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Decision making from economic and signal detection perspectives: development of an integrated framework.

Lynn SK, Wormwood JB, Barrett LF, Quigley KS - Front Psychol (2015)

Bottom Line: Conversely, traditional signal detection approaches to decision making focus on the uncertainty that arises from variability in perceptual signals and typically ignore the influence of outcome value uncertainty.Here, we compare and contrast the economic and signals frameworks that guide research in decision making, with the aim of promoting their integration.We show that an integrated framework can expand our ability to understand a wider variety of decision-making behaviors, in particular the complexly determined real-world decisions we all make every day.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Affective Science Laboratory, Department of Psychology, Northeastern University Boston, MA, USA.

ABSTRACT
Behavior is comprised of decisions made from moment to moment (i.e., to respond one way or another). Often, the decision maker cannot be certain of the value to be accrued from the decision (i.e., the outcome value). Decisions made under outcome value uncertainty form the basis of the economic framework of decision making. Behavior is also based on perception-perception of the external physical world and of the internal bodily milieu, which both provide cues that guide decision making. These perceptual signals are also often uncertain: another person's scowling facial expression may indicate threat or intense concentration, alternatives that require different responses from the perceiver. Decisions made under perceptual uncertainty form the basis of the signals framework of decision making. Traditional behavioral economic approaches to decision making focus on the uncertainty that comes from variability in possible outcome values, and typically ignore the influence of perceptual uncertainty. Conversely, traditional signal detection approaches to decision making focus on the uncertainty that arises from variability in perceptual signals and typically ignore the influence of outcome value uncertainty. Here, we compare and contrast the economic and signals frameworks that guide research in decision making, with the aim of promoting their integration. We show that an integrated framework can expand our ability to understand a wider variety of decision-making behaviors, in particular the complexly determined real-world decisions we all make every day.

No MeSH data available.


Expected value overxfor target and foil options (A) with no perceptual uncertainty, (B) little perceptual uncertainty, and (C) more perceptual uncertainty. Sigma refers to the standard deviations of both signal distributions.
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Figure 6: Expected value overxfor target and foil options (A) with no perceptual uncertainty, (B) little perceptual uncertainty, and (C) more perceptual uncertainty. Sigma refers to the standard deviations of both signal distributions.

Mentions: Expressed as a signal detection issue, the expected value function for a decision in the economic framework reduces to a constant over all perceptual signal values, x, which is the average of the expected values of the options (Figure 6A). Moreover, because there are only two perceptually distinct values of x (one for each option), what is generally a continuous function across all values of x in SDT is reduced here to two discrete points.


Decision making from economic and signal detection perspectives: development of an integrated framework.

Lynn SK, Wormwood JB, Barrett LF, Quigley KS - Front Psychol (2015)

Expected value overxfor target and foil options (A) with no perceptual uncertainty, (B) little perceptual uncertainty, and (C) more perceptual uncertainty. Sigma refers to the standard deviations of both signal distributions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495727&req=5

Figure 6: Expected value overxfor target and foil options (A) with no perceptual uncertainty, (B) little perceptual uncertainty, and (C) more perceptual uncertainty. Sigma refers to the standard deviations of both signal distributions.
Mentions: Expressed as a signal detection issue, the expected value function for a decision in the economic framework reduces to a constant over all perceptual signal values, x, which is the average of the expected values of the options (Figure 6A). Moreover, because there are only two perceptually distinct values of x (one for each option), what is generally a continuous function across all values of x in SDT is reduced here to two discrete points.

Bottom Line: Conversely, traditional signal detection approaches to decision making focus on the uncertainty that arises from variability in perceptual signals and typically ignore the influence of outcome value uncertainty.Here, we compare and contrast the economic and signals frameworks that guide research in decision making, with the aim of promoting their integration.We show that an integrated framework can expand our ability to understand a wider variety of decision-making behaviors, in particular the complexly determined real-world decisions we all make every day.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Affective Science Laboratory, Department of Psychology, Northeastern University Boston, MA, USA.

ABSTRACT
Behavior is comprised of decisions made from moment to moment (i.e., to respond one way or another). Often, the decision maker cannot be certain of the value to be accrued from the decision (i.e., the outcome value). Decisions made under outcome value uncertainty form the basis of the economic framework of decision making. Behavior is also based on perception-perception of the external physical world and of the internal bodily milieu, which both provide cues that guide decision making. These perceptual signals are also often uncertain: another person's scowling facial expression may indicate threat or intense concentration, alternatives that require different responses from the perceiver. Decisions made under perceptual uncertainty form the basis of the signals framework of decision making. Traditional behavioral economic approaches to decision making focus on the uncertainty that comes from variability in possible outcome values, and typically ignore the influence of perceptual uncertainty. Conversely, traditional signal detection approaches to decision making focus on the uncertainty that arises from variability in perceptual signals and typically ignore the influence of outcome value uncertainty. Here, we compare and contrast the economic and signals frameworks that guide research in decision making, with the aim of promoting their integration. We show that an integrated framework can expand our ability to understand a wider variety of decision-making behaviors, in particular the complexly determined real-world decisions we all make every day.

No MeSH data available.