The case against economic values in the orbitofrontal cortex (or anywhere else in the brain)

2020 ◽  
Author(s):  
Benjamin Hayden ◽  
Yael Niv

Much of traditional neuroeconomics proceeds from the hypothesis that value is reified in the brain, that is, that there are neurons or brain regions whose responses serve the discrete purpose of encoding value. This hypothesis is supported by the finding that the activity of many neurons covaries with subjective value as estimated in specific tasks and has led to the idea that the primary function of the orbitofrontal cortex is to compute and signal economic value. Here we consider an alternative: that economic value, in the cardinal, common-currency sense, is not represented in the brain and used for choice by default. This idea is motivated by consideration of the economic concept of value, which places important epistemic constraints on our ability to identify its neural basis. It is also motivated by the behavioral economics literature, especially work on heuristics, which proposes value-free process models for much if not all of choice. Finally, it is buoyed by recent neural and behavioral findings regarding how animals and humans learn to choose between options. In light of our hypothesis, we critically reevaluate putative neural evidence for the representation of value and explore an alternative: direct learning of action policies. We delineate how this alternative can provide a robust account of behavior that concords with existing empirical data.

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Seiki Tajima ◽  
Shigeyuki Yamamoto ◽  
Masaaki Tanaka ◽  
Yosky Kataoka ◽  
Masao Iwase ◽  
...  

Fatigue is an indispensable bioalarm to avoid exhaustive state caused by overwork or stresses. It is necessary to elucidate the neural mechanism of fatigue sensation for managing fatigue properly. We performedH2O  15positron emission tomography scans to indicate neural activations while subjects were performing 35-min fatigue-inducing task trials twice. During the positron emission tomography experiment, subjects performed advanced trail-making tests, touching the target circles in sequence located on the display of a touch-panel screen. In order to identify the brain regions associated with fatigue sensation, correlation analysis was performed using statistical parametric mapping method. The brain region exhibiting a positive correlation in activity with subjective sensation of fatigue, measured immediately after each positron emission tomography scan, was located in medial orbitofrontal cortex (Brodmann's area 10/11). Hence, the medial orbitofrontal cortex is a brain region associated with mental fatigue sensation. Our findings provide a new perspective on the neural basis of fatigue.


2018 ◽  
Vol 29 (8) ◽  
pp. 3380-3389
Author(s):  
Timothy J Andrews ◽  
Ryan K Smith ◽  
Richard L Hoggart ◽  
Philip I N Ulrich ◽  
Andre D Gouws

Abstract Individuals from different social groups interpret the world in different ways. This study explores the neural basis of these group differences using a paradigm that simulates natural viewing conditions. Our aim was to determine if group differences could be found in sensory regions involved in the perception of the world or were evident in higher-level regions that are important for the interpretation of sensory information. We measured brain responses from 2 groups of football supporters, while they watched a video of matches between their teams. The time-course of response was then compared between individuals supporting the same (within-group) or the different (between-group) team. We found high intersubject correlations in low-level and high-level regions of the visual brain. However, these regions of the brain did not show any group differences. Regions that showed higher correlations for individuals from the same group were found in a network of frontal and subcortical brain regions. The interplay between these regions suggests a range of cognitive processes from motor control to social cognition and reward are important in the establishment of social groups. These results suggest that group differences are primarily reflected in regions involved in the evaluation and interpretation of the sensory input.


2019 ◽  
Author(s):  
Harry Farmer ◽  
Uri Hertz ◽  
Antonia Hamilton

AbstractDuring our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each confederate in terms of consistency of the similarity to the self, with respect to the choices they have made before (consistency account). Our study combined fMRI and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one confederate who usually shared their preference and another who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Data showed more brain regions whose activity pattern fits with the consistency account, specifically, areas linked to reward and social cognition. Our findings suggest that impressions of other people can be calculated in a person-specific manner which assumes that each individual behaves consistently with their past choices.


