Sebo Uithol

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Recent findings in monkeys suggest that action selection is based on a competition between various action options that are automatically planned by the motor system. Here we discuss data from intracranial EEG recordings in human premotor cortex during a bimanual version of the Eriksen flankers test that suggest that the same principles apply to human action decisions. Recording sites in the dorsal premotor cortex show an early but undifferentiated activation, a delayed response that depends on the experimental conditions and, finally, a movement related activation during action execution. Additionally, we found that the medial part of the premotor cortex show a significant increase in response for ipsilateral trials, suggesting a role in inhibiting the wrong response. The ventral premotor cortex seems to be involved in action execution, rather than action selection. Together these findings suggest that the human premotor cortex is part of a network that specifies, selects, and executes actions.

The idea that prior intentions make the difference between intentional and unintentional behavior is simple and intuitive. At the same time, we lack an understanding of how voluntary actions actually come about, and the unquestioned appeal to intentions as discrete causes of actions offers little if anything in the way of an answer. We cite evidence suggesting that the origin of actions varies depending on context and effector, and argue that actions emerge from a causal web in the brain, rather than a central origin of intentional action. We will speculate on how our intuitions about forming an intention prior to acting might stem from the brain’s propensity to predict upcoming events. Finally we argue that the complex and dynamic origins of voluntary action and the interplay with predictions are better studied using a dynamical systems approach.

Intention reading and action understanding have been reported in ever-younger infants. However, the notions of intention attribution and action understanding, as well as their relation to each other, are surrounded by much confusion, making it difficult to assess the meaning and value of such findings. In this paper we set out to clarify the notions of ‘action understanding’ and ‘intention attribution’, and discuss their relation. We will show that what is commonly referred to as ‘action understanding’ in fact encompasses various heterogeneous association and prediction mechanisms. In general, these forms of action understanding do not result in the attribution of an intention to an observed actor. By disentangling intention attribution from action understanding, and by exposing the latter as an umbrella notion, we provide a framework that allows for better comparison of findings from different experimental paradigms, and a much more fruitful approach to comparative questions.

Control structures based on abstract action decisions, such as intentions, prove to be highly problematic, both on conceptual and empirical grounds, and many have argued that common sense cannot be implemented this way. I will discuss an alternative control structure in which these problems can be evaded. In this model action decisions are not made ‘in abstract’, but are the result of a competition between specified action options. Since these action options are implemented as sensorimotor processes, they are built around common sense knowledge of how to perform actions and what the sensorimotor consequences of those actions will be, which is implemented in the sensorimotor cortex. I will discuss recent evidence of our lab supporting this model. Finally, I will discuss research strategies that help to avoid using common sense concepts in neuroscientific investigation.

There is increasing evidence that the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. As a consequence, I argue, projects aimed at finding the neural implementation of intentions have produced diverse and highly puzzling findings. I will argue that actions do not stem from intentions, but from a competition between action options in the form of sensorimotor loops. I will present data supporting this view, and I will discuss the consequences of such a reinterpretation of intentional action for our conception of action understanding.

Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of finding the neural implementation of intentions, and a number of areas have been posited as implementing these states. I argue, however, that adopting the folk notion of ‘intention’ or one of its philosophical descendants in neuroscientific explanations can easily lead to a misinterpretation of the data, and can negatively influence investigation into the neural correlates of intentional action. In fact, I will argue that it is likely that intentions in this guise will never be found in the brain. As a consequence, the general idea of an action hierarchy has to be rethought thoroughly. In the final part of the talk I will zoom out, and discuss the relation between folk concepts and neuroscience, and the role philosophy might play in relating them.

Intentions are generally conceived as discrete mental states that cause appropriate actions. In the last decade, a variety of attempts have been made to localize intentions in the brain, and several areas have been allocated as representing intentions. We argue, however, that it is doubtful that the notion of intention applies to any particular physical process by which the brain initiates actions. We will show that the idea of a discrete representation that causes an action is deeply incompatible with the dynamic organization of the prefrontal cortex, which is presumed to be the locus of causation and control of actions. Discrete representations can at best, we will claim, play a subsidiary, stabilizing role in action planning. This role, however, is still incompatible with the folk notion of intention. We conclude by arguing that prevalence of the folk view stems from the central role intentions play in explaining our own and others’ behavior.

