regulation, it is more or less consensual that ER refers to all efforts to influence the emotions we have, when we have them, and how we experience and express them. 

As emotions have a strong temporal dimension, regulatory dynamic processes — no matter whether automatic or voluntarily used — interact to influence our emotional states flexibly so as to promote adaptive, goal-oriented behaviors.

Gross et al., 2020

ER strategies vary greatly (e.g., putting on a poker face, applying mediative breathing, taking a shower, trying to think differently about a situation), and they are traditionally classified into those that occur prior to the emotional response (antecedent-focused regulation; e.g., situation selection, situation modification, attentional deployment, and cognitive change) and regulation strategies that occur after the emotional response is already triggered (response-focused regulation; e.g., expressive suppression; see Fig. 1). 

The strategies also differ in their effectiveness to modify emotional outcomes as indexed by experiential, behavioral, and physiological measures.

For example, active thinking about a positive event that is unrelated to the evocative situation is an effective distraction strategy across all response levels. Cognitive change strategies such as perspective taking are effective in modifying experiential and behavioral outcomes, and response modulation may change behavior in the intended direction, while physiological states are not necessarily related to such strategies.

However, temporal dynamics have been spotlighted in recent theoretical debates. 

According to the extended model of emotion regulation by Gross (see Fig. 1 for details), more dynamic processes or regulatory stages have been introduced. 

To gain control of emotions, we enter a cyclical process whereby we identify the emotion needing to be regulated (identification), select (selection), and then execute an ER strategy (implementation).  Finally, we monitor the regulatory effects so as to decide whether regulation was sufficient or not (stopping or maintaining regulatory engagement) or whether we should switch to another strategy (monitoring).

Neurocognitive Correlates of ER in ADHD

ERD in ADHD is often hypothesized to result from the same or at least overlapping neurocognitive deficits in inhibition, working memory, and executive functions as core symptoms do. 

  • deficits in ER are an integral part of adult ADHD
  • presence of comorbidity did not significantly account for ERD

Neurophysiology and ER in ADHD

A brief review of underlying brain activity may be useful to differentiate emotion generation (bottom-up processes arising from related brain areas as amygdala, ventral striatum, and somatosensory areas) from emotion regulation (top-down processes associated with neuronal activity in executive neural networks including the central role of the prefrontal cortex).

Bottom-Up Emotion Generation

Two different paradigms in neuroscience are regularly used to study emotion generation: (1) functional MRI that confronts patients with affectively charged stimuli and (2) associations of structural MRI or resting state connectivity with trait measures of emotional reactivity.

Superficial amygdala regions are connected to the OFC areas associated with emotional outcome evaluation. These detected circuits may form the core of an emotion network that is dysregulated in ADHD. 

The amygdala may also modulate the processing of fearful compared to neutral and joyful stimuli and may initiate visual attention orientation when processing fearful ones by establishing functional connectivity between the superficial amygdala, the visual cortex, and the superior parietal cortex. 

Further, abnormally increased amygdala activity (and hypo-responsiveness in the ventral striatum) was evoked when anticipated reward was delayed, in a study with adults with ADHD. 

  • As such, activity in the amygdala may serve as an important bottom-up processing hub that is probably impaired in ADHD.

Top-Down Regulation

A recent meta-analysis on human neuroimaging studies divides top-down processes into three parts: 

(1) a common core system for self-regulation, encompassing the inferior frontal gyrus, the medial frontal cortex (MFC) with the anterior cingulate cortex (ACC), the supplementary motor area (SMA), and the right temporoparietal junction (TPJ); 

(2) a specific action regulation or response inhibition system (e.g., dorsal premotor cortices, intra-parietal sulci, thalamus); and 

(3) an ER system (e.g., more posterior parts of the TPJ and MFC). 

A number of studies revealed difficulties in patients with ADHD within the former two regulation systems; however, there are very few studies that elucidate emotional regulatory processes specifically.

Core Systems for General Self-Regulation and Action Regulation

It is well known that patients with ADHD show deficits in cognitive control, an important aspect of executive functions. 

Such deficits have been demonstrated with a wide range of tasks such as tapping interference control (Stroop-tests) or inhibition of (prepotent) responses (Go/NoGo and Stop tasks). 

