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To make an accurate movement, the CNS has to overcome the inherent complexities of the multijoint limb. For example, interaction torques arise when motion of individual arm segments propagates to adjacent segments causing their movement without any muscle contractions. Since these passive joint torques significantly add to the overall torques generated by active muscular contractions, they must be taken into account during planning or execution of goal-directed movements. We investigated the role of the corticospinal tract in compensating for the interaction torques during arm movements in humans. Twelve subjects reached to visual targets with their arm supported by a robotic exoskeleton. Reaching to one target was accompanied by interaction torques that assisted the movement, while reaching to the other target was accompanied by interaction torques that resisted the movement. Corticospinal excitability was assessed at different times during movement using single-pulse transcranial magnetic stimulation (TMS) over the upper-arm region of M1 (primary motor cortex). We found that TMS responses in shoulder monoarticular and elbow-shoulder biarticular muscles changed together with the interaction torques during movements in which the interaction torques were resistive. In contrast, TMS responses did not correlate with assistive interaction torques or with co-contraction. This suggests that the descending motor command includes compensation for passive limb dynamics. Furthermore, our results suggest that compensation for interaction torques involves the biarticular muscles, which span both shoulder and elbow joints and are in a biomechanically advantageous position to provide such compensation. [ABSTRACT FROM AUTHOR]

It has been proposed that whenever an animal faces several action choices, their neural representations are processed in parallel in frontoparietal cortex and compete in a manner biased by any factor relevant to the decision. We tested this hypothesis by recording single-unit activity in dorsal premotor cortex (PMd) while a monkey performed two delayed center-out reaching tasks. In the one-target task, a single target was presented and its border style indicated its reward value. The two-target task was the same except two targets were presented and the value of each was varied. During the delay period of the one-target task, directionally tuned PMd activity showed no modulation with value. In contrast, during the two-target task, the same neurons showed strong effects of the value associated with their preferred target, always in relation to the value of the other target. Furthermore, the competition between action choices was strongest when targets were furthest apart. This angular distance effect appeared in neural activity as soon as cells became tuned, while modulation by relative value appeared much later. All of these findings can be reproduced by a computational model which suggests that decisions between actions are made through a biased competition taking place within a sensorimotor map of potential actions. [ABSTRACT FROM AUTHOR]

Oxford Instruments, Superconducting Technology (OI-ST) produces Nb[sub 3]Sn wire via several "internal Sn" routes. Recently, 12 T, 4.2 K non-Cu critical current density (J[sub c]) values of ∼2900 A/mm² have been achieved by increasing the Nb and Sn fractions of the filament subelements. Similar conductors for high field use have shown engineering current density (J[sub e]) values of 170 A/mm² at 23.5 T, 1.8 K. OI-ST is also involved with research for the High Energy Physics (HEP) National Conductor Program. Results on composites made entirely by hot extrusion are described. Finally, the present status of Ta-Sn powder-in-tube (PIT) and Nb[sub 3]Al precursor strand development are presented. PIT strands have irreversibility fields over 26 T at 4.2 K, while Nb[sub 3]Al precursor strand has been produced by a route that promotes bonding of the billet components. [ABSTRACT FROM AUTHOR]

Repair of distal renal artery aneurysms poses a significant threat to kidney salvage despite improved operative techniques described over the past decades. The authors describe the case of a seventy-one-year-old woman who presented with an enlarging right renal artery aneurysm located at the renal hilum involving the lobar arteries. Operative repair was accomplished by excision of the saccular posterior wall and reconstruction with a saphenous vein patch. The ischemia time was thirty-three minutes and her postoperative course was uneventful. She was discharged home on the fifth postoperative day with normal renal function, and the renal arteriogram demonstrated a technically successful operation. They review the current literature, and this case is put into perspective with the natural history, clinical course, and present treatment of renal artery aneurysms. [ABSTRACT FROM PUBLISHER]

