A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Neuro-computational account of arbitration between imitation and emulation during human observational learning

In observational learning (OL), organisms learn from observing the behavior of others. There are at least two distinct strategies for OL. Imitation involves learning to repeat the previous actions of other agents, while in emulation, learning proceeds from inferring the goals and intentions of others. While putative neural correlates for these forms of learning have been identified, a fundamental question remains unaddressed: how does the brain decides which strategy to use in a given situation? Here we developed a novel computational model in which arbitration between the strategies is determined by the predictive reliability, such that control over behavior is adaptively weighted toward the strategy with the most reliable prediction. To test the theory, we designed a novel behavioral task in which our experimental manipulations produced dissociable effects on the reliability of the two strategies. Participants performed this task while undergoing fMRI in two independent studies (the second a pre-registered replication of the first). Behavior manifested patterns consistent with both emulation and imitation and flexibly changed between the two strategies as expected from the theory. Computational modelling revealed that behavior was best described by an arbitration model, in which the reliability of the emulation strategy determined the relative weights allocated to behavior for each strategy. Emulation reliability - the model's arbitration signal - was encoded in the ventrolateral prefrontal cortex, temporoparietal junction and rostral cingulate cortex. Being replicated across two fMRI studies, these findings suggest a neuro-computational mechanism for allocating control between emulation and imitation during observational learning

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

Neuro-computational account of arbitration between imitation and emulation during human observational learning

In observational learning (OL), organisms learn from observing the behavior of others. There are at least two distinct strategies for OL. Imitation involves learning to repeat the previous actions of other agents, while in emulation, learning proceeds from inferring the goals and intentions of others. While putative neural correlates for these forms of learning have been identified, a fundamental question remains unaddressed: how does the brain decides which strategy to use in a given situation? Here we developed a novel computational model in which arbitration between the strategies is determined by the predictive reliability, such that control over behavior is adaptively weighted toward the strategy with the most reliable prediction. To test the theory, we designed a novel behavioral task in which our experimental manipulations produced dissociable effects on the reliability of the two strategies. Participants performed this task while undergoing fMRI in two independent studies (the second a pre-registered replication of the first). Behavior manifested patterns consistent with both emulation and imitation and flexibly changed between the two strategies as expected from the theory. Computational modelling revealed that behavior was best described by an arbitration model, in which the reliability of the emulation strategy determined the relative weights allocated to behavior for each strategy. Emulation reliability - the model's arbitration signal - was encoded in the ventrolateral prefrontal cortex, temporoparietal junction and rostral cingulate cortex. Being replicated across two fMRI studies, these findings suggest a neuro-computational mechanism for allocating control between emulation and imitation during observational learning

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Landau damping: instability mechanism of superfluid Bose gases moving in optical lattices

We investigate Landau damping of Bogoliubov excitations in a dilute Bose gas moving in an optical lattice at finite temperatures. Using a 1D tight-binding model, we explicitly obtain the Landau damping rate, the sign of which determines the stability of the condensate. We find that the sign changes at a certain condensate velocity, which is exactly the same as the critical velocity determined by the Landau criterion of superfluidity. This coincidence of the critical velocities reveals the microscopic mechanism of the Landau instability. This instability mechanism is also consistent with the recent experiment suggesting that a thermal cloud plays a crucial role in breakdown of superfluids, since the thermal cloud is also vital in the Landau damping process. We also examine the possibility of simultaneous disappearance of all damping processes.Comment: 9 pages, 5 figure

Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex

Having something to look forward to is a keystone of well-being. Anticipation of a future reward, like an upcoming vacation, can be more gratifying than the experience of reward itself. Theories of anticipation have described how it causes behaviors ranging from beneficial information-seeking to harmful addiction. Here, we investigated how the brain generates and enhances anticipatory pleasure, by analyzing brain activity of human participants who received information predictive of future pleasant outcomes in a decision-making task. Using a computational model of anticipation, we show that three regions orchestrate anticipatory pleasure. We show ventromedial prefrontal cortex (vmPFC) tracks the value of anticipation; dopaminergic midbrain responds to information that enhances anticipation, while the sustained activity in hippocampus provides for functional coupling between these regions. This coordinating role for hippocampus is consistent with its known role in the vivid imagination of future outcomes. Our findings throw new light on the neural underpinnings of how anticipation influences decision-making, while also unifying a range of phenomena associated with risk and time-delay preference

Cognitive Bias in Ambiguity Judgements:Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans

Positive and negative moods can be treated as prior expectations over future delivery of rewards and punishments. This provides an inferential foundation for the cognitive (judgement) bias task, now widely-used for assessing affective states in non-human animals. In the task, information about affect is extracted from the optimistic or pessimistic manner in which participants resolve ambiguities in sensory input. Here, we report a novel variant of the task aimed at dissecting the effects of affect manipulations on perceptual and value computations for decision-making under ambiguity in humans. Participants were instructed to judge which way a Gabor patch (250ms presentation) was leaning. If the stimulus leant one way (e.g. left), pressing the REWard key yielded a monetary WIN whilst pressing the SAFE key failed to acquire the WIN. If it leant the other way (e.g. right), pressing the SAFE key avoided a LOSS whilst pressing the REWard key incurred the LOSS. The size (0-100 UK pence) of the offered WIN and threatened LOSS, and the ambiguity of the stimulus (vertical being completely ambiguous) were varied on a trial-by-trial basis, allowing us to investigate how decisions were affected by differing combinations of these factors. Half the subjects performed the task in a 'Pleasantly' decorated room and were given a gift (bag of sweets) prior to starting, whilst the other half were in a bare 'Unpleasant' room and were not given anything. Although these treatments had little effect on self-reported mood, they did lead to differences in decision-making. All subjects were risk averse under ambiguity, consistent with the notion of loss aversion. Analysis using a Bayesian decision model indicated that Unpleasant Room subjects were ('pessimistically') biased towards choosing the SAFE key under ambiguity, but also weighed WINS more heavily than LOSSes compared to Pleasant Room subjects. These apparently contradictory findings may be explained by the influence of affect on different processes underlying decision-making, and the task presented here offers opportunities for further dissecting such processes

Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour

Scientific methods for assessing animal affect, especially affective valence (positivity or negativity), allow us to evaluate animal welfare and the effectiveness of 3Rs Refinements designed to improve wellbeing. Judgement bias tasks measure valence; however, task-training may be lengthy and/or require significant time from researchers. Here we develop an automated and self-initiated judgement bias task for rats which capitalises on their natural investigative behaviour. Rats insert their noses into a food trough to start trials. They then hear a tone and learn either to stay for 2 s to receive a food reward or to withdraw promptly to avoid an air-puff. Which contingency applies is signalled by two different tones. Judgement bias is measured by responses to intermediate ambiguous tones. In two experiments we show that rats learn the task in fewer sessions than other automated variants, generalise responses across ambiguous tones as expected, self-initiate 4-5 trials/min, and can be tested repeatedly. Affect manipulations generate main effect trends in the predicted directions, although not localised to ambiguous tones, so further construct validation is required. We also find that tone-reinforcer pairings and reinforcement or non-reinforcement of ambiguous trials can affect responses to ambiguity. This translatable task should facilitate more widespread uptake of judgement bias testing