Deficits typically appear as slower and more error-prone responses and as abnormal activity in brain regions associated with cognitive control. 

Patients with ADHD showed diminished brain activity during cognitive control in the dorsolateral prefrontal cortex (DLPFC), the ACC, and (pre-)SMA in studies using functional magnetic resonance imaging (fMRI) and in others using brain electrical activity. 

Moreover, error processing seems to be blunted in ADHD. 

For example, early error negativity assumed to indicate response conflict between required response and reinforcement learning mechanism (main sources: ACC, DLPFC) is less pronounced in ADHD compared to healthy controls.

Interestingly, there is some evidence that performance in cognitive tasks and associated brain activity impairments may be ameliorated by either immediate rewards or ADHD medication. 

Thus, it seems plausible to assume that even the “cold” self-regulation and action regulation systems are modulated by emotional bottom-up input. 

From an experimental psychology point of view, it seems indispensable to distinguish those emotionally colored cognitive control and action control processes from emotion regulation processes.

Psychophysiology and ER in ADHD

Along with the central nervous system (CNS), the autonomic nervous system (ANS) is highly relevant for emotions and their regulation. 

While emotion reactivity has been associated with sympathetic alterations, ER seems to be more closely related to parasympathetic regulation.

As hypothesized by the neurovisceral integration theory, accumulating evidence suggests that measures of vagally mediated heart rate variability reflect PFC function and hence CNS substrates of ER. 

This is corroborated by evidence demonstrating that resting state vagal tone, indexed by high-frequency heart rate variability (HF-HRV), predicts sustained attention, task engagement, and ER over the life span. 

A recent meta-analysis reports a small, but relevant association between markers of self-control in laboratory tasks and vagally mediated resting state heart rate (r = .15). 

Finally, greater task-induced withdrawal in vagal tone has been associated with better attention, social behavior, impulse control, and fewer externalizing and internalizing problems among children.

NOTEVagal tone refers to activity of the vagus nerve, the 10th cranial nerve and a  fundamental component of the parasympathetic branch of the  autonomic nervous system. This branch of the nervous system is not  under conscious control and is largely  responsible for the regulation of  several body compartments at rest. Vagal activity results in various  effects, including: heart rate reduction, vasodilation/constriction of  vessels, glandular activity in the heartlungs, and digestive tract, liver,  immune system  regulation as well as control of gastrointestinal  sensitivity, motility and inflammation.

However, the aforementioned extended process-oriented framework by Gross specifies iterative regulatory stages, regulatory decisions, and potential failure points related to 

(1) whether or not to regulate (identification), 

(2) when to select a regulatory category and specific regulation tactic to use, 

(3) then how to execute this strategy with the aim of changing the emotion or its generation, and 

(4) how to monitor the regulatory effects that tell us whether ER is sufficient or not (stopping or maintaining regulatory engagement) or whether we should switch to another strategy. 

Those challenges and potential points of failure might differ among individual patients with ADHD and may suggest specific alternative treatments for different subgroups. 

Such disparities may explain why one treatment, e.g., pharmacological interventions, has limited effects on specific emotional symptoms.

In sum, the process model allows for studying ER in real time, to specifically track emotions and the associations among emotions, ER, and psychopathology. 

Beyond general self-regulation problems, ERDs are thus viewed as specific difficulties or configurations of difficulties arising from separate levels of emotion processing (e.g., difficulties due to a deficient set of strategies, rigidity in using only a small set of strategies, identification problems (when to regulate which emotion in a specific context), problems with implementing the strategy). 

This could reveal valuable information about treatment options in order to tailor personalized strategies to improve ER.

However, to this end, we need more sophisticated experiments and research targeting the temporal dynamics of emotion and ER using multilevel approaches and techniques with high temporal resolution.

Source #2

ADHD, mind wandering and the default mode network

Bozhilova, N. S., Michelini, G., Kuntsi, J., & Asherson, P. (2018). Mind wandering perspective on attention-deficit/hyperactivity disorder. Neuroscience and biobehavioral reviews92, 464–476. https://doi.org/10.1016/j.neubiorev.2018.07.010

What is mind wandering?

