Neural substrate for the engagement of the ventral visual stream in motor control in the macaque monkey.

The present study aimed to describe the cortical connectivity of a sector located in the ventral bank of the superior temporal sulcus in the macaque (intermediate area TEa and TEm [TEa/m]), which appears to represent the major source of output of the ventral visual stream outside the temporal lobe. The retrograde tracer wheat germ agglutinin was injected in the intermediate TEa/m in four macaque monkeys. The results showed that 58-78% of labeled cells were located within ventral visual stream areas other than the TE complex. Outside the ventral visual stream, there were connections with the memory-related medial temporal area 36 and the parahippocampal cortex, orbitofrontal areas involved in encoding subjective values of stimuli for action selection, and eye- or hand-movement related parietal (LIP, AIP, and SII), prefrontal (12r, 45A, and 45B) areas, and a hand-related dysgranular insula field. Altogether these data provide a solid substrate for the engagement of the ventral visual stream in large scale cortical networks for skeletomotor or oculomotor control. Accordingly, the role of the ventral visual stream could go beyond pure perceptual processes and could be also finalized to the neural mechanisms underlying the control of voluntary motor behavior.

Dynamic ensemble balance in direct- and indirect-pathway striatal projection neurons underlying decision-related action selection.

The posterior dorsal striatum (pDS) plays an essential role in sensory-guided decision-making. However, it remains unclear how the antagonizing direct- and indirect-pathway striatal projection neurons (dSPNs and iSPNs) work in concert to support action selection. Here, we employed deep-brain two-photon imaging to investigate pathway-specific single-neuron and population representations during an auditory-guided decision-making task. We found that the majority of pDS projection neurons predominantly encode choice information. Both dSPNs and iSPNs comprise divergent subpopulations of comparable sizes representing competing choices, rendering a multi-ensemble balance between the two pathways. Intriguingly, such ensemble balance displays a dynamic shift during the decision period: dSPNs show a significantly stronger preference for the contraversive choice than iSPNs. This dynamic shift is further manifested in the inter-neuronal coactivity and population trajectory divergence. Our results support a balance-shift model as a neuronal population mechanism coordinating the direct and indirect striatal pathways for eliciting selected actions during decision-making.

Neuromechanical proficiency in elite performance decision-making: An event-related potential (ERP) analysis.

This research focuses intensively on the neural mechanisms of action selection responses in elite athletes during high-stakes decision-making. It emphasizes the neuromechanical dimensions of these processes, contrasting the responses of expert and novice players within a theoretical framework of neural information processing in high-performance contexts. Utilizing advanced EEG technologies, including event-related potential (ERP) analysis, the study captures a comprehensive view of both behavioral and neurophysiological data. The central aim is to unravel the intricate neural underpinnings that distinguish elite athletes in their decision-making strategies. Key findings highlight: (1) Enhanced accuracy and swifter reaction times in elite athletes during the action selection phase; (2) Significant neurophysiological differences, marked by pronounced N1 peak amplitudes with prolonged latencies, reduced P2 peak amplitudes with stable latencies, decreased P3 peak amplitudes with reduced latencies, and increased average PSW amplitudes. These discoveries significantly advance our understanding of the neural foundations of expert decision-making in high-performance sports. This study not only sheds light on the cognitive and neural dynamics of elite sports performance but also provides a foundation for developing training and performance enhancement techniques in various high-stakes domains.

Biomechanical costs influence decisions made during ongoing actions.

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.

Distinct Action Signals by Subregions in the Nucleus Accumbens during STOP-Change Performance.

