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Explore as a Researcher Student or Teacher Member of public. Who we are. Home What we do Our expert advice Expert advice and practice framework. Share our content. Return to top Mandate The Society operates under a private Parliamentary Act to advance and promote science, technology, and the humanities in New Zealand. Return to top The Society's strengths The Society is a non-profit organisation, independent from Government, allowing it to provide a trusted independent voice on matters of research evidence.
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The primary staff member supporting the committee is the Director — Expert Advice and Practice. Return to top Terms of reference Each advice or research practice project has a Terms of Reference, informed and approved in draft by Council, which sets out: The working title of the project The scope and objectives of the project Relevant context Intended Note 1 form of outputs and publication Time frame Consultation and peer review expectations Guidelines around payment of expenses if relevant Any other relevant information EAPC agrees the final terms of reference with the convenor of a deliberative advice panel, or project leader in other cases.
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Our expert advice Expert advice and practice framework. Plastics in the Environment Understanding Aotearoa's plastics problem. First name. These sectors require expertise, analytic assessment of in-depth investigations that could possible include big data sets such as agriculture and climate change. By seeking advice from a specialized expert, you will have the ability to develop solutions based on solid, experience-based information culled from solid facts.
Experts offer advice based on tried out past and current industries practices that provides projects with valuable insights on various challenges. Solving problems not only requires experience but also a definite degree of methodological creativity. By providing fresh perspectives on a particular subject matter qualified consultants can save time, money and avoid possible setbacks.
Expert advice and guidance allows projects to stay on track to achieve set objectives and time frames. Organizational, project and team leaders can effectively initiate, design, implement and carry out projects by engaging expert consultants assistance.
Instead of spending time trying to streamline operations outside their area of expertise, leaders now have the time to do what they do best, whether it is developing marketing strategies, or procuring funding.
Are you looking for expert advice? Maximpact Consulting Network can help. All regressors were convolved with the FSL default gamma hemodynamic response function. To note, our model contains two regressors at the time point where participants utilized advice Time 2 that were modulated by the weight of advice on a trial-by-trial basis R9, R R9 modeled trials in which participants received expert advice, and R10 modeled trials in which participants received novice advice.
These regressors were orthogonalized with respect to the main effect regressors see list above. The goal of including these regressors was to capture any additional, specifically linear parametric variance that was not already modeled in the unmodulated regressors. Analysis with these modulated regressors did not yield significant, reportable results. Individual contrast images were computed and taken to a group-level mixed-effect analysis using voxel-wise one-sample t-tests see below.
Z-statistic images were thresholded with default FSL cluster correction for multiple comparisons with a minimum Z-score set at 2. Parameter estimates were extracted by contrasting indicated regressors against baseline R5, R6, R7, R8; see Figure 3C. In our task, we emulated this by providing a monetary incentive to participants, where they believed they would be rewarded for the accuracy of their estimations. Therefore, when participants made their first estimate and then discovered the advice amount, they calculated an opinion difference that was directly related to the probability that they would receive a reward, depending on how much they valued the advice source.
For example, if the participant valued the advice source and found out there was a high opinion difference, then they would think their estimate needs revision in order to obtain a reward.
This demonstrates the direct relation of the opinion difference to reward. Furthermore, prior behavioral research has shown that advice discounting is affected by monetary reward [3] , [20] , [25]. If the size of the monetary reward affects the weight of advice, it could be that calculation of the opinion difference, which occurs during the only instance that participants receive information from their advisors, is reflected in reward areas.
Therefore, due to the above two lines of reasoning, we hypothesized that the opinion difference would be calculated by brain regions that have previously been established to be reward-sensitive, such as the ventral striatum, amygdala, anterior cingulate gyrus, ventromedial prefrontal cortex and the orbitofrontal cortex [5] — [7] , [9] — [11].
For analysis of neuroimaging data related to the opinion difference, we created a region of interest mask of these reward-sensitive areas. The resulting mask was then smoothed using a mean-filtered kernel of 3.
Because of the involvement of the opinion difference in the contrast, we used the above-described region of interest mask to reveal BOLD signal changes. Parameter estimates were extracted by contrasting indicated regressors against baseline R5, R6, R7, R8; see Figure 5B. Participants also displayed individual differences in how they utilized advice from experts and novices Figure 2B.