Mind wandering (MW) occurs when one’s mind drifts away from the primary task and focuses on internal, task-unrelated thoughts and images. MW is a universal experience that represents up to 50% of daily thinking time (Smallwood & Schooler, 2015). While some forms of MW can be beneficial to individuals (e.g. strategic thinking about a grant proposal while driving a car), other forms can be detrimental (e.g. spontaneous uncontrolled thoughts that interfere with tasks such as listening to a lecture). These two types of MW have been referred to as deliberate and spontaneous, respectively, and are thought to reflect a different balance of regulatory processes on internal self-generated thought (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016; Seli, Cheyne, Xu, Purdon, & Smilek, 2015). Spontaneous MW, detrimental to performance, has been proposed as a mechanism explaining many of the symptoms and impairments of ADHD (Mowlem et al., 2016; Seli et al., 2015) believed to reflect dysfunctional connectivity between the brain’s default mode network (DMN) and executive control networks (Fox, Spreng, Ellamil, Andrews-Hanna, & Christoff, 2015; Sripada, Kessler, & Angstadt, 2014).

Spontaneous mind wandering is associated with ADHD

The first study of MW in ADHD was conducted using an experience sampling technique to

measure on-task and off-task thoughts during a simple attention task (Shaw & Giambra,

1993).The frequency of task-unrelated thoughts was found to be increased in college students with a childhood history of ADHD diagnosis, compared to controls. Among the controls, male and female groups that reported high levels of childhood ADHD symptoms also demonstrated more task-unrelated thoughts than controls reporting low levels of childhood ADHD symptoms.

Perceptual Decoupling in MW

A key characteristic of MW is the association with attenuated somatosensory processing, referred to as perceptual decoupling

This means that during periods of MW there is a reduced somatosensory response to sensory stimuli (Schooler et al., 2011; Smallwood, Ruby, & Singer, 2013). 

One hypothesis is that perceptual decoupling explains the co-activation of the FPN and DMN, during low demand conditions; reflecting active executive control over attention to disengage from perceptual input, so as to enable mental processing of personal goals (Smallwood, Brown, Baird, & Schooler, 2012).

In line with this, MW has been linked to anti-correlation and lack of synchronisation between sensory cortices and DMN (Christoff, 2012; Kirschner, Kam, Handy, & Ward, 2012) and a positive correlation between sensory cortices and FPN hubs during on-task conditions. 

A novel concept suggests that the depth of perceptual decoupling might be able to distinguish between spontaneous and deliberate MW (Seli et al., 2015). 

EEG research has consistently reported that event-related potential (ERP) components (P1), markers of early visual information processing within 100 ms are attenuated during periods of task-unrelated compared to task-related thoughts (Baird, Smallwood, Lutz, & Schooler, 2014; Broadway, Franklin, & Schooler, 2015; Kam & Handy, 2014). 

Phase-locking factor analyses reflect phase synchrony of a particular frequency at a particular time across multiple trials of an event (Tallon-Baudry, Bertrand, Delpuech, & Pernier, 1996). 

Episodes of MW were further linked to diminished theta phase synchronisation within 50-150ms following a visual stimulus confirming a neural state of perceptual decoupling during MW (Baird et al., 2014).

Cortical source activity analyses during a visual Sustained Attention to Response Task (SART) also confirm the attenuation of early visual information processing, during periods of MW (Kirschner et al., 2012). 

In this study, there was also deficient intra-regional (occipital cortex) and inter-regional (visual cortex and right medial temporal lobe) connectivity when attention was focused on internal thoughts (Kirschner et al., 2012). 

In contrast, during periods when the participants were focused on the visual task, there was greater inter-regional connectivity between the visual cortex and task-positive regionsincluding the anterior/posterior cingulate, orbitofrontal cortex and posterior parietal gyrus. 

Similarly, MW was linked to both an increase in occipital and parieto-central theta and fronto-central delta power and a decrease in occipital alpha and frontal lateral beta power (Braboszcz & Delorme, 2011).

Collectively, these findings suggest a switch from active cognitive processing to MW, which is facilitated by a state of perceptual decoupling, or detachment of perception from attention.

Consequently, a reduced P1 amplitude is regarded as a marker of perceptual decoupling during episodes of MW.

