Framing effects: Behavioral dynamics and neural basis

Zheng, Hongming; Wang, X.T.; Zhu, Liqi

doi:10.1016/j.neuropsychologia.2010.06.031

Cited by 30 publications

(18 citation statements)

References 44 publications

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“…Activation in frontal, parietal areas differed between risky and certain choices, but only for the positive and not for the negative frame. Another study compared choices in the positive frame with choices in the negative frame and found that choices in the positive frame were associated with enhanced activity in inferior frontal gyrus, insula and parietal lobe (Zheng et al, 2010). No significantly increased neural activity for choices in the negative frame was reported.…”

Section: Discussionmentioning

confidence: 99%

Neural evidence for description dependent reward processing in the framing effect

Zhang

2014

Front. Neurosci.

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Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the “worse than expected” negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to “better than expected” positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect.

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Section: Discussionmentioning

confidence: 99%

Neural evidence for description dependent reward processing in the framing effect

Zhang

2014

Front. Neurosci.

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“…Moreover, across subjects, orbital and medial prefrontal cortical activity was related to reduced susceptibility to framing effects. This pattern of activation was interpreted in terms of opposing neural systems, with ACC activation consistent with the detection of conflict between analytical responses versus a more emotional amygdala-based system (see also Roiser et al, 2009; Zheng, Wang, & Zhu, 2010). …”

Section: Neurodevelopment and Decision Makingmentioning

confidence: 93%

Dual processes in decision making and developmental neuroscience: A fuzzy-trace model

Reyna

Brainerd

2011

Developmental Review

228

337

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From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals—that reasoning biases emerge with development —have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects—that risk preferences shift when the same decisions are phrases in terms of gains versus losses—emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making—prospect theory—can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes.

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“…Data on its specific role in decision-making under risk are inconsistent in separate studies. Some studies have found that the activity of the insula is related to uncertainty [36], [62] or risk per se [63] and that it may signal the possibility of aversive outcomes [30], [70]–[72]. Other studies have suggested that the insula is also sensitive to reward magnitude [73], [74].…”

Section: Discussionmentioning

confidence: 99%

Is Payoff Necessarily Weighted by Probability When Making a Risky Choice? Evidence from Functional Connectivity Analysis

Rao

Jiang

et al. 2012

PLoS ONE

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How people make decisions under risk remains an as-yet-unresolved but fundamental question. Mainstream theories about risky decision making assume that the core processes involved in reaching a risky decision include weighting each payoff or reward magnitude by its probability and then summing the outcomes. However, recently developed theories question whether payoffs are necessarily weighted by probability when making a risky choice. Using functional connectivity analysis, we aimed to provide neural evidence to answer whether this key assumption of computing expectations holds when making a risky choice. We contrasted a trade-off instruction choice that required participants to integrate probability and payoff information with a preferential choice that did not. Based on the functional connectivity patterns between regions in which activity was detected during both of the decision-making tasks, we classified the regions into two networks. One network includes primarily the left and right lateral prefrontal cortices and posterior parietal cortices, which were found to be related to probability in previous reports, and the other network is composed of the bilateral basal ganglia, which have been implicated in payoff. We also found that connectivity between the payoff network and some regions in the probability network (including the left lateral prefrontal cortices and bilateral inferior parietal lobes) were stronger during the trade-off instruction choice task than during the preferential choice task. This indicates that the functional integration between the probability and payoff networks during preferential choice was not as strong as the integration during trade-off instruction choice. Our results provide neural evidence that the weighting process uniformly predicted by the mainstream theory is unnecessary during preferential choice. Thus, our functional integration findings can provide a new direction for the investigation of the principles of risky decision making.

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Framing effects: Behavioral dynamics and neural basis

Cited by 30 publications

References 44 publications

Neural evidence for description dependent reward processing in the framing effect

Neural evidence for description dependent reward processing in the framing effect

Dual processes in decision making and developmental neuroscience: A fuzzy-trace model

Is Payoff Necessarily Weighted by Probability When Making a Risky Choice? Evidence from Functional Connectivity Analysis

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