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 some inconsistency of results. 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 a bias to categorize faces as angry would maximize point earnings. Forty participants (45% women) received 30 IU intranasal oxytocin or placebo before testing. Oxytocin was hypothesized to be associated with insufficient bias, due to an underestimation of the factors creating risk, the encounter rate and cost. Men given oxytocin were less influenced by cost and base rate, exhibiting a less liberal (i.e., worse) response bias, than men given placebo (p<0.037). 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. Oxytocin pharmacotherapy may only be helpful when patients exhibit an overly-liberal threat detection bias. 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. 2013. Gender differences in oxytocin-associated disruption of decision bias during emotion perception. 20th Annual Meeting of the Cognitive Neuroscience Society, 13-16 April 2013, San Francisco, California.

*These authors contributed equally to the study.

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These data also presented at ACNP 2012.

Oxytocin influences response bias in men but not women in a signal detection emotion perception task

Background: Accumulating studies document changes in the perception of and response to socially mediated information, such as facial expressions, with administration of oxytocin. Across studies, however, its effects on emotion perception have been inconsistent. Outside the laboratory, affective judgments about another person (e.g., Is that person angry at me?) involve interpretation of perceptual uncertainty (e.g., scowls do not always indicate anger) and assessment of behavioral risk (e.g., the costs of inferring anger when it does not exist differ from the costs of missing anger when it does exist). An account of these decision variables is missing from studies of the effects of oxytocin on emotion perception. To characterize oxytocin’s effects on emotion perception from a decision-making perspective, we utilized a task that combines perceptual uncertainty with behavioral economics in a signal detection framework. We used the probability of encountering angry faces and the cost of misidentifying them as not angry (risk) to create a biased (biased towards reporting anger when not sure) perceptual environment. We measured the effect of oxytocin on perceivers’ ability to achieve optimal bias in this environment. Based on prior data suggesting that oxytocin attenuates risk aversion, we hypothesized that receiving oxytocin would result in insufficient bias, due to under-estimating the probability of encountering angry faces and/or to under-valuing the cost of mistakes relative to placebo. Methods: Forty psychotropic-free healthy control participants (age: M=44.0 ± 10.32 [SD] years, 45% women) participated in a randomized double-blind administration of intranasal oxytocin or placebo prior to computer based tasks at the Center for Anxiety and Traumatic Stress Disorders (CATSD) at Massachusetts General Hospital. All were free of psychiatric disorders, per clinical interview with the Structured Clinical Interview for DSM-IV. Participants were given 30 IU of double-blind intranasal oxytocin (Syntocinon®, Novartis) or placebo (oxytocin: n=22, 9 women; placebo: n=18, 7 women) thirty minutes before the computer tasks. In this signal detection framework, faces that depicted expressions ranging from relaxed to strongly scowling comprised two categories: “angry” (targets) and “not angry” (foils). Uncertainty was implemented by creating distributions of targets and foils which shared exemplars (i.e., the distributions overlapped on the perceptual domain: targets were M = 60 ± 15% (1 SD) scowl intensity, foils were M = 40 ± 15% (1 SD) scowl intensity). Risk was created by earning or losing points for correct vs. incorrect categorization of targets and foils (i.e., categorizing a target as “not angry” cost more points than categorizing a foil as “angry”). Additionally, the base rate of targets was 0.6 (60% of trials were targets). The combination of relatively high missed detection cost and relatively frequent targets dictated a liberal optimal bias: a tendency to categorize faces as angry was required to maximize points earned. Over 230 trials, participants attempted to optimize their categorization of the faces, answering the on-screen prompt “Is this person angry?”. Participants received immediate on-screen feedback (“Yes – that was right” or “No – that was wrong”, points earned for the current trial, and cumulative points earned). Results: Controlling for baseline perceived stress (Perceived Stress Scale) and trait anxiety (State Trait Anxiety Index, Trait Total Score) in this non-psychiatrically ill sample, we found a significant interaction of drug and gender on response bias (ANCOVA, F(1,32)=4.1, p<0.049), without main effects. Men who received oxytocin exhibited a significantly less liberal (less optimal, based on experimental parameters) bias for perception of anger in faces than those who received placebo (follow-up ANCOVA among men, F(1,20)=5.0, p<0.037). In contrast, women's bias was not significantly affected by oxytocin (follow-up ANCOVA among women, F(1,12)=0.6, p>0.4). Discussion: Participants attempted to optimize their judgments about angriness depicted in facial expressions, in the context of experimenter-defined values of target-foil perceptual similarity, payoffs (points earned/lost), and “anger” base rate. Men given oxytocin appeared less able to calibrate their emotion perception to the signal detection parameters that cause bias (payoffs, base rate, or both). As a learning experiment, our results suggest that oxytocin may impair men’s ability to optimally adapt emotion perception (e.g., judgments of angriness from faces) to differences in risk and uncertainty that characterize different social contexts, while there was no effect of oxytocin for women. These data suggest that oxytocin might reduce (normalize) over-estimates of the base rate of threat or reduce (normalize) over-estimates of the magnitude of punishments that otherwise might contribute to excessive social withdrawal or reduced social approach behaviors. We cannot rule out, however, that by reducing the salience of risk, oxytocin treatment in men could potentially promote risk-prone decision-making in domains outside a patient’s core symptomatology. More research is needed to understand the potential role and possible side effects of oxytocin in interventions.