Perceptual decoupling in ADHD

Somatosensory responses are far less studied in ADHD, and the association between sensory decoupling and ADHD is not well established. Furthermore, there have been no studies that directly investigate the relationship of perceptual decoupling to periods of MW in ADHD. 

Yet, the few studies that have focused on early sensory processing in ADHD find deficits that are similar to those seen during periods of MW in neurotypical controls. 

Initial reports found decreased slow frequency fluctuations within the left sensorimotor cortex (Yu-Feng et al., 2007) and suppression of visual event-related potential amplitudes during cognitive-performance tasks in children with ADHD compared to controls (Steger, Imhof, Steinhausen, & Brandeis, 2000). 

Using magnetoencephalography (MEG), adults with ADHD showed reduced event-related desynchronization in the alpha band, and synchronisation in the beta bands, in primary and secondary somatosensory cortices in response to median nerve stimulation (Dockstader et al., 2008). 

A similar attenuated cortical sensory response was found in children with ADHD, which improved following successful treatment with methylphenidate (Lee et al., 2005).

Using ERP, a larger P1 (100 ms post-stimulus) amplitude has been seen in children with ADHD compared to controls; a finding that was interpreted as a compensatory mechanism in the absence of performance differences(Kóbor et al., 2015; Shahaf et al., 2012). 

In contrast, when children with ADHD made more omission errors than controls, P1 amplitude was significantly reduced (Nazari et al., 2010). 

At the time of writing, preliminary findings from our group support this result. Using the SART, we found reduced P1 amplitude in 33 adults with ADHD compared to 30 controls (p<0.02), which was associated with trait measures of inattention, and MW measured using the MEWS as a state measure of excessive MW in ADHD (Bozhilova et al., unpublished data). 

We further found that in the ADHD cases there was a reduced P1 amplitude prior to errors compared to correct responses(p<.001). Under the assumption that MW will be higher prior to error than non-error responses, these findings suggest that somatosensory processing deficits could be linked to excessive MW in ADHD. This hypothesis has yet to be formally tested. 

Mind wandering and “Maturational lag hypothesis” of brain development in ADHD

Related to the findings on typical functional brain development, El‐Sayed, Larsson, Persson, Santosh, & Rydelius, (2003) proposed a maturational lag hypothesis. 

The hypothesis suggests that a persistent maturational lag in functional brain development might become a sustained functional abnormality leading to symptoms and impairments of ADHD. 

We further propose that abnormalities in resting-state functional connectivity resulting from co-activation of functionally related brain regions (Power et al., 2010) may lead to the self-generation of excessive, spontaneous and context-independent thought, typical of MW, which are externalised as inattentive behaviours over the lifespan. 

Consistent with this view, recent work in ADHD has shown a maturational lag in major large-scale brain networks, especially within-network integration (DMN and FPN) and interactions between default mode, frontoparietal, ventral attention and salience networks (Sripada et al., 2014). 

A recent review also summarised findings for decreased synchrony/connectivity between the two major DMN hubs in ADHD (Castellanos & Aoki, 2016). 

The lagged maturation was associated with DMN interference, poor performance (e.g. greater reaction time variability) during cognitively demanding tasks, and was proportionate to the severity of inattention (Sripada et al. 2014).

With regard to the development of MW in ADHD, to date there has been only one published study, which compared the frequency of MW in children and adults with ADHD (Van den Driessche et al., 2017). 

Using an experience sampling method, similar frequencies of “mind blanking” (MW without awareness of the content) were seen in 6-12-year-old children and young adults with ADHD. 

While the authors found no case-control performance differences on the SART, medication-naïve children and adults reported twice as much mind blanking, but fewer episodes of task focus and MW with awareness than controls. 

We propose that individuals with ADHD in this study tended to report mind blanking rather than MW due to the lack of a coherent reportable content. 

When comparing a group of children with ADHD treated with methylphenidate, with drug naïve children and controls, there was reduced frequency of mind blanking to the level of controls; although the treated group still had a greater frequency of MW with awareness of content than controls (Van den Driessche et al., 2017). 

Medication was therefore proposed to allow access to awareness of MW. The authors (Van den Driessche et al., 2017) further hypothesised that this effect could be due to restoration of executive resources. 

