Different population dynamics in the supplementary motor area and motor cortex during reaching

Neural populations perform computations through their collective activity. Different computations likely require different population-level dynamics. We leverage this assumption to examine neural responses recorded from the supplementary motor area (SMA) and motor cortex. During visually guided reaching, the respective roles of these areas remain unclear; neurons in both areas exhibit preparation-related activity and complex patterns of movement-related activity. To explore population dynamics, we employ a novel “hypothesis-guided” dimensionality reduction approach. This approach reveals commonalities but also stark differences: linear population dynamics, dominated by rotations, are prominent in motor cortex but largely absent in SMA. In motor cortex, the observed dynamics produce patterns resembling muscle activity. Conversely, the non-rotational patterns in SMA co-vary with cues regarding when movement should be initiated. Thus, while SMA and motor cortex display superficially similar single-neuron responses during visually guided reaching, their different population dynamics indicate they are likely performing quite different computations

Posterior parietal cortex guides visual decisions in rats

Neurons in putative decision-making structures can reflect both sensory and decision signals, making their causal role in decisions unclear. Here, we tested whether rat posterior parietal cortex (PPC) is causal for processing visual sensory signals or instead for accumulating evidence for decision alternatives. We optogenetically disrupted PPC activity during decision-making and compared effects on decisions guided by auditory vs. visual evidence. Deficits were largely restricted to visual decisions. To further test for visual dominance in PPC, we evaluated electrophysiological responses following individual sensory events and observed much larger response modulation following visual stimuli than auditory stimuli. Finally, we measured trial-to-trial spike count variability during stimulus presentation and decision formation. Variability sharply decreased, suggesting the network is stabilized by inputs, unlike what would be expected if sensory signals were locally accumulated. Our findings argue that PPC plays a causal role in processing visual signals that are accumulated elsewhere.SIGNIFICANCE STATEMENTDefining the neural circuits that support decision-making bridges a gap between our understanding of simple sensorimotor reflexes and our understanding of truly complex behavior. However, identifying brain areas which play a causal role in decision-making has proved challenging. We tested the causal role of a candidate component of decision circuits, the rat posterior parietal cortex (PPC). Our interpretation of the data benefitted from our use of animals trained to make decisions guided by either visual or auditory evidence. Our results argue that PPC plays a causal role specifically in visual decision-making, and that PPC may support sensory aspects of the decision, such as interpreting the visual signals so that evidence for a decision can be accumulated elsewhere

The Explication Defence of Arguments from Reference

In a number of influential papers, Machery, Mallon, Nichols and Stich have presented a powerful critique of so-called arguments from reference, arguments that assume that a particular theory of reference is correct in order to establish a substantive conclusion. The critique is that, due to cross-cultural variation in semantic intuitions supposedly undermining the standard methodology for theorising about reference, the assumption that a theory of reference is correct is unjustified. I argue that the many extant responses to Machery et al.’s critique do little for the proponent of an argument from reference, as they do not show how to justify the problematic assumption. I then argue that it can in principle be justified by an appeal to Carnapian explication. I show how to apply the explication defence to arguments from reference given by Andreasen (for the biological reality of race) and by Churchland (against the existence of beliefs and desires)

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Reorganization between preparatory and movement population responses in motor cortex

Neural populations can change the computation they perform on very short timescales. Although such flexibility is common, the underlying computational strategies at the population level remain unknown. To address this gap, we examined population responses in motor cortex during reach preparation and movement. We found that there exist exclusive and orthogonal population-level subspaces dedicated to preparatory and movement computations. This orthogonality yielded a reorganization in response correlations: the set of neurons with shared response properties changed completely between preparation and movement. Thus, the same neural population acts, at different times, as two separate circuits with very different properties. This finding is not predicted by existing motor cortical models, which predict overlapping preparation-related and movement-related subspaces. Despite orthogonality, responses in the preparatory subspace were lawfully related to subsequent responses in the movement subspace. These results reveal a population-level strategy for performing separate but linked computations

Agent-based Social Psychology: from Neurocognitive Processes to Social Data

Moral Foundation Theory states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations. The use of functional imaging techniques has revealed a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information that is correlated to political affiliation. Here we characterize the collective behavior of an agent-based model whose inter individual interactions due to information exchange in the form of opinions are in qualitative agreement with experimental neuroscience data. The main conclusion derived connects the existence of diversity in the cognitive strategies and statistics of the sets of moral foundations and suggests that this connection arises from interactions between agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data on conformity and learning processes, presents statistical signatures consistent with moral judgment patterns of conservatives and liberals as obtained by survey studies of social psychology.Comment: 11 pages, 4 figures, 2 C codes, to appear in Advances in Complex System

The practical other : teleology and its development

We argue for teleology as a description of the way in which we ordinarily understand others’ intentional actions. Teleology starts from the close resemblance between the reasoning involved in understanding others’ actions and one’s own practical reasoning involved in deciding what to do. We carve out teleology’s distinctive features more sharply by comparing it to its three main competitors: theory theory, simulation theory, and rationality theory. The plausibility of teleology as our way of understanding others is underlined by developmental data in its favour

The Ontology of Intentional Agency in Light of Neurobiological Determinism: Philosophy Meets Folk Psychology

The moot point of the Western philosophical rhetoric about free will consists in examining whether the claim of authorship to intentional, deliberative actions fits into or is undermined by a one-way causal framework of determinism. Philosophers who think that reconciliation between the two is possible are known as metaphysical compatibilists. However, there are philosophers populating the other end of the spectrum, known as the metaphysical libertarians, who maintain that claim to intentional agency cannot be sustained unless it is assumed that indeterministic causal processes pervade the action-implementation apparatus employed by the agent. The metaphysical libertarians differ among themselves on the question of whether the indeterministic causal relation exists between the series of intentional states and processes, both conscious and unconscious, and the action, making claim for what has come to be known as the event-causal view, or between the agent and the action, arguing that a sort of agent causation is at work. In this paper, I have tried to propose that certain features of both event-causal and agent-causal libertarian views need to be combined in order to provide a more defendable compatibilist account accommodating deliberative actions with deterministic causation. The ‘‘agent-executed-eventcausal libertarianism’’, the account of agency I have tried to develop here, integrates certain plausible features of the two competing accounts of libertarianism turning them into a consistent whole. I hope to show in the process that the integration of these two variants of libertarianism does not challenge what some accounts of metaphysical compatibilism propose—that there exists a broader deterministic relation between the web of mental and extra-mental components constituting the agent’s dispositional system—the agent’s beliefs, desires, short-term and long-term goals based on them, the acquired social, cultural and religious beliefs, the general and immediate and situational environment in which the agent is placed, etc. on the one hand and the decisions she makes over her lifetime on the basis of these factors. While in the ‘‘Introduction’’ the philosophically assumed anomaly between deterministic causation and the intentional act of deciding has been briefly surveyed, the second section is devoted to the task of bridging the gap between compatibilism and libertarianism. The next section of the paper turns to an analysis of folk-psychological concepts and intuitions about the effects of neurochemical processes and prior mental events on the freedom of making choices. How philosophical insights can be beneficially informed by taking into consideration folk-psychological intuitions has also been discussed, thus setting up the background for such analysis. It has been suggested in the end that support for the proposed theory of intentional agency can be found in the folk-psychological intuitions, when they are taken in the right perspective

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models