Optimizing Signal Detection: A Parametric Approach to Assessment and Training

Principal investigator: Spencer Lynn
Source: US Army Research Institute for the Behavioral and Social Sciences
Award: W911NF-16-1-0192
Dates: 5/16/16-5/15/17
Amount: $89,978

Can inaccuracies in a person’s subjective “cognitive model” of the operational environment be identified and corrected, to improve decision making? In our prior ARI-funded work we developed a signal detection theory (SDT) framework to define and manipulate environmental parameters in a social threat perception task and to measure individual differences predictive of threat detection abilities. Here, we propose to extend that work, developing means to quantitatively assess perceivers’ cognitive model of the environment, provide individually tailored training targeting a person’s environmental parameter “misestimate,” and describe neurophysiological (EEG) correlates of parameter estimation and training effectiveness. In a one-year project, 100 participants will complete a baseline social-threat perception test. Results will determine individual vulnerabilities to misestimating three environmental parameters known from SDT to control threat detection effectiveness. Subsequently, participants will receive a training protocol and a retest. We hypothesize that participants who receive training specific to their misestimated parameter will show greater improvement than participants who receive training on an accurately estimated parameter. We will assess how executive function and personality traits may modulate the efficacy of neurophysiological measures as putative markers of parameter estimation and training effectiveness. We will address High Priority Research Questions concerning: assessing learning processes and learner status to tailor training individually, linking constructs of adaptability to job performance, and determining neurophysiological individual differences related to core military skills. Therefore, if successful this project could transition to applications relevant to Army objectives in Learning in Formal & Informal Environments, Personnel Testing & Performance, and Psychophysiology of Individual Differences.

Working memory capacity is associated with optimal adaptation of response bias to perceptual sensitivity in emotion perception

Emotion perception, inferring the emotional state of another person, is a frequent judgment made under perceptual uncertainty (e.g., a scowling facial expression can indicate anger or concentration) and behavioral risk (e.g., incorrect judgment can be costly to the perceiver). Working memory capacity (WMC), the ability to maintain controlled processing in the face of competing demands, is an important component of many decisions. We investigated the association of WMC and anger perception in a task in which “angry” and “not angry” categories comprised overlapping ranges of scowl intensity, and correct and incorrect responses earned and lost points, respectively. Participants attempted to earn as many points as they could; adopting an optimal response bias would maximize decision utility. Participants with higher WMC more optimally tuned their anger perception response bias to accommodate their perceptual sensitivity (their ability to discriminate the categories) than did participants with lower WMC. Other factors that influence response bias (i.e., the relative base rate of angry vs. not angry faces and the decision costs & benefits) were ruled out as contributors to the WMC- bias relationship. Our results suggest that WMC optimizes emotion perception by contributing to perceivers’ ability to adjust their response bias to account for their level of perceptual sensitivity, likely an important component of adapting emotion perception to dynamic social interactions and changing circumstances.

Lynn, S. K., Ibagon, C., Bui, E., Palitz, S., Simon, N. M., & Barrett, L. F. (2016). Working memory capacity is associated with optimal adaptation of response bias to perceptual sensitivity in emotion perception. Emotion 16(2):155-163.

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

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.

Lynn, S. K.*, Wormwood, J.B.*, Barrett, L. F., & Quigley, K. S. (In press). Decision making from economic and signal detection perspectives: Development of an integrated framework. Frontiers in Psychology. DOI: 10.3389/fpsyg.2015.00952.

*These authors contributed equally to the study.

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“Utilizing” signal detection theory

What do inferring what a person is thinking or feeling, judging a defendant’s guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A “utilized” signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework.

Lynn, S.K, and L.F. Barrett. 2014. “Utilizing” signal detection theory. Psychological Science, 25(9):1663–1673.

