The competition dynamics of approach and avoidance motivations following interpersonal transgression
Bo Shen,
Yang Chen,
Zhewen He
et al.
Abstract:Two behavioral motivations coexist in transgressors following an interpersonal transgression—approaching and compensating the victim and avoiding the victim. Little is known about how these motivations arise, compete, and drive transgressors’ decisions. The present study adopted a social interaction task to manipulate participants’ (i.e., the transgressor) responsibility for another’s (i.e., the victim) monetary loss and measure the participants’ tradeoff between compensating the victim and avoiding face-to-fa… Show more
“…From an evolutionary perspective, uncertainty in the representation of value poses significant challenges for humans and animals in making accurate decisions. The uncertainty of an option’s value is an inevitable aspect of decision-making and is due to various factors like the stochasticity inherited from the environment (Drugowitsch et al, 2016), uncertain reward associations (Daw et al, 2005; Schlichting & Preston, 2016; Shohamy & Wagner, 2008; Wimmer & Shohamy, 2012), stochastic memory retrieval (Bakkour et al, 2019; Shadlen & Shohamy, 2016), imprecise inferential reasoning (Barron et al, 2020; Xue et al, 2024), and/or fluctuating motivational states (Priestley et al, 2024; Shen, Chen, et al, 2023). Our findings regarding early noise illustrate a special case where noise can be effectively managed by the brain circuit.…”
Section: Discussionmentioning
confidence: 99%
“…No contextual effects would be predicted in this case since the rank of input values would always be preserved when scaled by the same constant. This implies a biological substrate where inhibitory neurons, a biological counterpart of the divisive gain-control from the denominator (Louie et al, 2014; Shen, Louie, et al, 2023; Zimmermann et al, 2018), are distinct pools for different options. This motif is supported by recent discoveries of inhibitory neurons with choice-selective properties (Najafi et al, 2020) and implemented in models like the local disinhibition decision model (LDDM) (Shen, Louie, et al, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…This implies a biological substrate where inhibitory neurons, a biological counterpart of the divisive gain-control from the denominator (Louie et al, 2014; Shen, Louie, et al, 2023; Zimmermann et al, 2018), are distinct pools for different options. This motif is supported by recent discoveries of inhibitory neurons with choice-selective properties (Najafi et al, 2020) and implemented in models like the local disinhibition decision model (LDDM) (Shen, Louie, et al, 2023). Similarly, in the late noise scenario, when the late noise covariance is fully correlated across options, the choice would lose its stochastic nature.…”
Unlike the predictions of normative choice theories, the real-world accuracy of human and animal decision-making depends on context. Observed impairments of choice performance by context can be explained by computations like divisive normalization. However, recent behavioral evidence suggests that context can sometimes enhance choice performance, a finding that challenges existing explanations. Here, we propose and test an extension of existing frameworks for context-dependent choice that incorporates a richer notion of noise. Model simulations show that noise arising before (early noise) and after (late noise) normalization predict opposing effects: early noise can cause contextual information to enhance choice performance while late noise can cause context to degrade choice performance. In experiments with human subjects, we confirm key elements of this model. Manipulating early and late noise - by inducing uncertainty in option values and controlling time pressure – produces dissociable positive and negative effects of context. Together, these findings suggest a new unifying mechanism for contextual modulation, highlighting the significant role of noise source in neural computation and behavior.
“…From an evolutionary perspective, uncertainty in the representation of value poses significant challenges for humans and animals in making accurate decisions. The uncertainty of an option’s value is an inevitable aspect of decision-making and is due to various factors like the stochasticity inherited from the environment (Drugowitsch et al, 2016), uncertain reward associations (Daw et al, 2005; Schlichting & Preston, 2016; Shohamy & Wagner, 2008; Wimmer & Shohamy, 2012), stochastic memory retrieval (Bakkour et al, 2019; Shadlen & Shohamy, 2016), imprecise inferential reasoning (Barron et al, 2020; Xue et al, 2024), and/or fluctuating motivational states (Priestley et al, 2024; Shen, Chen, et al, 2023). Our findings regarding early noise illustrate a special case where noise can be effectively managed by the brain circuit.…”
Section: Discussionmentioning
confidence: 99%
“…No contextual effects would be predicted in this case since the rank of input values would always be preserved when scaled by the same constant. This implies a biological substrate where inhibitory neurons, a biological counterpart of the divisive gain-control from the denominator (Louie et al, 2014; Shen, Louie, et al, 2023; Zimmermann et al, 2018), are distinct pools for different options. This motif is supported by recent discoveries of inhibitory neurons with choice-selective properties (Najafi et al, 2020) and implemented in models like the local disinhibition decision model (LDDM) (Shen, Louie, et al, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…This implies a biological substrate where inhibitory neurons, a biological counterpart of the divisive gain-control from the denominator (Louie et al, 2014; Shen, Louie, et al, 2023; Zimmermann et al, 2018), are distinct pools for different options. This motif is supported by recent discoveries of inhibitory neurons with choice-selective properties (Najafi et al, 2020) and implemented in models like the local disinhibition decision model (LDDM) (Shen, Louie, et al, 2023). Similarly, in the late noise scenario, when the late noise covariance is fully correlated across options, the choice would lose its stochastic nature.…”
Unlike the predictions of normative choice theories, the real-world accuracy of human and animal decision-making depends on context. Observed impairments of choice performance by context can be explained by computations like divisive normalization. However, recent behavioral evidence suggests that context can sometimes enhance choice performance, a finding that challenges existing explanations. Here, we propose and test an extension of existing frameworks for context-dependent choice that incorporates a richer notion of noise. Model simulations show that noise arising before (early noise) and after (late noise) normalization predict opposing effects: early noise can cause contextual information to enhance choice performance while late noise can cause context to degrade choice performance. In experiments with human subjects, we confirm key elements of this model. Manipulating early and late noise - by inducing uncertainty in option values and controlling time pressure – produces dissociable positive and negative effects of context. Together, these findings suggest a new unifying mechanism for contextual modulation, highlighting the significant role of noise source in neural computation and behavior.
“…The order of the conditions was randomized across participants. Previous studies have demonstrated the validity of this task in inducing different levels of perceived guilt, negative self-conscious emotions, and compensatory behaviors 37 – 40 , 42 . Figure 1 Procedure of the interpersonal transgression task and guilt-related measures.…”
Section: Methodsmentioning
confidence: 99%
“…To characterize how people make social trait judgments from faces, we used naturalistic face stimuli of celebrities of diverse races, face angles, gaze directions, and facial expressions taken in naturalistic contexts (e.g., non-posing photos captured in the street or events) 35 and a set of eight traits that summarize the comprehensive dimensions of trait judgments from faces 36 . To measure recognition of responsibility, guilt experience, and guilt-induced behavioral tendencies following interpersonal transgression, we adopted an interpersonal interaction task where we successfully manipulated participants’ responsibility in causing unpleasant outcomes to a social partner 37 – 40 . With these two tasks, we tested whether perception of social traits was associated with socio-emotional experience in interpersonal transgression context, and how such association was altered in people with ASD compared to neurotypical participants.…”
Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others’ faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants’ emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants’ self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others' faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD.
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