Humans and other animals are able to adjust their speed–accuracy trade-off (SAT) at will depending on the urge to act, favoring either cautious or hasty decision policies in different contexts. An emerging view is that SAT regulation relies on influences exerting broad changes on the motor system, tuning its activity up globally when hastiness is at premium. The present study aimed to test this hypothesis. A total of 50 participants performed a task involving choices between left and right index fingers, in which incorrect choices led either to a high or to a low penalty in 2 contexts, inciting them to emphasize either cautious or hasty policies. We applied transcranial magnetic stimulation (TMS) on multiple motor representations, eliciting motor-evoked potentials (MEPs) in 9 finger and leg muscles. MEP amplitudes allowed us to probe activity changes in the corresponding finger and leg representations, while participants were deliberating about which index to choose. Our data indicate that hastiness entails a broad amplification of motor activity, although this amplification was limited to the chosen side. On top of this effect, we identified a local suppression of motor activity, surrounding the chosen index representation. Hence, a decision policy favoring speed over accuracy appears to rely on overlapping processes producing a broad (but not global) amplification and a surround suppression of motor activity. The latter effect may help to increase the signal-to-noise ratio of the chosen representation, as supported by single-trial correlation analyses indicating a stronger differentiation of activity changes in finger representations in the hasty context. Many have argued that the regulation of the speed-accuracy tradeoff relies on an urgency signal, which implements "collapsing decision thresholds" by tuning neural activity in a global manner in decision-related structures. This study indicates that the reality is more subtle, with several aspects of "urgency" being specifically targeted to particular corticospinal populations within the motor system. [ABSTRACT FROM AUTHOR]

In this article, we challenge the usefulness of "attention" as a unitary construct and/or neural system. We point out that the concept has too many meanings to justify a single term, and that "attention" is used to refer to both the explanandum (the set of phenomena in need of explanation) and the explanans (the set of processes doing the explaining). To illustrate these points, we focus our discussion on visual selective attention. It is argued that selectivity in processing has emerged through evolution as a design feature of a complex multi-channel sensorimotor system, which generates selective phenomena of "attention" as one of many by-products. Instead of the traditional analytic approach to attention, we suggest a synthetic approach that starts with well-understood mechanisms that do not need to be dedicated to attention, and yet account for the selectivity phenomena under investigation. We conclude that what would serve scientific progress best would be to drop the term "attention" as a label for a specific functional or neural system and instead focus on behaviorally relevant selection processes and the many systems that implement them. [ABSTRACT FROM AUTHOR]

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional "decision manifold" defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution. Recent theoretical models suggest that deciding about actions and executing them are not mediated by completely distinct neural mechanisms but are instead two modes of a single system. Analysis of neural activity in several cortical and subcortical regions reveals how they implement an integrated dynamical system that governs deliberation, selection, and execution of actions. [ABSTRACT FROM AUTHOR]

In a recent report, Winkel, Keuken, van Maanen, Wagenmakers & Forstmann ( Psychonomics Bulletin and Review 21(3): 777-784, ) show that during a random-dot motion discrimination task, early differences in motion evidence can influence reaction times (RTs) and error rates in human subjects. They use this as an argument in favor of the drift-diffusion model and against the urgency-gating model. However, their implementation of the urgency-gating model is incomplete, as it lacks the low-pass filter that is necessary to deal with noisy input such as the motion signal used in their experimental task. Furthermore, by focusing analyses solely on comparison of mean RTs they overestimate how long early information influences individual trials. Here, we show that if the urgency-gating model is correctly implemented, including a low-pass filter with a 250 ms time constant, it can successfully reproduce the results of the Winkel et al. experiment. [ABSTRACT FROM AUTHOR]

