How the Brain Converts Negative Evaluation into Performance Facilitation.

Surpassing negative evaluation is a recurrent theme of success stories. Yet, there is little evidence supporting the counterintuitive idea that negative evaluation might not only motivate people, but also enhance performance. To address this question, we designed a task that required participants to decide whether taking up a risky challenge after receiving positive or negative evaluations from independent judges. Participants believed that these evaluations were based on their prior performance on a related task. Results showed that negative evaluation caused a facilitation in performance. Concurrent functional magnetic resonance imaging revealed that the motivating effect of negative evaluation was represented in the insula and striatum, while the performance boost was associated with functional positive connectivity between the insula and a set of brain regions involved in goal-directed behavior and the orienting of attention. These findings provide new insight into the neural representation of negative evaluation-induced facilitation.

Inferences of Others' Competence Reduces Anticipation of Pain When under Threat.

On a daily basis, we place our lives in the hands of strangers. From dentists to pilots, we make inferences about their competence to perform their jobs and consequently to keep us from harm. Here we explore whether the perceived competence of others can alter one's anticipation of pain. In two studies, participants (Receivers) believed their chances of experiencing an aversive stimulus were directly dependent on the performance of another person (Players). We predicted that perceiving the Players as highly competent would reduce Receivers' anxiety when anticipating the possibility of an electric shock. Results confirmed that high competence ratings consistently corresponded with lower reported anxiety, and complementary fMRI data showed that increased competence perception was further expressed as decreased activity in the bilateral posterior insula, a region localized to actual pain stimulation. These studies suggest that inferences of competence act as predictors of protection and reduce the expectation of negative outcomes.

The ecology of human fear: survival optimization and the nervous system.

We propose a Survival Optimization System (SOS) to account for the strategies that humans and other animals use to defend against recurring and novel threats. The SOS attempts to merge ecological models that define a repertoire of contextually relevant threat induced survival behaviors with contemporary approaches to human affective science. We first propose that the goal of the nervous system is to reduce surprise and optimize actions by (i) predicting the sensory landscape by simulating possible encounters with threat and selecting the appropriate pre-encounter action and (ii) prevention strategies in which the organism manufactures safe environments. When a potential threat is encountered the (iii) threat orienting system is engaged to determine whether the organism ignores the stimulus or switches into a process of (iv) threat assessment, where the organism monitors the stimulus, weighs the threat value, predicts the actions of the threat, searches for safety, and guides behavioral actions crucial to directed escape. When under imminent attack, (v) defensive systems evoke fast reflexive indirect escape behaviors (i.e., fight or flight). This cascade of responses to threat of increasing magnitude are underwritten by an interconnected neural architecture that extends from cortical and hippocampal circuits, to attention, action and threat systems including the amygdala, striatum, and hard-wired defensive systems in the midbrain. The SOS also includes a modulatory feature consisting of cognitive appraisal systems that flexibly guide perception, risk and action. Moreover, personal and vicarious threat encounters fine-tune avoidance behaviors via model-based learning, with higher organisms bridging data to reduce face-to-face encounters with predators. Our model attempts to unify the divergent field of human affective science, proposing a highly integrated nervous system that has evolved to increase the organism's chances of survival.

Associative Self-Anchoring Interacts with Obtainability of Chosen Objects.

While there is evidence that implicit self-esteem transfers to chosen objects (associative self-anchoring), it is still unknown whether this phenomenon extends to explicit self-esteem. Moreover, whether the knowledge that these objects might belong to the self in the future or not affects the evaluation of these objects has received little attention. Here, we demonstrate that evaluations of chosen objects are further enhanced when they are obtainable as compared to when they are not in participants with high explicit self-esteem, whereas participants with low explicit self-esteem exhibit the opposite pattern. These findings extend previous results and shed new light on the role of self-esteem in altering preferences for chosen objects depending on their obtainability.