Some participants used a similar amount of advice from both expert and novice sources none used more novice than expert , while others displayed a greater use of expert advice compared to novice advice. Importantly, we wanted to keep performance constant over the course of the experiment and therefore did not give feedback to participants about the actual price of the apartments. After the experiment, participants were asked to rate the value of each type of advice on a Likert scale from 1 to 5, with the higher number indicating a higher value.
We hypothesized that the same areas which represent value and reward expectation when receiving money and objects, such as the ventral striatum [6] , also represent value when receiving advice. To address this, we analyzed neural activity when participants discovered that advice would be coming from an expert or a novice Time 1.
Although not directly relevant to our research question, we also compared neural activity in the control condition where participants found out they would not be receiving advice to the experimental condition where they found out they would be receiving advice main effect of advice across the expert and novice conditions.
See Figure S1 and Table S1 for results. To examine brain activity associated with utilizing advice from sources with different levels of expertise, we analyzed brain activity when participants received the advice Time 2.
In addition, we contrasted the control condition where participants did not receive advice and re-evaluated their opinion with the experimental condition where participants used advice both expert and novice conditions. See Figure S2 and Table S2 for results. We hypothesized that the opinion difference would be represented in previously established reward-sensitive regions.
To address this, we analyzed neural activity when participants received advice Time 2 with respect to the size of the opinion difference. We tested the hypothesis that changes in BOLD signal in areas integrating both the expertise level of the advisor and the opinion difference would correlate with the behavioral influence of advice. This analysis reveals any brain region in which the BOLD signal change in response to a change in one factor eg.
There were no significant interactions in the reverse contrast. We had hypothesized that the activity in areas that demonstrate an interaction between the expertise level of an advisor and the opinion difference would correlate with the individual weight of advice.
We then performed a correlation analysis with the parameter estimate and the mean weight of advice across all trials for each participant. In the present study, we designed a task to emulate real world decision making situations where people form an initial opinion, discover they will be receiving advice along with the expertise level of their advisor , receive advice and then adjust their opinion to make a final decision.
To better understand the neurocognitive processes involved in these types of decisions, we varied the expertise level of the advisor and ensured that participants experienced variations in the size of the opinion difference on a trial-by-trial basis. This resulted in participants exhibiting a behavioral change that we quantified with the weight of advice index.
Our behavioral results demonstrate that participants valued expert advice more than novice advice, as indicated in the post-experiment questionnaire. Participants used advice from both groups of advisors, but they used advice from experts more than advice from novices.
This result replicates previous behavioral research demonstrating that people use more advice when it comes from experts [2] — [4]. Before participants received the actual advice, they also displayed greater changes in BOLD signal in the ventral striatum when they discovered that they would be receiving expert advice compared to novice advice. This result agrees with previous research demonstrating that activity in the ventral striatum tracks value through reward anticipation [6] , [26] , [27].
People may value expert advice more because they believe it will enable them to make better decisions with higher value outcomes, even before they receive a specific recommendation. Brain activity at the time of the utilization of expert and novice advice supports this view. Participants demonstrated greater increases in BOLD signal in the medial prefrontal cortex when utilizing expert advice.
This result is in line with previous research demonstrating that activity in this region positively correlates with the value of a chosen option when choosing between options; the higher the expected value of the choice, the higher the activity [28] — [31].
Our behavioral results demonstrate that participants used advice more when the distance between their first estimate and the advice was low.
A recent behavioral study which asked people to estimate historical dates, such as the year the Suez Canal first opened, while either receiving or not receiving advice, found the same result [8]. To note, the size of the difference in the weight of advice index between the high and low opinion difference conditions in this study was comparable to our study, 0. Our neuroimaging results show that the opinion difference is represented in brain regions previously indicated to be involved in reward processing.
When the opinion difference was high, an increased BOLD signal was observed in the lateral orbitofrontal cortex and the ventromedial prefrontal cortex.
When the opinion difference was low, an increased BOLD signal was observed in the ventral striatum and the anterior cingulate cortex. With respect to our anterior cingulate cortex result, it has been well documented that this area is involved in computing rewards during behavioral tasks [10]. Similar to the ventral striatum, our finding that anterior cingulate cortex is more active in low opinion difference trials can be interpreted with regard to this reward literature, although it may be somewhat surprising when considering its role in other previous literature on conflict monitoring and cognitive control [33] , [34].