The nucleus accumbens (NAc) is thought to contribute to motivated behavior by signaling the value of reward-predicting cues and the delivery of anticipated reward. The NAc is subdivided into core and shell, with each region containing different populations of neurons that increase or decrease firing to rewarding events. While there are numerous theories of functions pertaining to these subregions and cell types, most are in the context of reward processing, with fewer considering that the NAc might serve functions related to action selection more generally. We recorded from single neurons in the NAc as rats of both sexes performed a STOP-change task that is commonly used to study motor control and impulsivity. In this task, rats respond quickly to a spatial cue on 80% of trials (GO) and must stop and redirect planned movement on 20% of trials (STOP). We found that the activity of reward-excited neurons signaled accurate response direction on GO, but not STOP, trials and that these neurons exhibited higher precue firing after correct trials. In contrast, reward-inhibited neurons significantly represented response direction on STOP trials at the time of the instrumental response. Finally, the proportion of reward-excited to reward-inhibited neurons and the strength of precue firing decreased as the electrode traversed the NAc. We conclude that reward-excited cells (more common in core) promote proactive action selection, while reward-inhibited cells (more common in shell) contribute to accurate responding on STOP trials that require reactive suppression and redirection of behavior.

Neural Correlates of Online Action Preparation.

When performing movements in rapid succession, the brain needs to coordinate ongoing execution with the preparation of an upcoming action. Here we identify the processes and brain areas involved in this ability of online preparation. Human participants (both male and female) performed pairs of single-finger presses or three-finger chords in rapid succession, while 7T fMRI was recorded. In the overlap condition, they could prepare the second movement during the first response and in the nonoverlap condition only after the first response was completed. Despite matched perceptual and movement requirements, fMRI revealed increased brain activity in the overlap condition in regions along the intraparietal sulcus and ventral visual stream. Multivariate analyses suggested that these areas are involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action execution and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with the execution of a current action without requiring additional neural activity.

Action Selection in Everyday Activities: The Opportunistic Planning Model.

While action selection strategies in well-defined domains have received considerable attention, little is yet known about how people choose what to do next in ill-defined tasks. In this contribution, we shed light on this issue by considering everyday tasks, which in many cases have a multitude of possible solutions (e.g., it does not matter in which order the items are brought to the table when setting a table) and are thus categorized as ill-defined problems. Even if there are no hard constraints on the ordering of subtasks in everyday activities, our research shows that people exhibit specific preferences. We propose that these preferences arise from bounded rationality, that is, people only have limited knowledge and processing power available, which results in a preference to minimize the overall physical and cognitive effort. In the context of everyday activities, this can be achieved by (a) taking properties of the spatial environment into account to use them to one's advantage, and (b) employing a stepwise-optimal action selection strategy. We present the Opportunistic Planning Model as an explanatory cognitive model, which instantiates these assumptions, and show that the model is able to generalize to new everyday tasks, outperforming machine learning models such as neural networks during generalization.

Clinical severity in Parkinson's disease is determined by decline in cortical compensation.

Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Perceptual control or action-selection? Comment on: a perceptual control theory of emotional action.

The Perceptual Control Theory of Emotional Action provides a compelling view of the synergy between action and perception in the context of emotion. In this invited response, we outline three suggestions to further clarify and concretesise the theory in the hope that it can provide a solid basis for the theoretical, empirical, and clinical fields of emotion and emotion regulation. First, we emphasise the importance of concretesising these ideas in a way that is biologically plausible and testable in terms of its neuronal implementation, which has not been addressed in the main manuscript. Secondly, we highlight the challenges for this account to effectively describe core symptoms in emotional disorders, an essential step if the theory aims to foster the development of better-tuned neurocognitively grounded interventions. Finally, we take a leap on what action-oriented accounts of emotion can mean for the field of emotion regulation.

Comment on Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. Entropy 2023, 25, 449.

Integrated information theory (IIT) is a powerful tool that provides a framework for evaluating consciousness, whether in the human brain or in other systems. In Computing the Integrated Information of a Quantum Mechanism, the authors extend IIT from digital gates to a quantum CNOT logic gate, and while they explicitly distinguish the analysis from quantum theories of consciousness, they nonetheless provide an analytical road map for extending IIT not only to other quantum mechanisms but also to hybrid computing structures like the brain. This comment provides additional information relating to an adiabatic quantum mechanical energy routing mechanism that is part of a hybrid biological computer that provides an action selection mechanism, which has been hypothesized to exist in the human brain and for which predicted evidence has been subsequently observed, and it hopes to motivate the further evaluation and extension of IIT not only to that hypothesized mechanism but also to other hybrid biological computers.