In analyses of the motor system, two hierarchies are often posited : The first – the action hierarchy –is a decomposition of an action into sub-actions and sub-sub-actions. The second – the control hierarchy – is a presumed hierarchy in the neural control processes that are supposed to bring the action about. A general assumption in cognitive (neuro)science is that these two hierarchies match. I will show, however, that two different structuring principles are used to construct the two hierarchies. The action hierarchy is constructed using a part-whole relation between the elements in the hierarchy, while the control hierarchy is structured around causal relations between the elements. I will argue that these two structuring principles are, however, incompatible, and that neither structure can account for both hierarchies. As a consequence, the action hierarchy and the control hierarchy need not, and probably will not, match entirely, suggesting that matching these to hierarchies might not be a viable strategy for empirical research. In addition, an alternative principle to structure a hierarchy can be derived from models in artificial intelligence: higher-level entities are represented more stably than lower-level entities. A hierarchy based on this principle is not subject to the problems that plague the traditional accounts.

After introducing the notions of ‘representation’, ‘action’ and ‘intention’ in Chapter 1, in Chapter 2 I investigate the process of content attribution to the firing of single mirror neurons. Single cell recordings in monkeys provide strong evidence for an important role of the motor system in action understanding, but although the data acquired from single cell recordings are generally considered to be robust, several debates have shown that the interpretation of these data is far from straightforward. Chapter 2 argues that, without principled restrictions, research based on single-cell recordings allows for unlimited content attribution to single mirror neurons. A theoretical analysis of the type of processing attributed to the mirror neuron system can help formulating restrictions on what mirroring is and what cognitive functions could, in principle, be explained by a mirror mechanism. It is argued that the processing at higher levels of abstraction needs assistance of non-mirroring processes to such an extent that subsuming the processes needed to infer goals from actions under the label ‘mirroring’ is not warranted.
In humans single cell recordings are problematic. Therefore, activation of the motor areas upon action observation using fMRI or EEG is studied. The finding of this so called ‘motor resonance’ is generally regarded to be supportive for motor theories of action understanding. These theories take motor resonance to be essential in the understanding of observed actions and the inference of action goals. However, Chapter 3 shows that the notions of ‘resonance’, ‘action understanding' and ‘action goal’ appear to be used ambiguously in the literature. A survey of the literature on mirror neurons and motor resonance yields two different interpretations of the term resonance, three different interpretations of action understanding, and again three different interpretations of what the goal of an action is. This entails that, unless it is specified what interpretation is used, the meaning of any statement about the relation between these concepts can differ to a great extent. By discussing Umilta et al.’s (2001) well-known experiment on mirror neurons I show that more precise definitions and use of the concepts will allow for better assessments of motor theories of action understanding and hence a more fruitful scientific debate. Lastly, I provide an example of how the discussed experimental setup could be adapted, based on the preceding analysis, to test other interpretations of the concepts.
Actions are commonly thought of as structured hierarchically. Chapter 4 analyses such hierarchies. In the literature two hierarchies are often posited: The first—the action hierarchy—is a decomposition of an action into sub-actions and sub-sub-actions. The second—the control hierarchy—is a postulated hierarchy in the neural control processes that are supposed to bring about the action. A general assumption in cognitive neuroscience is that these two hierarchies are internally consistent and provide complementary descriptions of neuronal control processes. In this chapter I show that that neither hierarchy offers a complete explanation and that they cannot be reconciled in a logical or conceptual way. Furthermore, neither pays proper attention to the dynamics and temporal aspects of neural control processes. I explore an alternative hierarchical organization in which causality is inherent in the dynamics over time. Specifically, high levels of the hierarchy encode slower (goal-related) representations, while lower levels represent faster (action and motor acts) kinematics. If employed properly, a hierarchy based on this principle is not subject to the problems that plague the traditional accounts.
Chapter 5 analyzes the neural applicability of the notion of ‘intention’. Intentions are commonly conceived of as discrete mental states that are the direct cause of actions. In the last several decades, neuroscientists have taken up the project of localizing intentions in the brain, and a number of areas have been posited as implementing representations of intentions. I argue, however, that it is doubtful that the folk notion of ‘intention’ applies to any particular physical process by which the brain initiates actions. Drawing on the analysis of Chapter 4, Pacherie’s account of intentions (Pacherie, 2006, 2008), and Koechlin’s model on action control (Koechlin et al, 1999, 2003) I show that the idea of a discrete state that causes an action is deeply incompatible with the dynamic organization of the prefrontal cortex, the presumed neural locus of the causation and control of actions. Discrete representations can at best, I will claim, play a subsidiary, stabilizing role in action planning, but this role is still incompatible with the folk notion of intention. This chapter concludes by arguing that the prevalence of the folk notion, including its intuitive appeal in neuroscientific explanations, stems from the central role intentions play in constructing intuitive explanations of our own and others’ behavior. Some future directions based on the presented analysis are sketched below.
Finally, in Chapter 6 the ideas, results, and analyses of the previous chapters are applied to the field of developmental psychology. Intention reading and action understanding have been reported in ever-younger infants, but these findings are highly debated. In this chapter I set out to clarify the notions of ‘action understanding’ and ‘intention attribution’ and discuss their relation. I use the various forms of ‘action understanding’ from Chapter 3 and speculate on the mechanisms that could underlie these capacities. Based on Chapter 5 I argue that these forms of action understanding do not generally result in the attribution of an intention to an observed actor. By disentangling intention attribution from action understanding, and by exposing the latter as an umbrella notion, I provide a framework that allows for better comparing findings from different experimental paradigms.