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Gender differences in oxytocin-associated disruption of decision bias during emotion perception

Oxytocin is associated with differences in the perception of and response to socially mediated information, such as facial expressions. Across studies, however, oxytocin’s effect on emotion perception has been inconsistent. Outside the laboratory, emotion perception involves interpretation of perceptual uncertainty and assessment of behavioral risk. An account of these factors is largely missing from studies of oxytocin’s effect on emotion perception and might explain inconsistent results across studies. Of relevance, studies of oxytocin’s effect on learning and decision-making indicate that oxytocin attenuates risk aversion. We used the probability of encountering angry faces and the cost of misidentifying them as not angry to create a risky environment wherein bias to categorize faces as angry would maximize point earnings. Consistent with an underestimation of the factors creating risk (i.e., encounter rate and cost), men given oxytocin exhibited a worse (i.e., less liberal) response bias than men given placebo. Oxytocin did not influence women’s performance. These results suggest that oxytocin may impair men’s ability to adapt to changes in risk and uncertainty when introduced to novel or changing social environments. Because oxytocin also influences behavior in non-social realms, oxytocin pharmacotherapy could have unintended consequences (i.e., risk-prone decision-making) while nonetheless normalizing pathological social interaction.

Lynn, S. K.*, Hoge, E. A.*, Fischer, L. E., Barrett, L. F., and Simon, N. M. 2014. Gender differences in oxytocin-associated disruption of decision bias during emotion perception. Psychiatry Research 219, 198-203. DOI:10.1016/j.psychres.2014.04.031

*These authors contributed equally to the study.

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These data also presented at ACNP 2012 and CNS 2013.

Optimizing Threat Detection Under Signal-Borne Risk

Principal investigator: Spencer Lynn
Source: US Army Research Institute for the Behavioral and Social Sciences
Contract: W5J9CQ-12-C-0028
Dates: 9/27/12-9/26/15
Amount: $434,499

Emotion perception research has revealed marked variability in people’s abilities to infer the emotional states of others. This variability is a function of (i) the uncertainty and risk in the environment inherent to perception (perceivers cannot be certain about what they are experiencing, and errors of perception may be costly) and (ii) factors internal to individual perceivers (physical and psychological states and traits). Using a novel utility-based signal detection framework, we will examine how individual differences in affective reactivity, executive function, and motivation contribute to this variability in perception and decision-making, under conditions of changing environmental uncertainty and risk.

Affective state influences perception by affecting decision parameters underlying bias and sensitivity

Studies of the effect of affect on perception often show consistent directional effects of a person’s affective state on perception. Unpleasant emotions have been associated with a “locally focused” style of stimulus evaluation, and positive emotions with a “globally focused” style. Typically, however, studies of affect and perception have not been conducted under the conditions of perceptual uncertainty and behavioral risk inherent to perceptual judgments outside the laboratory. We investigated the influence of perceivers’ experienced affect (valence and arousal) on the utility of social threat perception by combining signal detection theory and behavioral economics. We compared 3 perceptual decision environments that systematically differed with respect to factors that underlie uncertainty and risk: the base rate of threat, the costs of incorrect identification threat, and the perceptual similarity of threats and nonthreats. We found that no single affective state yielded the best performance on the threat perception task across the 3 environments. Unpleasant valence promoted calibration of response bias to base rate and costs, high arousal promoted calibration of perceptual sensitivity to perceptual similarity, and low arousal was associated with an optimal adjustment of bias to sensitivity. However, the strength of these associations was conditional upon the difficulty of attaining optimal bias and high sensitivity, such that the effect of the perceiver’s affective state on perception differed with the cause and/or level of uncertainty and risk.

Lynn, SK, X Zhang, & LF Barrett. 2012. Affective state influences perception by affecting decision parameters underlying bias and sensitivity. Emotion 12(4):726-736.

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The Utility of Threat Detection in Generalized Social Anxiety Disorder

Principal investigators (multi-PIs): Spencer Lynn, Naomi Simon
Source: National Institute of Mental Health
Award: R01 MH093394-01
Dates: 8/1/11-4/30/16
Amount: $1,954,208


