scholarly journals Reward boosts neural coding of task rules to optimise cognitive flexibility

2019 ◽  
Author(s):  
S. Hall-McMaster ◽  
P.S. Muhle-Karbe ◽  
N.E. Myers ◽  
M.G. Stokes
Keyword(s):  
Decision Making ◽  
Cognitive Flexibility ◽  
Neural Coding ◽  
Adaptive Behaviour ◽  
Neural Encoding ◽  
Healthy Human ◽  
Complex Behaviour ◽  
Flexible Control ◽  
Task Rule ◽  
High Reward

AbstractCognitive flexibility is critical for intelligent behaviour. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behaviour can be improved by the prospect of reward, which suggests that rewards optimise flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimise cognitive flexibility. We applied representational similarity analysis (RSA) to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high reward trials. Critically, high reward boosted neural coding of the active task rule and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening neural coding of task rule information, helping to improve cognitive flexibility during complex behaviour.Significance StatementThe importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behaviour and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates encoding of task rules needed for flexible behaviour. The extent to which reward increased task rule coding also tracked improvements in behavioural performance under high reward conditions. These findings help inform how motivation shapes neural processing in the healthy human brain.

scholarly journals Outcome contingency selectively affects the neural coding of outcomes but not of tasks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass
Keyword(s):  
Decision Making ◽  
Parietal Cortex ◽  
Neural Coding ◽  
Free Choice ◽  
Pattern Analysis ◽  
Maintenance Phase ◽  
Large Network ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.

scholarly journals Neural encoding of perceived patch value during competitive and hazardous virtual foraging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Brian Silston ◽  
Toby Wise ◽  
Song Qi ◽  
Xin Sui ◽  
Peter Dayan ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Foraging ◽  
Survival Strategy ◽  
Neural Encoding ◽  
Individual Level ◽  
Hazardous Environments ◽  
Predatory Attack ◽  
Multidimensional Information ◽  
Human Participants ◽  
Foraging Task

AbstractNatural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.

Target Detection by Opponent Coding in Monkey Prefrontal Cortex

2010 ◽  
Vol 22 (4) ◽  
pp. 751-760 ◽  
Author(s):  
Makoto Kusunoki ◽  
Natasha Sigala ◽  
Hamed Nili ◽  
David Gaffan ◽  
John Duncan
Keyword(s):  
Decision Making ◽  
Target Detection ◽  
Neural Model ◽  
Stimulus Onset ◽  
Stimulus Category ◽  
Flexible Control ◽  
Cell Groups ◽  
Show Evidence ◽  
Context Specific ◽  
Monkey Prefrontal Cortex

The pFC plays a key role in flexible, context-specific decision making. One proposal [Machens, C. K., Romo, R., & Brody, C. D. Flexible control of mutual inhibition: A neural model of two-interval discrimination. Science, 307, 1121–1124, 2005] is that prefrontal cells may be dynamically organized into opponent coding circuits, with competitive groups of cells coding opposite behavioral decisions. Here, we show evidence for extensive, temporally evolving opponent organization in the monkey pFC during a cued target detection task. More than a half of all randomly selected cells discriminated stimulus category in this task. The largest set showed target-positive activity, with the strongest responses to the current target, intermediate activity for a nontarget that was a target on other trials, and lowest activity for nontargets never associated with the target category. Second most frequent was a reverse, antitarget pattern. In the ventrolateral frontal cortex, opponent organization was strongly established in phasic responses at stimulus onset; later, such activity was widely spread across dorsolateral and ventrolateral sites. Task-specific organization into opponent cell groups may be a general feature of prefrontal decision making.

Neuropsychological profile of entrepreneurs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Esteban R. Brenes ◽  
Gabriel Rodríguez ◽  
Joseph Acuña ◽  
Yadira Villalobos ◽  
Caleb A. Pichardo
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Cognitive Flexibility ◽  
Risk Taking ◽  
Design Methodology ◽  
Qualitative Comparative Analysis ◽  
Content Type ◽  
Government Institutions ◽  
Neuropsychological Profile ◽  
Neuropsychological Characteristics

PurposeBy analyzing variables from the fields of business and neuropsychology, this document examines alternative combinations of behavioral economics and neuropsychological characteristics that would explain a successful entrepreneurial profile.Design/methodology/approachThe research is based on information gathered through a survey of 1,080 entrepreneurs. The findings offer interesting perspectives for academics, professionals and government institutions, which illustrate various neuropsychological characteristics that a person must have to be a successful entrepreneur. The method consists of a novel perspective that integrates qualitative comparative analysis (QCAs), a method based on Boolean algebra that offers a study from a configurational perspective.FindingsFrom the mixture of configurations, the paper explores following possible traits of an entrepreneurial mindset: cognitive flexibility, risk-taking, decision-making and teamwork.Originality/valueThis paper contributes to the literature on emerging attempts and approaches to understand the entrepreneurial mindset and the possible skillset that underpins successful entrepreneurship.