2016 ◽  
Author(s):  
Chuan-Peng Hu ◽  
Yi Huang ◽  
Simon B. Eickhoff ◽  
Kaiping Peng ◽  
Jie Sui

AbstractThe existence of a common beauty is a long-standing debate in philosophy and related disciplines. In the last two decades, cognitive neuroscientists have sought to elucidate this issue by exploring the common neural basis of the experience of beauty. Still, empirical evidence for such common neural basis of different forms of beauty is not conclusive. To address this question, we performed an activation likelihood estimation (ALE) meta-analysis on the existing neuroimaging studies of beauty appreciation of faces and visual art by non-expert adults (49 studies, 982 participants, meta-data are available at https://osf.io/s9xds/). We observed that perceiving these two forms of beauty activated distinct brain regions: while the beauty of faces convergently activated the left ventral striatum, the beauty of visual art convergently activated the anterior medial prefrontal cortex (aMPFC). However, a conjunction analysis failed to reveal any common brain regions for the beauty of visual art and faces. The implications of these results are discussed.


2019 ◽  
Author(s):  
Tehrim Yoon ◽  
Afareen Jaleel ◽  
Alaa A. Ahmed ◽  
Reza Shadmehr

AbstractDecisions are made based on the subjective value that the brain assigns to options. However, subjective value is a mathematical construct that cannot be measured directly, but rather inferred from choices. Recent results have demonstrated that reaction time and velocity of movements are modulated by reward, raising the possibility that there is a link between how the brain evaluates an option, and how it controls movements toward that option. Here, we asked people to choose among risky options represented by abstract stimuli, some associated with gain, others with loss. From their choices in decision trials we estimated the subjective value that they assigned to each stimulus. In probe trials, they were presented with a single stimulus at center and made a saccade to a peripheral location. We found that the reaction time and peak velocity of that saccade varied roughly linearly from loss to gain with the subjective value of the stimulus. Naturally, participants differed in how much they valued a given stimulus. Remarkably, those who valued a stimulus more, as evidenced by their choices in decision trials, tended to move with greater vigor in response to that stimulus in probe trials. Thus, saccade vigor partly reflected the subjective value that the brain assigned the stimulus. However, the influence of subjective value on vigor was only a modest predictor of preference: vigor in probe trials allowed us to predict choice in decision trials with roughly 60% accuracy.New and NoteworthyWe found that saccade vigor tends to vary monotonically with subjective value: smallest for stimuli that predict a loss, and highest for stimuli that predict a gain. Notably, between-subject differences in valuation could be gleaned from the between-subject differences in their patterns of vigor. However, the influence of subjective value on vigor was modest, allowing partial ability to infer subjective value for the purpose of predicting choice in decision trials.


2017 ◽  
Author(s):  
Cameron Parro ◽  
Matthew L Dixon ◽  
Kalina Christoff

AbstractCognitive control mechanisms support the deliberate regulation of thought and behavior based on current goals. Recent work suggests that motivational incentives improve cognitive control, and has begun to elucidate the brain regions that may support this effect. Here, we conducted a quantitative meta-analysis of neuroimaging studies of motivated cognitive control using activation likelihood estimation (ALE) and Neurosynth in order to delineate the brain regions that are consistently activated across studies. The analysis included functional neuroimaging studies that investigated changes in brain activation during cognitive control tasks when reward incentives were present versus absent. The ALE analysis revealed consistent recruitment in regions associated with the frontoparietal control network including the inferior frontal sulcus (IFS) and intraparietal sulcus (IPS), as well as consistent recruitment in regions associated with the salience network including the anterior insula and anterior mid-cingulate cortex (aMCC). A large-scale exploratory meta-analysis using Neurosynth replicated the ALE results, and also identified the caudate nucleus, nucleus accumbens, medial thalamus, inferior frontal junction/premotor cortex (IFJ/PMC), and hippocampus. Finally, we conducted separate ALE analyses to compare recruitment during cue and target periods, which tap into proactive engagement of rule-outcome associations, and the mobilization of appropriate viscero-motor states to execute a response, respectively. We found that largely distinct sets of brain regions are recruited during cue and target periods. Altogether, these findings suggest that flexible interactions between frontoparietal, salience, and dopaminergic midbrain-striatal networks may allow control demands to be precisely tailored based on expected value.