Inhibitory Pedunculopontine Neurons Gate Dopamine-Mediated Motor Actions of Unsigned Valence.

BACKGROUND: The pedunculopontine nucleus (PPN) maintains a bidirectional connectivity with the basal ganglia that supports their shared roles in the selection and execution of motor actions. Previous studies identified a role for PPN neurons in goal-directed behavior, but the cellular substrates underlying this function have not been elucidated. We recently revealed the existence of a monosynaptic GABAergic input from the PPN that inhibits dopamine neurons of the substantia nigra. Activation of this pathway interferes with the execution of learned motor sequences when the actions are rewarded, even though the inhibition of dopamine neurons did not shift the value of the action, hence suggesting executive control over the gating of behavior. OBJECTIVE: To test the attributes of the inhibition of dopamine neurons by the PPN in the context of goal-directed behavior regardless of whether the outcome is positive or negative. METHODS: We delivered optogenetic stimulation to PPN GABAergic axon terminals in the substantia nigra during a battery of behavioral tasks with positive and negative valence. RESULTS: Inhibition of dopamine neurons by PPN optogenetic activation during an appetitive task impaired the initiation and overall execution of the behavioral sequence without affecting the consumption of reward. During an active avoidance task, the same activation impaired the ability of mice to avoid a foot shock, but their escape response was unaffected. In addition, responses to potential threats were significantly attenuated. CONCLUSION: Our results show that PPN GABAergic neurons modulate learned, goal-directed behavior of unsigned valence without affecting overall motor behavior.

Multiple dynamic interactions from basal ganglia direct and indirect pathways mediate action selection.

The basal ganglia are known to be essential for action selection. However, the functional role of basal ganglia direct and indirect pathways in action selection remains unresolved. Here, by employing cell-type-specific neuronal recording and manipulation in mice trained in a choice task, we demonstrate that multiple dynamic interactions from the direct and indirect pathways control the action selection. While the direct pathway regulates the behavioral choice in a linear manner, the indirect pathway exerts a nonlinear inverted-U-shaped control over action selection, depending on the inputs and the network state. We propose a new center (direct)-surround (indirect)-context (indirect) 'Triple-control' functional model of basal ganglia, which can replicate the physiological and behavioral experimental observations that cannot be simply explained by either the traditional 'Go/No-go' or more recent 'Co-activation' model. These findings have important implications on understanding the basal ganglia circuitry and action selection in health and disease.

Emotion regulation from an action-control perspective.

Despite increasing interest in emotional processes in cognitive science, theories on emotion regulation have remained rather isolated, predominantly focused on cognitive regulation strategies such as reappraisal. However, recent neurocognitive evidence suggests that early emotion regulation may involve sensorimotor control in addition to other emotion-regulation processes. We propose an action-oriented view of emotion regulation, in which feedforward predictions develop from action-selection mechanisms. Those can account for acute emotional-action control as well as more abstract instances of emotion regulation such as cognitive reappraisal. We argue the latter occurs in absence of overt motor output, yet in the presence of full-blown autonomic, visceral, and subjective changes. This provides an integrated framework with testable neuro-computational predictions and concrete starting points for intervention to improve emotion control in affective disorders.

The Basal Ganglia and Mesencephalic Locomotor Region Connectivity Matrix.