These findings suggest similar abnormalities in the frequency of MW-related measures in ADHD during both childhood and young to middle adulthood.

Overall, the developmental studies suggest that understanding the cortical maturation of key networks leading to aware/unaware and spontaneous/deliberate forms of MW, may be important to understanding the onset, course and development of ADHD.

Source #3

The theory of constructed emotion

Barrett L. F. (2017). The theory of constructed emotion: an active inference account of interoception and categorization. Social cognitive and affective neuroscience12(11), 1833. https://doi.org/10.1093/scan/nsx060

What is a brain for?

A brain did not evolve for rationality, happiness or accurate perception. All brains accomplish the same core task: to efficiently ensure resources for physiological systems within an animal’s body (i.e. its internal milieu) so that an animal can grow, survive and reproduce. 

This balancing act is called ‘allostasis’. 

Growth, survival and reproduction (and therefore gene transmission) require a continual intake of metabolic and other biological resources. 

Metabolic and other expenditures are required to plan and execute the physical movements necessary to acquire those resources in the first place (and to protect against threats and dangers).

Allostasis is not a condition of the body, but a process for how the brain regulates the body according to costs and benefits; ‘efficiency’ requires the ability to anticipate the body’s needs and satisfy them before they arise.

How does a brain perform allostasis?

For a brain to effectively regulate its body in the world, it runs an internal model of that body in the world. In psychology, we refer to this modeling as ‘embodied simulation’.

As an animal’s integrated physiological state changes constantly throughout the day, its immediate past determines the aspects of the sensory world that concern the animal in the present, which in turn influences what its niche will contain in the immediate future.

Ample evidence shows that ongoing brain activity influences how the brain processes incoming sensory information, and that neurons fire intrinsically within large networks without any need for external stimuli.

The implications of these insights are profound: namely, it is very unlikely that perception, cognition, and emotion are localized in dedicated brain systems, with perception triggering emotions that battle with cognition to control behavior.

Without an internal model, the brain cannot transform flashes of light into sights, chemicals into smells and variable air pressure into music. You’d be experientially blind. Thus, simulations are a vital ingredient to guide action and construct perceptions in the present.

They are embodied, whole brain representations that anticipate (i) upcoming sensory events both inside the body and out as well as (ii) the best action to deal with the impending sensory events. Their consequence for allostasis is made available in consciousness as affect.

Prediction Coding, Prediction Errors, and Simulation

I hypothesize that, using past experience as a guide, the brain prepares multiple competing simulations that answer the question, ‘what is this new sensory input most similar to?’. 

Similarity is computed with reference to the current sensory array and the associated energy costs and potential rewards for the body. 

That is, simulation is a partially completed pattern that can classify (categorize) sensory signals to guide action in the service of allostasis. 

Each simulation has an associated action plan.

Using Bayesian logic, a brain uses pattern completion to decide among simulations and implement one of them, based on predicted maintenance of physiological efficiency across multiple body systems (e.g. need for glucose, oxygen, salt etc.).

From this perspective, unanticipated information from the world (prediction error) functions as feedback for embodied simulations (also known as ‘bottom-up’ or, confusingly, ‘feedforward’ signals). 

Error signals track the difference between the predicted sensations and those that are incoming from the sensory world (including the body’s internal milieu). 

Once these errors are minimized, simulations also serve as inferences about the causes of sensory events and plans for how to move the body (or not) to deal with them. By modulating ongoing motor and visceromotor actions to deal with upcoming sensory events, a brain infers their likely causes.

In predictive coding, as we will see, sensory predictions arise from motor predictions; simulations arise as a function of visceromotor predictions (to control your autonomic nervous system, your neuroendocrine system, and your immune system) and voluntary motor predictions, which together anticipate and prepare for the actions that will be required in a moment from now. 

These observations reinforce the idea that the stimulus!response model of the mind is incorrect.

For a given event, perception follows (and is dependent on) action, not the other way around. Therefore, all classical theories of emotion are called into question, even those that explain emotion as iterative stimulus !response sequences.

The computational architecture of the brain is a conceptual system plus pattern generators.

The mechanistic details of predictive coding provide yet another deep insight: a brain implements its internal model with ‘concepts’ that ‘categorize’ sensations to give them meaning.