• We continuously face "embodied decisions" between action possibilities or affordances. • Given their complexity, embodied decisions have been challenging to study so far. • We review novel ways to study embodied decisions in sports analytics and psychology. • We advance a novel method to formalize embodied decision variables rigorously. • Studying embodied decisions can shed light on how we make decisions "in the wild". Most current decision-making research focuses on classical economic scenarios, where choice offers are prespecified and where action dynamics play no role in the decision. However, our brains evolved to deal with different choice situations: "embodied decisions". As examples of embodied decisions, consider a lion that has to decide which gazelle to chase in the savannah or a person who has to select the next stone to jump on when crossing a river. Embodied decision settings raise novel questions, such as how people select from time-varying choice options and how they track the most relevant choice attributes; but they have long remained challenging to study empirically. Here, we summarize recent progress in the study of embodied decisions in sports analytics and experimental psychology. Furthermore, we introduce a formal methodology to identify the relevant dimensions of embodied choices (present and future affordances) and to map them into the attributes of classical economic decisions (probabilities and utilities), hence aligning them. Studying embodied decisions will greatly expand our understanding of what decision-making is. [ABSTRACT FROM AUTHOR]

• In PMd neural dynamics is shared across action execution and mere observation. • Task directional features are more accurately discriminated than action types. • Spatial aspects are encoded in PMd independently from specific behavioral demands. • PMd dynamics largely reflect higher cognitive-motor processes rather than strictly motor-related functions. Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor's motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been suggested to reflect a transformation from planning to movement execution, may rather reflect higher cognitive-motor processes, such as the covert representation of actions and goals shared across tasks that require movement and those that do not. [ABSTRACT FROM AUTHOR]

Accurate interaction with the environment relies on the integration of external information about the spatial layout of potential actions and knowledge of their costs and benefits. Previous studies have shown that when given a choice between voluntary reaching movements, humans tend to prefer actions with lower biomechanical costs. However, these studies primarily focused on decisions made before the onset of movement ("decide-then-act" scenarios), and it is not known to what extent their conclusions generalize to many real-life situations, in which decisions occur during ongoing actions ("decide-while-acting"). For example, one recent study found that biomechanical costs did not influence decisions to switch from a continuous manual tracking movement to a point-to-point movement, suggesting that biomechanical costs may be disregarded in decide-while-acting scenarios. To better understand this surprising result, we designed an experiment in which participants were faced with the decision between continuing to track a target moving along a straight path or changing paths to track a new target that gradually moved along a direction that deviated from the initial one. We manipulated tracking direction, angular deviation rate, and side of deviation, allowing us to compare scenarios where biomechanical costs favored either continuing or changing the path. Crucially, here the choice was always between two continuous tracking actions. Our results show that in this situation decisions clearly took biomechanical costs into account. Thus we conclude that biomechanics are not disregarded during decide-while-acting scenarios but rather that cost comparisons can only be made between similar types of actions. NEW & NOTEWORTHY In this study, we aim to shed light on how biomechanical factors influence decisions made during ongoing actions. Previous work suggested that decisions made during actions disregard biomechanical costs, in contrast to decisions made before movement. Our results challenge that proposal and suggest instead that the effect of biomechanical factors is dependent on the types of actions being compared (e.g., continuous tracking vs. point-to-point reaching). These findings contribute to our understanding of the dynamic interplay between biomechanical considerations and action choices during ongoing interactions with the environment.

One of the most exciting new developments in systems neuroscience is the progress being made toward neurophysiological experiments that move beyond simplified laboratory settings and address the richness of natural behavior. This is enabled by technological advances such as wireless recording in freely moving animals, automated quantification of behavior, and new methods for analyzing large data sets. Beyond new empirical methods and data, however, there is also a need for new theories and concepts to interpret that data. Such theories need to address the particular challenges of natural behavior, which often differ significantly from the scenarios studied in traditional laboratory settings. Here, we discuss some strategies for developing such novel theories and concepts and some example hypotheses being proposed., Competing Interests: Declaration of competing interest The authors declare that no competing interests exist., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI., Competing Interests: Declaration of interests K.F. holds a chief scientific adviser position at VERSES AI. The other authors declare no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