Although classically considered a relay station for basal ganglia (BG) output, the anatomy, connectivity, and function of the mesencephalic locomotor region (MLR) were redefined during the last two decades. In striking opposition to what was initially thought, MLR and BG are actually recip- rocally and intimately interconnected. New viral-based, optogenetic, and mapping technologies re- vealed that cholinergic, glutamatergic, and GABAergic neurons coexist in this structure, which, in ad- dition to extending descending projections, send long-range ascending fibers to the BG. These MLR projections to the BG convey motor and non-motor information to specific synaptic targets throughout different nuclei. Moreover, MLR efferent fibers originate from precise neuronal subpopulations locat- ed in particular MLR subregions, defining independent anatomo-functional subcircuits involved in particular aspects of animal behavior such as fast locomotion, explorative locomotion, posture, fore- limb-related movements, speed, reinforcement, among others. In this review, we revised the literature produced during the last decade linking MLR and BG. We conclude that the classic framework con- sidering the MLR as a homogeneous output structure passively receiving input from the BG needs to be revisited. We propose instead that the multiple subcircuits embedded in this region should be taken as independent entities that convey relevant and specific ascending information to the BG and, thus, actively participate in the execution and tuning of behavior.

Non-shared coding of observed and executed actions prevails in macaque ventral premotor mirror neurons.

According to the mirror mechanism the discharge of F5 mirror neurons of a monkey observing another individual performing an action is a motor representation of the observed action that may serve to understand or learn from the action. This hypothesis, if strictly interpreted, requires mirror neurons to exhibit an action tuning that is shared between action observation and execution. Due to insufficient data it remains contentious if this requirement is met. To fill in the gaps, we conducted an experiment in which identical objects had to be manipulated in three different ways in order to serve distinct action goals. Using three methods, including cross-task classification, we found that at most time points F5 mirror neurons did not encode observed actions with the same code underlying action execution. However, in about 20% of neurons there were time periods with a shared code. These time periods formed a distinct cluster and cannot be considered a product of chance. Population classification yielded non-shared coding for observed actions in the whole population, which was at times optimal and consistently better than shared coding in differentially selected subpopulations. These results support the hypothesis of a representation of observed actions based on a strictly defined mirror mechanism only for small subsets of neurons and only under the assumption of time-resolved readout. Considering alternative concepts and recent findings, we propose that during observation mirror neurons represent the process of a goal pursuit from the observer's viewpoint. Whether the observer's goal pursuit, in which the other's action goal becomes the observer's action goal, or the other's goal pursuit is represented remains to be clarified. In any case, it may allow the observer to use expectations associated with a goal pursuit to directly intervene in or learn from another's action.

How honey bees make fast and accurate decisions.

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

In the natural world, decision-making processes are often intricate and challenging. Animals frequently encounter situations where they have limited information on which to rely to guide them, yet even simple choices can have far-reaching impact on survival. Each time a bee sets out to collect nectar, for example, it must use tiny variations in colour or odour to decide which flower it should land on and explore. Each 'mistake' is costly, wasting energy and exposing the insect to potential dangers. To learn how to refine their choices through trial-and-error, bees only have at their disposal a brain the size of a sesame seed, which contains fewer than a million neurons. And yet, they excel at this task, being both quick and accurate. The underlying mechanisms which drive these remarkable decision-making capabilities remain unclear. In response, MaBouDi et al. aimed to explore which strategies honeybees adopt to forage so effectively, and the neural systems that may underlie them. To do so, they released the insects in a 'field' containing artificial flowers in five different colours. The bees were trained to link each colour with a certain likelihood of receiving either a sugary liquid (reward) or bitter quinine (punishment); they were then tested on this knowledge. Next, MaBouDi et al. recorded how the bees would navigate a 'reduced evidence' test, where the colour of the flowers were ambiguous and consisted in various blends of the originally rewarded or punished colours; and a 'reduced reward likelihood' test, where the sweet recompense was offered less often than before. Response times and accuracy rates revealed a complex pattern of decision-making processes. How quickly the insects made a choice, and the types of mistakes they made (such as deciding to explore a non-rewarded flower, or to ignore a rewarded one) were dependent on both the quality of the evidence and the certainty of the reward. Such sophistication and subtlety in decision-making is comparable to that of primates. Next, MaBouDi et al. developed a computational model which could faithfully replicate the pattern of decisions exhibited by the bees, while also being plausible biologically. This approach offered insights into how a small brain could execute such complex choices 'on the fly', and the type of neural circuits that would be required. Going forward, this knowledge could be harnessed to design more efficient decision-making algorithms for artificial systems, and in particular for autonomous robotics.