Summary: During social interactions, we look into the face of another person and in the blink of an eye infer that person’s emotional state and their intentions. These perceptions inform decisions about what to do or say next. Generalized Social Anxiety Disorder (GSAD) is characterized by exaggerated concerns about negative evaluation and rejection in social situations. These symptoms have been quantified with signal detection theory (SDT). The application of SDT has led to novel approaches within anxiety research; a primary hypothesis, supported by several studies, has been that the “over-reactive” nature of the anxious state can be characterized as a bias to respond to or remember situations as more threatening than they in fact are. In spite of SDT’s power, its conventional use has been limited to simply quantifying differences in sensitivity, bias, and accuracy among perceivers. Left unanswered are questions of particular relevance to research and treatment: what causes the observed differences in bias and sensitivity? A critical barrier to answering this question is the current understanding of SDT in clinical research, which lacks a framework to predict or explain behavior, or in which to pose experimental questions about how mood and anxiety disorders influence the underlying mechanisms involved in threat perception. To bridge this barrier, we introduce a mathematical model of perceptual decision making that incorporates key insights from behavioral economics-utility and optimality- into a signal detection framework. Our primary objective is to use this novel framework to explain differences in threat perception among individuals with GSAD, anxious controls with generalized anxiety disorder (GAD), and non-psychiatrically-ill participants. Our secondary objective is to assess whether our framework could be used to improve interventions to reduce misperceptions of threat in GSAD. Our model is a unique conceptualization of perception (e.g., optimal detection, subjective miscalibration to underlying environmental parameters that influence overt behavior) that could eventually lead to improvements in cognitive-behavioral therapies by tailoring them to a patient’s individual perceptual decision-making impairment. To achieve our aims, we will recruit 100 individuals with GSAD and 100 individuals each from age- and gender-matched GAD and healthy populations. Participants will complete a suite of perceptual tasks to isolate which of several perceptual decision parameters cause misperceptions of social threat in GSAD. Successful characterization of GSAD along such lines will take the field in new directions by framing social threat perception as a decision made by attempting to optimize detection in the presence ambiguous sensory information and conflicting, risky consequences. As well, the novel theoretical developments represented by our model will broaden SDT’s usefulness deepening the insights it affords into the nature of cognitive processes.

Public Health Relevance: Generalized social anxiety disorder (GSAD) is characterized by frequent, debilitating misperceptions of threat and disapproval in non-threatening social circumstances. This research uses a novel theory and method for characterizing various pathways for disordered threat detection in GSAD. The findings will enable clinicians to build more effective behavioral and cognitive therapies by tailoring therapy to target an individual patient’s particular pathways to perceptual impairments.

Decision-Making and Learning: The Peak Shift Behavioral Response

[Excerpt] Peak shift is taxonomically widespread: exhibited by birds; mammals, including humans; fish; and at least some arthropods. The phenomenon thus appears to reflect uni- versal attributes of generalization, discrimination learning, and choice-making behavior. As such, peak shift is a ‘model’ type of decision making, suitable for comparative study at functional and mechanistic levels. Using peak shift as a tractable example of decision making, a variety of organisms can be studied, with strengths differentially well suited to phylogenetic, behavioral, neural, cellular, or molecular investigations.

In addition to being well suited to study at multiple levels, considerations of peak shift go beyond what is typically investigated in research on decision making. Many models of behavioral economics maximize utility: these models consider variability in (1) the costs and benefits of obtaining resources, and how those payoffs change with body state, and (2) the probability of encountering resources of some quality. Game theoretic approaches additionally account for the effect of others’ responses on the decision maker’s own behavior. However, these models overlook the fact that an animal’s estimates of a resource’s payoff and probability are based on sensory signals emitted by the resource. Outside of the laboratory, signals, such as color or tail length, vary. This variation may exist indepen- dently of any variation in the information encoded by the signals. For example, a signal that indicates a particular food quality (yellow skin on a banana signals ripeness) may vary even if the food quality itself does not (ten bananas of the same ripeness may not share the same yellow color). Typical utility optimization approaches account for variance in resource quality, not variance in the stimuli that signal that quality. Since real world signals are noisy, our understanding of choice behavior will be incomplete with- out accounting for signal variation and uncertainty. As a signal detection issue, peak shift experiments present an opportunity to investigate the role of this signal-borne risk in decision making and its interactions with those aspects of decision making more commonly investigated.

Lynn, S.K. 2010. Decision-making and learning: The peak shift behavioral response. In M. Breed & J. Moore (Eds.), Encyclopedia of Animal Behavior (Vol. 1, pp. 470-475). Oxford: Academic Press. DOI: 10.1016/B978-0-08-045337-8.00146-7

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Cognition and evolution: learning and the evolution of sex traits.

The evolution of gender characteristics is an outcome of mate choice, which has been assumed to be genetically mediated. Recent research suggests that learning also has a role to play as an agent of sexual selection.

Lynn, S.K. 2006. Cognition and evolution: learning and the evolution of sex traits. Current Biology 16(11):421-423.

(Invited Dispatch item)

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