Simon, N. M., Lynn, S. K., Hoge, E. A., Fischer, L. E., and Barrett, L. F. 2012. Oxytocin influences response bias in men but not women in a signal detection emotion perception task. 51st Annual Meeting of the American College of Neuropsychopharacology, December 2-6, 2012, Hollywood Florida.

These data also presented at 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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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.

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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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Learning to avoid aposematic prey

The evolution of prey warning colouration is, literally, a text-book example of Darwinian adaptive evolution by natural selection. The cornerstone of this evolutionary process is a predation event, the dynamics of which are poorly understood. Aposematic (warningly-coloured) prey are relatively unpalatable and their conspicuous appearance should enable predators to avoid them, but such is not always the case. It has been assumed, based on models of conditioned learning, that the number of aposematic prey that a predator will attack as it learns to avoid such prey should be constant or declining as the prey’s abundance increases. However, empirical studies have instead shown that predators make greater numbers of attacks on aposematic prey when those prey are more common. I show that this failure of theory to predict behaviour likely arises from limitations of the learning models in question. Rather than mechanistic models of conditioned learning, I use signal detection theory to provide a functional characterization of the response uncertainty encountered by inexperienced predators. This characterization explains otherwise puzzling data on aposeme predation and can offer insight on the selective pressures driving the evolution of aposematism and mimicry.

Lynn, S.K. 2005. Learning to avoid aposematic prey. Animal Behaviour 70(5):1221-1226.

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Peak shift discrimination learning as a mechanism of signal evolution

“Peak shift” is a behavioral response bias arising from discrimination learning in which animals display a directional, but limited, preference for or avoidance of unusual stimuli. Its hypothesized evolutionary relevance has been primarily in the realm of aposematic coloration and limited sexual dimorphism. Here, we develop a novel functional approach to peak shift, based on signal detection theory, which characterizes the response bias as arising from uncertainty about stimulus appearance, frequency, and quality. This approach allows the influence of peak shift to be generalized to the evolution of signals in a variety of domains and sensory modalities. The approach is illustrated with a bumblebee (Bombus impatiens) discrimination learning experiment. Bees exhibited peak shift while foraging in an artificial Batesian mimicry system. Changes in flower abundance, color distribution, and visitation reward induced bees to preferentially visit novel flower colors that reduced the risk of flower-type misidentification. Under conditions of signal uncertainty, peak shift results in visitation to rarer, but more easily distinguished, morphological variants of rewarding species in preference to their average morphology. Peak shift is a common and taxonomically wide-spread phenomenon. This example of the possible role of peak shift in signal evolution can be generalized to other systems in which a signal receiver learns to make choices in situations in which signal variation is linked to the sender’s reproductive success.

Lynn, S.K., J. Cnaani, and D.R. Papaj. 2005. Peak shift discrimination learning as a mechanism of signal evolution. Evolution 59(6):1300-1305.

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The Signals Approach to Decision-making in Behavioral Ecology

The “signals approach” is an articulation of signal detection theory (SDT) as a model of decision-making in behavioral ecology. Though previous models of decision-making have taken into account variation in the quality of resources among which choices are made, variation in cues that signal quality has remained unaddressed. Treating stimuli as signals, accounting for stimulus variation as a source of uncertainty, reveals that such variation can have significant consequences on choice behavior. The signals approach functions alongside traditional models to produce a more full understanding of decision making. Here, I apply SDT in novel ways to predator response to aposematic prey, mimicry, discrimination learning, and sexual selection.

Using data from existing literature, I show that the signals approach offers an account of predator response to aposematic prey alternative to traditional explanations based on associative learning. The mistakes that predators make may be better characterized as “false alarm” attacks rather than due to poor associative learning. Under SDT, the number of false alarms is expected to rise as aposematic prey abundance rises from rare to moderate levels. This increase in attacks is contrary to expectations based on associative learning, wherein the mistakes are expected to decrease or remain constant. SDT explains otherwise enigmatic empirical data.

I develop a novel expression of SDT by questioning the “integrated signals” assumption. Changing this assumption extends the applicability of signal detection theory, providing a model of generalization and discrimination learning. This model is contrasted to associative learning and yields a novel explanation of the “peak shift” phenomenon. Peak shift can be characterized as a directional preference for novel stimuli under conditions of signal uncertainty.

In flower discrimination learning experiments designed within a signal detection framework, bumblebees (Bombus impatiens) demonstrated peak shift. Peak shift has the potential to act as an agent of selection; pollinator selection of flower morphology and sexual selection of exaggerated traits provide examples.

As a model of decision-making, signal detection theory can yield insight into receiver (e.g., predator) choice behavior and the consequences of that choice behavior on the subsequent evolution of the signals (e.g., prey appearance) upon which decisions are made.

Lynn, S.K. 2003. The Signals Approach to Decision-making in Behavioral Ecology. Ph.D. dissertation, Univerisity of Arizona.

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