The effect of context and reason on the neural correlates of intentions.

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.

Evidence for a common mechanism supporting invigoration of action selection and action execution.

The speed, or vigor, of our movements can vary depending on circumstances. For instance, the promise of a reward leads to faster movements. Reward also leads us to move with a lower reaction time, suggesting that the process of action selection can also be invigorated by reward. It has been proposed that invigoration of action selection and of action execution might occur through a common mechanism, and thus these aspects of behavior might be coupled. To test this hypothesis, we asked participants to make reaching movements to "shoot" through a target at varying speeds to assess whether moving more quickly was also associated with more rapid action selection. We found that, when participants were required to move with a lower velocity, the speed of their action selection was also significantly slowed. This finding was recapitulated in a further dataset in which participants determined their own movement speed, but had to move slowly to stop their movement inside the target. By reanalyzing a previous dataset, we also found evidence for the converse relationship between action execution and action selection; when pressured to select actions more rapidly, people also executed movements with higher velocity. Our results establish that invigoration of action selection and action execution vary in tandem with one another, supporting the hypothesis of a common underlying mechanism.NEW & NOTEWORTHY We show that voluntary increases in the vigor of action execution lead action selection to also occur more rapidly. Conversely, hastening action selection by imposing a deadline to act also leads to increases in movement speed. These findings provide evidence that these two distinct aspects of behavior are modulated by a common underlying mechanism.

A biologically inspired decision-making system for the autonomous adaptive behavior of social robots.

The decisions made by social robots while they fulfill their tasks have a strong influence on their performance. In these contexts, autonomous social robots must exhibit adaptive and social-based behavior to make appropriate decisions and operate correctly in complex and dynamic scenarios. This paper presents a Decision-Making System for social robots working on long-term interactions like cognitive stimulation or entertainment. The Decision-making System employs the robot's sensors, user information, and a biologically inspired module to replicate how human behavior emerges in the robot. Besides, the system personalizes the interaction to maintain the users' engagement while adapting to their features and preferences, overcoming possible interaction limitations. The system evaluation was in terms of usability, performance metrics, and user perceptions. We used the Mini social robot as the device where we integrated the architecture and carried out the experimentation. The usability evaluation consisted of 30 participants interacting with the autonomous robot in 30 min sessions. Then, 19 participants evaluated their perceptions of robot attributes of the Godspeed questionnaire by playing with the robot in 30 min sessions. The participants rated the Decision-making System with excellent usability (81.08 out of 100 points), perceiving the robot as intelligent (4.28 out of 5), animated (4.07 out of 5), and likable (4.16 out of 5). However, they also rated Mini as unsafe (security perceived as 3.15 out of 5), probably because users could not influence the robot's decisions.

Why it hurts: with freedom comes the biological need for pain.

We argue that pain is not needed to protect the body from damage unless the organism is able to make free choices in action selection. Then pain (including its affective and evaluative aspects) provides a necessary prioritising motivation to select actions expected to avoid it, whilst leaving the possibility of alternative actions to serve potentially higher priorities. Thus, on adaptive grounds, only organisms having free choice over action selection should experience pain. Free choice implies actions must be selected following appraisal of their effects, requiring a predictive model generating estimates of action outcomes. These features give organisms anticipatory behavioural autonomy (ABA), for which we propose a plausible system using an internal predictive model, integrated into a system able to produce the qualitative and affective aspects of pain. Our hypothesis can be tested using behavioural experiments designed to elicit trade-off responses to novel experiences for which algorithmic (automaton) responses might be inappropriate. We discuss the empirical evidence for our hypothesis among taxonomic groups, showing how testing for ABA guides thinking on which groups might experience pain. It is likely that all vertebrates do and plausible that some invertebrates do (decapods, cephalopods and at least some insects).