When choosing between options with multiple attributes, do we integrate all attributes into a unified measure for comparison, or does the comparison also occur at the level of each attribute, involving parallel processes that can dynamically influence each other? What happens when independent sensory features all carry information about the same decision factor, such as reward value? To investigate these questions, we asked human participants to perform a two-alternative forced choice reaching task in which the reward value of a target was indicated by two visual attributes-its brightness ("bottom-up," BU feature) and its orientation ("top-down," TD feature). If decisions always occur after the integration of both features, there should be no difference in the reaction time (RT) regardless of the attribute combinations that drove the choice. Counter to that prediction, RT distributions depended on the attribute combinations of given targets and the choices made by participants. RTs were shortest when both attributes were congruent or when the choice was based on the bottom-up feature, and longer when the attributes were in conflict (favoring opposite options). In conflict trials, nearly two-thirds of participants made faster decisions when choosing the option favored by the bottom-up feature than when choosing the top-down-favored option. We also observed mid-reach changes-of-mind in a subset of conflict trials, mostly changing from the bottom-up to the top-down-favored target. These data suggest that multi-attribute value-based decisions are better explained by a distributed process including competition among different features than by a competition based on a single, integrated estimate of value. NEW & NOTEWORTHY We show that during value-based decisions, humans do not always use all reward-related information to make their choice, but instead can "jump the gun" using partial information. In particular, when different sources of information were in conflict, early decisions were mostly based on fast bottom-up information, and sometimes followed by corrective changes-of-mind based on slower top-down information. This supports parallel decision processes among different information sources, as opposed to a single integrated "common currency."

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the opportunity cost of time, including deliberation time. Importantly, this cost can itself vary with the environmental context and is not trivial to estimate. Here, we propose how the opportunity cost of deliberation can be estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour., Competing Interests: The authors have declared that no competing interests exist.

This article proposes that biologically plausible theories of behavior can be constructed by following a method of "phylogenetic refinement," whereby they are progressively elaborated from simple to complex according to phylogenetic data on the sequence of changes that occurred over the course of evolution. It is argued that sufficient data exist to make this approach possible, and that the result can more effectively delineate the true biological categories of neurophysiological mechanisms than do approaches based on definitions of putative functions inherited from psychological traditions. As an example, the approach is used to sketch a theoretical framework of how basic feedback control of interaction with the world was elaborated during vertebrate evolution, to give rise to the functional architecture of the mammalian brain. The results provide a conceptual taxonomy of mechanisms that naturally map to neurophysiological and neuroanatomical data and that offer a context for defining putative functions that, it is argued, are better grounded in biology than are some of the traditional concepts of cognitive science.

We discuss how cybernetic principles of feedback control, used to explain sensorimotor behavior, can be extended to provide a foundation for understanding cognition. In particular, we describe behavior as parallel processes of competition and selection among potential action opportunities ('affordances') expressed at multiple levels of abstraction. Adaptive selection among currently available affordances is biased not only by predictions of their immediate outcomes and payoffs but also by predictions of what new affordances they will make available. This allows animals to purposively create new affordances that they can later exploit to achieve high-level goals, resulting in intentional action that links across multiple levels of control. Finally, we discuss how such a 'hierarchical affordance competition' process can be mapped to brain structure., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Neurophysiological studies of decision-making have focused primarily on elucidating the mechanisms of classic economic decisions, for which the relevant variables are the values of expected outcomes and action is simply the means of reporting the selected choice. By contrast, here we focus on the particular challenges of embodied decision-making faced by animals interacting with their environment in real time. In such scenarios, the choices themselves as well as their relative costs and benefits are defined by the momentary geometry of the immediate environment and change continuously during ongoing activity. To deal with the demands of embodied activity, animals require an architecture in which the sensorimotor specification of potential actions, their valuation, selection and even execution can all take place in parallel. Here, we review behavioural and neurophysiological data supporting a proposed brain architecture for dealing with such scenarios, which we argue set the evolutionary foundation for the organization of the mammalian brain., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)