Optimizing Supply Chains Through System Dynamics Modelling and Simulation

2021 ◽  
pp. 31-56
Author(s):  
Pedro B. Agua ◽  
Anacleto C. Correia ◽  
Armindo Frias
Keyword(s):  
Decision Making ◽  
Supply Chains ◽  
Organizational Systems ◽  
Complex Behaviour ◽  
Cognitive Limitations ◽  
The Face ◽  
Decision Making Processes ◽  
Dynamics Modelling ◽  
Technical Systems ◽  
Parameters Of Interest

In critical activities and organizations, decision making in the face of complexity has been a growing normal. Complexity troubles humans due to cognitive limitations. Moreover, humans are merely able to understand cause-and-effect relationships that are close in time and space, not the paradigm of many complex socio-technical systems. Decision-making processes shall rely on models that help harness a problem´s associated complexity – among them the dynamics of supply chains. Models typically fall into two broad categories: mental and formal models. Supply chains are complex systems, which may exhibit complex behaviour patterns. Decisions and policies within organizational systems are the causes of many problems, among them undesirable oscillations and other problematic patterns of the parameters of interest. A system is a grouping of parts that work together for a purpose. Hence, the systems dynamics methodology is an adequate approach to deal with fuel supply chain management. A model was developed that helps manage marine gasoil supply chains in the context of the navy.

scholarly journals An evolutionary computational theory of prefrontal executive function in decision-making

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130474 ◽  
Author(s):  
Etienne Koechlin
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Adaptive Behaviour ◽  
Computational Theory ◽  
Critical Reasoning ◽  
Model Free ◽  
Evolutionary Advantage ◽  
Adaptive Processes ◽  
Principled Account ◽  
Selection Of

The prefrontal cortex subserves executive control and decision-making, that is, the coordination and selection of thoughts and actions in the service of adaptive behaviour. We present here a computational theory describing the evolution of the prefrontal cortex from rodents to humans as gradually adding new inferential Bayesian capabilities for dealing with a computationally intractable decision problem: exploring and learning new behavioural strategies versus exploiting and adjusting previously learned ones through reinforcement learning (RL). We provide a principled account identifying three inferential steps optimizing this arbitration through the emergence of (i) factual reactive inferences in paralimbic prefrontal regions in rodents; (ii) factual proactive inferences in lateral prefrontal regions in primates and (iii) counterfactual reactive and proactive inferences in human frontopolar regions. The theory clarifies the integration of model-free and model-based RL through the notion of strategy creation. The theory also shows that counterfactual inferences in humans yield to the notion of hypothesis testing, a critical reasoning ability for approximating optimal adaptive processes and presumably endowing humans with a qualitative evolutionary advantage in adaptive behaviour.

scholarly journals The Allure of High-Risk Rewards in Huntington’s disease

2015 ◽  
Vol 22 (4) ◽  
pp. 426-435 ◽  
Author(s):  
Nelleke C. van Wouwe ◽  
Kristen E. Kanoff ◽  
Daniel O. Claassen ◽  
K. Richard Ridderinkhof ◽  
Peter Hedera ◽  
...  
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Neurodegenerative Disorder ◽  
Low Risk ◽  
Decision Bias ◽  
Clear Preference ◽  
High Reward ◽  
Risk Choice

AbstractObjectives: Huntington’s disease (HD) is a neurodegenerative disorder that produces a bias toward risky, reward-driven decisions in situations where the outcomes of decisions are uncertain and must be discovered. However, it is unclear whether HD patients show similar biases in decision-making when learning demands are minimized and prospective risks and outcomes are known explicitly. We investigated how risk decision-making strategies and adjustments are altered in HD patients when reward contingencies are explicit. Methods: HD (N=18) and healthy control (HC; N=17) participants completed a risk-taking task in which they made a series of independent choices between a low-risk/low reward and high-risk/high reward risk options. Results: Computational modeling showed that compared to HC, who showed a clear preference for low-risk compared to high-risk decisions, the HD group valued high-risks more than low-risk decisions, especially when high-risks were rewarded. The strategy analysis indicated that when high-risk options were rewarded, HC adopted a conservative risk strategy on the next trial by preferring the low-risk option (i.e., they counted their blessings and then played the surer bet). In contrast, following a rewarded high-risk choice, HD patients showed a clear preference for repeating the high-risk choice. Conclusions: These results indicate a pattern of high-risk/high-reward decision bias in HD that persists when outcomes and risks are certain. The allure of high-risk/high-reward decisions in situations of risk certainty and uncertainty expands our insight into the dynamic decision-making deficits that create considerable clinical burden in HD. (JINS, 2016, 22, 426–435)

scholarly journals Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making

2017 ◽  
Author(s):  
Laura Gwilliams ◽  
Jean-Rémi King
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Brain Activity ◽  
Hand Movement ◽  
Temporal Organization ◽  
Natural Image ◽  
Neural Responses ◽  
Perceptual Decision Making ◽  
Healthy Human ◽  
Perceptual Decision

AbstractModels of perceptual decision making have historically been designed to maximally explain behaviour and brain activity independently of their ability to actually perform tasks. More recently, performance-optimized models have been shown to correlate with brain responses to images and thus present a complementary approach to understand perceptual processes. In the present study, we compare how these approaches comparatively account for the spatio-temporal organization of neural responses elicited by ambiguous visual stimuli. Forty-six healthy human subjects performed perceptual decisions on briefly flashed stimuli constructed from ambiguous characters. The stimuli were designed to have 7 orthogonal properties, ranging from low-sensory levels (e.g. spatial location of the stimulus) to conceptual (whether stimulus is a letter or a digit) and task levels (i.e. required hand movement). Magneto-encephalography source and decoding analyses revealed that these 7 levels of representations are sequentially encoded by the cortical hierarchy, and actively maintained until the subject responds. This hierarchy appeared poorly correlated to normative, drift-diffusion, and 5-layer convolutional neural networks (CNN) optimized to accurately categorize alpha-numeric characters, but partially matched the sequence of activations of 3/6 state-of-the-art CNNs trained for natural image labeling (VGG-16, VGG-19, MobileNet). Additionally, we identify several systematic discrepancies between these CNNs and brain activity, revealing the importance of single-trial learning and recurrent processing. Overall, our results strengthen the notion that performance-optimized algorithms can converge towards the computational solution implemented by the human visual system, and open possible avenues to improve artificial perceptual decision making.

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