Predictions are concepts (see Figure 4). Completed predictions are categorizations that maintain physiological regulation, guide action and construct perception.

The meaning of a sensory event includes visceromotor and motor action plans to deal with that event. As detailed in Figure 5, meaning does not trigger action, but results from it. This makes classical appraisal theories highly doubtful, because they assume that a response derives from a stimulus that is evaluated for its meaning. Appraisals as descriptions of the world, however, are produced by categorization with concepts.

Traditionally, a ‘category’ is a population of events or objects that are treated as similar because they all serve a particular goal in some context; a ‘concept’ is the population of representations that correspond to those events or objects.

I hypothesize that in assembling populations of predictions, each one having some probability of being the best fit to the current circumstances (i.e., Bayesian priors), the brain is constructing concepts, or what Barsalou refers to as ‘ad hoc’ concepts.

In the language of the brain, a concept is a group of distributed ‘patterns’ of activity across some population of neurons. 

Incoming sensory evidence, as prediction error, helps to select from or modify this distribution of predictions, because certain simulations will better fit the sensory array (i.e. they will have stronger priors), with the end result that incoming sensory events are categorized as similar to some set of past experiences. 

This, in effect, is the original formulation of the conceptual act theory of emotion: the brain uses emotion concepts to categorize sensations to construct an instance of emotion.

That is, the brain constructs meaning by correctly anticipating (predicting and adjusting to) incoming sensations. 

Sensations are categorized so that they are (i) actionable in a situated way and therefore (ii) meaningful, based on past experience. 

When past experiences of emotion (e.g. happiness) are used to categorize the predicted sensory array and guide action, then one experiences or perceives that emotion (happiness).

In other words, an instance of emotion is constructed the same way that all other perceptions are constructed, using the same well-validated neuroanatomical principles for information flow within the brain.

Accordingly, all action and perception are created with concepts. 

All concepts contribute to allostasis and represent changes in affect, not just those that construct the events that feel affectively intense or are created with emotion concepts.

An internal model is predictive, not reactive

An increasingly popular hypothesis is that the brain’s simulations function as Bayesian filters for incoming sensory input, driving action and constructing perception and other psychological phenomena, including emotion. 

Simulations are thought to function as prediction signals (also known as ‘top-down’ or ‘feedback’ signals, and more recently as ‘forward’ models) that continuously anticipate events in the sensory environment.

This hypothesis is variously called predictive coding, active inference, or belief propagation.

Prediction errors also arise within the amygdala, the basal ganglia, and the cerebellum and are forwarded to the cortex to correct its internal model.

I hypothesize that information from the amygdala to the cortex is not ‘emotional’ per se, but signals uncertainty about the predicted sensory input (via the basolateral complex) and helps to adjust allostasis (via the central nucleus) as a result.

The arousal signals that are associated with increases in amygdala activity can be considered a learning signal.

Similarly, prediction errors from the ventral striatum to the cortex (referred to as ‘reward prediction errors; Schultz, 2016) convey information about sensory inputs that impact allostasis more than expected (i.e. that this information should be encoded and consolidated in the cortex, and acted upon in the moment).

Dopamine is associated with engaging in vigorous action and learning that is necessary to achieve the rewards that maintain efficient allostasis (or restore it in the event of disruption), rather than playing a necessary or sufficient role in rewards themselves. Other neuromodulators, such as opioids, seem to be more intrinsic to reward in that regard.


A brain implements an internal model of the world with concepts because it is metabolically efficient to do so. Even before birth, a brain begins to build its internal model by processing prediction error from the body and the world

Emotions are constructions of the world, not reactions to it.

Lisa L.F. Barrett (2017)

This insight is a game changer for the science of emotion. It dissolves many of the debates that remained mired in philosophical confusion, and allows us to better understand the value of non-human animal models, without resorting to the perils of essentialism and anthropomorphism. 

It provides a common framework for understanding mental, physical, and neurodegenerative disorders, and collapses the artificial boundaries between cognitive, affective, and social neurosciences.

Ultimately, the theory of constructed emotion equips scientists with new conceptual tools to solve the age-old mysteries of how a human nervous system creates a human mind.


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