Perceptual learning and decision-making in human medial frontal cortex

The dominant view that perceptual learning is accompanied by changes in early sensory representations has recently been challenged. Here we tested the idea that perceptual learning can be accounted for by reinforcement learning involving changes in higher decision-making areas. We trained subjects on an orientation discrimination task involving feedback over 4 days, acquiring fMRI data on the first and last day. Behavioral improvements were well explained by a reinforcement learning model in which learning leads to enhanced readout of sensory information, thereby establishing noise-robust representations of decision variables. We find stimulus orientation encoded in early visual and higher cortical regions such as lateral parietal cortex and anterior cingulate cortex (ACC). However, only activity patterns in the ACC tracked changes in decision variables during learning. These results provide strong evidence for perceptual learning-related changes in higher order areas and suggest that perceptual and reward learning are based on a common neurobiological mechanism.     

Frontal cortex: goal-relatedness and the cortical motor system

A recent study has shown that prolonged electrical stimulation of the ventral premotor cortex can evoke complex defensive movements. Moreover, neurons in this region show activity correlated with the vigour of an induced defensive reaction. These results support the idea that this cortical region encodes goal-related actions.     

Neural mechanisms of social learning and decision-making

One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when "rational" social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of "social" brain structures such as temporal-parietal junction(TPJ) and dorsomedial prefrontal cortex(dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial(altruistic)behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.     

Structure learning in human sequential decision-making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner.     

Attracting dynamics of frontal cortex ensembles during memory-guided decision-making

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states.     

Motor cortex rules for learning and memory

Primary motor cortex has a complex, interconnected anatomical and functional architecture with dynamic properties. Recent evidence suggests that, concomitantly with regulating muscle activity and movements, the motor cortex makes key contributions to learning and remembering motor skills.     

Temporal evolution of a decision-making process in medial premotor cortex

The events linking sensory discrimination to motor action remain unclear. It is not known, for example, whether the motor areas of the frontal lobe receive the result of the discrimination process from other areas or whether they actively participate in it. To investigate this, we trained monkeys to discriminate between two mechanical vibrations applied sequentially to the fingertips; here subjects had to recall the first vibration, compare it to the second one, and indicate with a hand/arm movement which of the two vibrations had the higher frequency. We recorded the activity of single neurons in medial premotor cortex (MPC) and found that their responses correlate with the diverse stages of the discrimination process. Thus, activity in MPC reflects the temporal evolution of the decision-making process leading to action selection during this perceptual task.     

Orbitofrontal cortex, associative learning, and expectancies

Orbitofrontal cortex is characterized by its unique pattern of connections with subcortical areas, such as basolateral amygdala. Here we distinguish between the critical role of these areas in associative learning and the pivotal contribution of OFC to the manipulation of this information to control behavior. This contribution reflects the ability of OFC to signal the desirability of expected outcomes, which requires the integration of associative information with information concerning internal states and goals in representational memory.     

Prefrontal cortex: procedural sequence learning and awareness

Activation of the prefrontal cortex has been linked to awareness during sequence-learning tasks. A recent study, however, finds activation of the prefrontal cortex during such tasks regardless of awareness. So what is the neurophysiological basis of awareness, and what is the role of the prefrontal cortex in sequence learning?     

Odour learning and decision-making during food collection in the leaf-cutting antAcromyrmex lundi

Summary Recruited workers of the leaf-cutting antAcromyrmex lundi learn the odour of the food fragment initially carried by a successful scout, and use this cue as a decision criterion during food collection. Citral, a natural deterrent odour, can also be associated as a food signal, showing how powerful olfactory learning is in this species. The role of both food-odour learning and a previously reported leaf-marking pheromone during diet selection by leaf-cutting ants is discussed.     

Perceptual learning and dynamic changes in primary visual cortex

Perceptual learning is the improved performance that follows practice in a perceptual task. In this issue of Neuron, Yotsumoto et al. use fMRI to show that stimuli presented at the location used in training initially evoke greater activation in primary visual cortex than stimuli presented elsewhere, but this difference disappears once learning asymptotes.     

Reasoning, learning, and creativity: frontal lobe function and human decision-making

The frontal lobes subserve decision-making and executive control--that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior.     

Stress undermines reward-guided cognitive performance through synaptic depression in the lateral habenula

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Observing the observer (I): meta-bayesian models of learning and decision-making

In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context- or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task.     

Modelling the learning of biomechanics and visual planning for decision-making of motor actions

Recent experiments showed that the bio-mechanical ease and end-point stability associated to reaching movements are predicted prior to movement onset, and that these factors exert a significant influence on the choice of movement. As an extension of these results, here we investigate whether the knowledge about biomechanical costs and their influence on decision-making are the result of an adaptation process taking place during each experimental session or whether this knowledge was learned at an earlier stage of development. Specifically, we analysed both the pattern of decision-making and its fluctuations during each session, of several human subjects making free choices between two reaching movements that varied in path distance (target relative distance), biomechanical cost, aiming accuracy and stopping requirement. Our main result shows that the effect of biomechanics is well established at the start of the session, and that, consequently, the learning of biomechanical costs in decision-making occurred at an earlier stage of development. As a means to characterise the dynamics of this learning process, we also developed a model-based reinforcement learning model, which generates a possible account of how biomechanics may be incorporated into the motor plan to select between reaching movements. Results obtained in simulation showed that, after some pre-training corresponding to a motor babbling phase, the model can reproduce the subjects’ overall movement preferences. Although preliminary, this supports that the knowledge about biomechanical costs may have been learned in this manner, and supports the hypothesis that the fluctuations observed in the subjects’ behaviour may adapt in a similar fashion.     

Online learning and stimulus-driven responses of neurons in visual cortex

In understanding how visual scene is processed in visual cortex, it has been an intriguing problem for theoretical and experimental neuroscientists to examine the relationship between visual stimuli and the induced responses of visual cortex. In particular, it is less explored whether and how the collective responses of visual neurons are patterned to reflect the geometrical regularities. In this paper, through a computation model and statistical analysis, we show that the orientation preference maps induced from correlated visual stimuli exhibit geometrical regularities similar as observed in natural images.     

A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour

Both orbitofrontal cortex (OFC) and ventral striatum (vStr) have been identified as key structures that represent information about value in decision-making tasks. However, the dynamics of how this information is processed are not yet understood. We recorded ensembles of cells from OFC and vStr in rats engaged in the spatial adjusting delay-discounting task, a decision-making task that involves a trade-off between delay to and magnitude of reward. Ventral striatal neural activity signalled information about reward before the rat''s decision, whereas such reward-related signals were absent in OFC until after the animal had committed to its decision. These data support models in which vStr is directly involved in action selection, but OFC processes decision-related information afterwards that can be used to compare the predicted and actual consequences of behaviour.