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, our understanding of the way these networks are involved in intentions is still very limited. In this study, we investigate two characteristics of these processes: context- and reason-dependence of the neural states associated with intentions. We ask whether these states depend on the context a person is in and the reasons they have for choosing an action. We used a combination of functional magnetic resonance imaging (fMRI) and multivariate decoding to directly assess the context- and reason-dependency of the neural states underlying intentions. We show that action intentions can be decoded from fMRI data based on a classifier trained in the same context and with the same reason, in line with previous decoding studies. Furthermore, we found that intentions can be decoded across different reasons for choosing an action. However, decoding across different contexts was not successful. We found anecdotal to moderate evidence against context-invariant information in all regions of interest and for all conditions but one. These results suggest that the neural states associated with intentions are modulated by the context of the action.

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss the ways to infer causality in fMRI research. We also formulate recommendations for the future directions in this area. 1 What is causality? Although inferring causal relations is a fundamental aspect of scientific research, the notion of causation itself can be notoriously difficult to define. The basic idea is straightforward: When process A is the cause of process B, A is necessarily in the past from B, and without A, B would not occur. But in practice, and in dynamic systems su...

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss the ways to infer causality in fMRI research. We also formulate recommendations for the future directions in this area. 1 What is causality? Although inferring causal relations is a fundamental aspect of scientific research, the notion of causation itself can be notoriously difficult to define. The basic idea is straightforward: When process A is the cause of process B, A is necessarily in the past from B, and without A, B would not occur. But in practice, and in dynamic systems such as the brain in particular, the picture is far less clear. First, for any event a large number of (potential) causes can be identified. The efficacy of certain neuronal process in producing behavior is dependent on the state of many other (neuronal) processes, but also on the availability of glucose and oxygen in the brain, etc. In a neuroscientific context, we are generally not interested in most of these causes, but only in a cause that stands out in such a way that it is deemed to provide a substantial part of the explanation, for instance causes that vary with the experimental conditions. However, the contrast between relevant and irrelevant causes (in terms of explanatory power) is arbitrary and strongly dependent on experimental setup, contextual factors, etc. For instance, respiratory movement is typically considered a confound in fMRI experiments, unless the research question concerns the influence of respiration speed on the dynamics of the neuronal networks. In dynamic systems, causal processes are unlikely to be part of a unidirectional chain of events, but rather a causal web, with often mutual influences between process A and B. As a result, it is hard to maintain the temporal ordering of cause and effect and, indeed, a clear separation between them [119]. Furthermore, causation can never be established directly, just correlation [61]. When a correlation is highly stable, we are inclined to infer a causal link. Additional information is then needed to assess the direction of the assumed causal link, as correlation indicates 1 ar X iv :1 70 8. 04 02 0v 1 [ qbi o. Q M ] 1 4 A ug 2 01 7 for association and not for causation [1]. For example, the motor cortex is always active when a movement is made, so we assume a causal link between the two phenomena. The anatomical and physiological properties of the motor cortex, and the timing of the two phenomena provide clues about the direction of causality (i.e. cortical activity causes the movement, and not the other way around). However, only intervention studies, such as delivering Transcranial Magnetic Simulation (TMS, [75]) pulses over the motor cortex or lesion studies, can confirm the causal link between the activity in the motor cortex and behavior. Causal studies in fMRI are based on three types of correlations: correlating neuronal activity to 1) mental and behavioral phenomena; 2) physiological state (such as neurotransmitters, hormones, etc.), and 3) neuronal activity in other parts of the brain. In this review we will focus on the last field of research: establishing causal connections between two or more brain areas. fMRI studies currently use a variety of algorithms to infer causal links [35, 131]. All these methods have different assumptions, advantages and disadvantages (see for instance [141, 136]). In a seminal study by Smith et al., popular approaches to causal processes were compared using synthetic data created with a Dynamic Causal Modeling (DCM, see below) generative model [42]. Surprisingly, most of the methods struggled to perform above chance level, even though the test networks were sparse and the noise levels introduced to the model were low compared to what one would expect in real recordings. This raises the question: given the characteristics of fMRI data (low temporal resolution, slow haemodynamics, low signal-to-noise ratio; see Section 2) and the fact that causal webs in the brain are likely dense and dynamic, is it in principle possible to investigate causality in the brain using MRI? In this review, we discuss this question. First, we identify seven characteristics of models used to study causality. Then, we compare and contrast the popular approaches to the causal research in fMRI according to these criteria. Our list of features of causality is as follows: 1. Sign of connections: Can the algorithm distinguish between excitatory and inhibitory causal relations? In this context, we do not mean synaptic effects, but rather an overall…