2021 ◽  
Vol 118 (30) ◽  
pp. e2022650118
Author(s):  
Alexandre Pastor-Bernier ◽  
Arkadiusz Stasiak ◽  
Wolfram Schultz

Sensitivity to satiety constitutes a basic requirement for neuronal coding of subjective reward value. Satiety from natural ongoing consumption affects reward functions in learning and approach behavior. More specifically, satiety reduces the subjective economic value of individual rewards during choice between options that typically contain multiple reward components. The unconfounded assessment of economic reward value requires tests at choice indifference between two options, which is difficult to achieve with sated rewards. By conceptualizing choices between options with multiple reward components (“bundles”), Revealed Preference Theory may offer a solution. Despite satiety, choices against an unaltered reference bundle may remain indifferent when the reduced value of a sated bundle reward is compensated by larger amounts of an unsated reward of the same bundle, and then the value loss of the sated reward is indicated by the amount of the added unsated reward. Here, we show psychophysically titrated choice indifference in monkeys between bundles of differently sated rewards. Neuronal chosen value signals in the orbitofrontal cortex (OFC) followed closely the subjective value change within recording periods of individual neurons. A neuronal classifier distinguishing the bundles and predicting choice substantiated the subjective value change. The choice between conventional single rewards confirmed the neuronal changes seen with two-reward bundles. Thus, reward-specific satiety reduces subjective reward value signals in OFC. With satiety being an important factor of subjective reward value, these results extend the notion of subjective economic reward value coding in OFC neurons.


2021 ◽  
Author(s):  
Jimmy Y. Zhong

Over the past two decades, many neuroimaging studies have attempted uncover the brain regions and networks involved in path integration and identify the underlying neurocognitive mechanisms. Although these studies made inroads into the neural basis of path integration, they have yet to offer a full disclosure of the functional specialization of the brain regions supporting path integration. In this paper, I reviewed notable neuroscientific studies on visual path integration in humans, identified the commonalities and discrepancies in their findings, and incorporated fresh insights from recent path integration studies. Specifically, this paper presented neuroscientific studies performed with virtual renditions of the triangle/path completion task and addressed whether or not the hippocampus is necessary for human path integration. Based on studies that showed evidence supporting and negating the involvement of the hippocampal formation in path integration, this paper introduces the proposal that the use of different path integration strategies may determine the extent to which the hippocampus and entorhinal cortex are engaged during path integration. To this end, recent studies that investigated the impact of different path integration strategies on behavioral performance and functional brain activity were discussed. Methodological concerns were raised with feasible recommendations for improving the experimental design of future strategy-related path integration studies, which can cover cognitive neuroscience research on age-related differences in the role of the hippocampal formation in path integration and Bayesian modelling of the interaction between landmark and self-motion cues. The practical value of investigating different path integration strategies was also discussed briefly from a biomedical perspective.


2021 ◽  
Vol 11 (8) ◽  
pp. 1096
Author(s):  
Yixuan Chen

Decision making is crucial for animal survival because the choices they make based on their current situation could influence their future rewards and could have potential costs. This review summarises recent developments in decision making, discusses how rewards and costs could be encoded in the brain, and how different options are compared such that the most optimal one is chosen. The reward and cost are mainly encoded by the forebrain structures (e.g., anterior cingulate cortex, orbitofrontal cortex), and their value is updated through learning. The recent development on dopamine and the lateral habenula’s role in reporting prediction errors and instructing learning will be emphasised. The importance of dopamine in powering the choice and accounting for the internal state will also be discussed. While the orbitofrontal cortex is the place where the state values are stored, the anterior cingulate cortex is more important when the environment is volatile. All of these structures compare different attributes of the task simultaneously, and the local competition of different neuronal networks allows for the selection of the most appropriate one. Therefore, the total value of the task is not encoded as a scalar quantity in the brain but, instead, as an emergent phenomenon, arising from the computation at different brain regions.


2020 ◽  
Author(s):  
Matthew Perich ◽  
Kanaka Rajan

The neural control of behavior is distributed across many functionally and anatomically distinct brain regions even in small nervous systems. While classical neuroscience models treated these regions as a set of hierarchically isolated nodes, the brain comprises a recurrently interconnected network in which each region is intimately modulated by many others. Uncovering these interactions is now possible through experimental techniques that access large neural populations from many brain regions simultaneously. Harnessing these large-scale datasets, however, requires new theoretical approaches. Here, we review recent work to understand brain-wide interactions using multi-region "network of networks" models and discuss how they can guide future experiments. We also emphasize the importance of multi-region recordings, and posit that studying individual components in isolation will be insufficient to understand the neural basis of behavior.


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