This difference was small yet significant. Either way, our reaction time data suggest a greater amount of information processing when encountering low opinion differences and agrees with the previous literature on the role of the anterior cingulate cortex [33] , [34] , [36] — [38]. We identified a brain region that represents the behavioral influence of advice by requiring that this region fulfill two conditions.
First, when the participant utilizes advice, the expertise of the advisor and the size of the opinion difference should interact in this area. Second, the activity in this region should correlate with individual differences in advice utilization across participants. We found that activity in the left lateral orbitofrontal cortex fulfilled these requirements. Specifically, we observed this correlation with the average parameter estimate across all advice conditions against baseline.
Thus, our data demonstrate that, across individuals, the greater the average BOLD signal change in the left lateral orbitofrontal cortex during decision making, the greater the influence of advice. Similar to the present study on explicit advice, certain types of implicit influence by celebrities or group opinion have previously been investigated. For example, activity in the anterior cingulate cortex was demonstrated to correlate with the perceived degree of expertise a celebrity has regarding a product [18].
It was shown that the next day after viewing a celebrity paired with a product, the greater the perceived expertise of the celebrity, the greater the intention to purchase the product and the greater the memory for the product. This study provided evidence for the implicit influence of expertise on decision making, and although in the present study we focused on the explicit influence of expertise, our results agree with their behavioral findings, showing that people are more influenced by individuals whom they perceive to have more expertise.
Furthermore, conformity to group opinion has previously been shown to recruit the intraparietal sulcus, temporoparietal junction, insular cortex, anterior cingulate, ventral striatum and the lateral orbitofrontal cortex [15] — [17] , [39] , [40]. Importantly, in the most recent study by Campbell-Meiklejohn et al. Thus, our results concerning advice utilization, taken together with this recent publication on conformity, strongly suggest a role for the lateral orbitofrontal cortex in the computation of social influence.
Previous neuroimaging research has investigated aspects of advice taking that are different from the present study. Neural correlates for receiving advice, compared to not receiving advice, have been demonstrated in the dorsomedial prefrontal cortex and the temporoparietal junction [13].
Furthermore, when making repeated decisions with the same advisor and receiving feedback on decision outcomes, the dorsomedial prefrontal cortex and the temporoparietal junction are active during the outcome period [12]. In this second study, these regions computed a social prediction error allowing a person to learn the trustworthiness of their advisor.
The septal area demonstrated a greater signal change after both positive and negative feedback from recommended choices compared to non-recommended choices. The current study did not demonstrate involvement of similar brain regions. However, this is not surprising because we focused on the differences between using expert and novice advice, and not the differences between making decisions with or without advice. Furthermore, we investigated brain activity at the time participants received advice and related it to behavioral change via the weight of advice index.
Examining brain activity at the time we receive and utilize advice and relating it to the behavioral change caused by the advice is crucial to understanding how we integrate advice into the decision making process. In conclusion, with the present report, we demonstrate how people use advice when making decisions.
We show that advice-taking consists of three neurocognitive processes: the valuation of advice, the assessment of the opinion difference, and the process of combining valuation and the opinion difference resulting in actual advice utilization. This last process was shown to occur in the left lateral orbitofrontal cortex, where the average activity correlates with the mean use of advice across participants.
This result establishes the lateral orbitofrontal cortex as a region of the brain responsible for the behavioral influence of advice. As a whole, our findings provide neural evidence for how advice engenders behavioral change during the decision making process, and advance the overall understanding of how humans use advice. Brain regions showing a main effect between the advice and no advice conditions when participants discovered whom their advisor will be Time 1.
Advice includes both the expert and novice conditions. Brain regions showing a main effect between the advice and no advice conditions when participants received advice Time 2. We thank C. Morawetz and N. Green for discussion, Y. Heussen and H. Bruehl for language translation, A. Hatri and L. Bennett for technical assistance, and D. Schiller for comments on the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
National Center for Biotechnology Information , U. PLoS One. Published online Nov Christoph W. Hauke R. Jean Daunizeau, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Received Jun 13; Accepted Oct This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
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