Evidence for model-based computations in the human amygdala during Pavlovian conditioning.

Contemporary computational accounts of instrumental conditioning have emphasized a role for a model-based system in which values are computed with reference to a rich model of the structure of the world, and a model-free system in which values are updated without encoding such structure. Much less studied is the possibility of a similar distinction operating at the level of Pavlovian conditioning. In the present study, we scanned human participants while they participated in a Pavlovian conditioning task with a simple structure while measuring activity in the human amygdala using a high-resolution fMRI protocol. After fitting a model-based algorithm and a variety of model-free algorithms to the fMRI data, we found evidence for the superiority of a model-based algorithm in accounting for activity in the amygdala compared to the model-free counterparts. These findings support an important role for model-based algorithms in describing the processes underpinning Pavlovian conditioning, as well as providing evidence of a role for the human amygdala in model-based inference.

Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: a high-resolution fMRI study.

It is widely held that the interaction between instrumental and Pavlovian conditioning induces powerful motivational biases. Pavlovian-Instrumental Transfer (PIT) is one of the key paradigms demonstrating this effect, which can further be decomposed into a general and specific component. Although these two forms of PIT have been studied at the level of amygdalar subregions in rodents, it is still unknown whether they involve different areas of the human amygdala. Using a high-resolution fMRI (hr-fMRI) protocol optimized for the amygdala in combination with a novel free operant task designed to elicit effects of both general and specific PIT, we demonstrate that a region of ventral amygdala within the boundaries of the basolateral complex and the ventrolateral putamen are involved in specific PIT, while a region of dorsal amygdala within the boundaries of the centromedial complex is involved in general PIT. These results add to a burgeoning literature indicating different functional contributions for these different amygdalar subregions in reward-processing and motivation.

Differentiable contributions of human amygdalar subregions in the computations underlying reward and avoidance learning.

To understand how the human amygdala contributes to associative learning, it is necessary to differentiate the contributions of its subregions. However, major limitations in the techniques used for the acquisition and analysis of functional magnetic resonance imaging (fMRI) data have hitherto precluded segregation of function with the amygdala in humans. Here, we used high-resolution fMRI in combination with a region-of-interest-based normalization method to differentiate functionally the contributions of distinct subregions within the human amygdala during two different types of instrumental conditioning: reward and avoidance learning. Through the application of a computational-model-based analysis, we found evidence for a dissociation between the contributions of the basolateral and centromedial complexes in the representation of specific computational signals during learning, with the basolateral complex contributing more to reward learning, and the centromedial complex more to avoidance learning. These results provide unique insights into the computations being implemented within fine-grained amygdala circuits in the human brain.

Separate valuation subsystems for delay and effort decision costs.

Decision making consists of choosing among available options on the basis of a valuation of their potential costs and benefits. Most theoretical models of decision making in behavioral economics, psychology, and computer science propose that the desirability of outcomes expected from alternative options can be quantified by utility functions. These utility functions allow a decision maker to assign subjective values to each option under consideration by weighting the likely benefits and costs resulting from an action and to select the one with the highest subjective value. Here, we used model-based neuroimaging to test whether the human brain uses separate valuation systems for rewards (erotic stimuli) associated with different types of costs, namely, delay and effort. We show that humans devalue rewards associated with physical effort in a strikingly similar fashion to those they devalue that are associated with delays, and that a single computational model derived from economics theory can account for the behavior observed in both delay discounting and effort discounting. However, our neuroimaging data reveal that the human brain uses distinct valuation subsystems for different types of costs, reflecting in opposite fashion delayed reward and future energetic expenses. The ventral striatum and the ventromedial prefrontal cortex represent the increasing subjective value of delayed rewards, whereas a distinct network, composed of the anterior cingulate cortex and the anterior insula, represent the decreasing value of the effortful option, coding the expected expense of energy. Together, these data demonstrate that the valuation processes underlying different types of costs can be fractionated at the cerebral level.