Humans have a remarkable capacity to arrange and rearrange perceptual input according to different categorizations. This begs the question whether the categorization is exclusively a higher visual or amodal process, or whether categorization processes influence early visual areas as well. To investigate this we scanned healthy participants in a magnetic resonance imaging scanner during a conceptual decision task in which participants had to answer questions about upcoming images of animals. Early visual cortices (V1 and V2) contained information about the current visual input, about the granularity of the forthcoming categorical decision, as well as perceptual expectations about the upcoming visual stimulus. The middle temporal gyrus, the anterior temporal lobe, and the inferior frontal gyrus were also involved in the categorization process, constituting an attention and control network that modulates perceptual processing. These findings provide further evidence that early visual p...

Representations in cognitive neuroscienceExplanations in terms of representations are ubiquitous in cognitive neuroscience. In this paper I will show that the question of who is using the representation is of crucial importance, but not often explicitly answered. Two possible users, the scientist and the cognitive system are theoretically strictly distinct, but the distinction is in practice often blurred. It is tempting to jump from ‘representations to the scientist’ to ‘representations to the system’. This step, however, is unwarranted. I will show that representations to the scientist are not in themselves problematic, and can even be useful, but can lead to wrong conclusions. The problems with representations for the system are more fundamental.

Many studies have identified networks in parietal and prefrontal cortex that are involved in intentional action. Yet, knowledge about what these networks exactly encoded is still scarce. In this study we look into the content of those processes. We ask whether the neural representations of intentions are context- and reason-invariant, or whether these processes depend on the context we are in, and the reasons we have for choosing an action. We use a combination of functional magnetic resonance imaging and multivariate decoding to directly assess the context- and reason-dependency of the processes underlying intentional action. We were able to decode action decisions in the same context and for the same reasons from the fMRI data, in line with previous decoding studies. Furthermore, we could decode action decisions across different reasons for choosing an action. Importantly, though, decoding decisions across different contexts was at chance level. These results suggest that for volu...

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel’s Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

In this review, we discuss the actual and active dependence of social cognitive processes on the body, i.e., that part of the organism beyond the central nervous system. In particular, we will discuss mirror mechanisms, and assess the extent to which the body is recruited during these processes. We show that for emotion mirroring, this dependency is well-documented, but for action mirroring far less so. By reviewing these mechanisms and processes while contrasting body from brain, and social from general cognition, we show that both contrasts are arbitrary and problematic and that any study of cognitive processes, both social and general, should take the body into account.

In this review, we discuss the actual and active dependence of social cognitive processes on the body, i.e., that part of the organism beyond the central nervous system. In particular, we will discuss mirror mechanisms, and assess the extent to which the body is recruited during these processes. We show that for emotion mirroring, this dependency is well-documented, but for action mirroring far less so. By reviewing these mechanisms and processes while contrasting body from brain, and social from general cognition, we show that both contrasts are arbitrary and problematic and that any study of cognitive processes, both social and general, should take the body into account. WIREs Cogn Sci 2015, 6:453-460. doi: 10.1002/wcs.1357 For further resources related to this article, please visit the WIREs website.

In analyses of the motor system, two hierarchies are often posited: The first—the action hierarchy—is a decomposition of an action into subactions and sub-subactions. The second—the control hierarchy—is a postulated hierarchy in the neural control processes that are supposed to bring about the action. A general assumption in cognitive neuroscience is that these two hierarchies are internally consistent and provide complementary descriptions of neuronal control processes. In this article, we suggest that neither offers a complete explanation and that they cannot be reconciled in a logical or conceptually coherent way. Furthermore, neither pays proper attention to the dynamics and temporal aspects of neural control processes. We will explore an alternative hierarchical organization in which causality is inherent in the dynamics over time. Specifically, high levels of the hierarchy encode more stable (goal-related) representations, whereas lower levels represent more transient (actions...

The discovery of mirror neurons in the 1990s has led to much excitement in the cognitive neurosciences. After the initial discovery more and more abilities have been attributed to these neurons. As mirror neurons are commonly viewed as vehicles of representation, we analyze the increasingly wider representational role mirror neurons play and argue for a principled distinction between mirror and non-mirror neurons.