Dysregulated affective arousal regulates reward-based decision making in patients with schizophrenia: an integrated study.

Schizophrenia is a chronic and severe mental disorder. Dysregulated decision-making and affective processing have been implicated in patients with schizophrenia (SZ) and have significant impacts on their cognitive and social functions. However, little is known about how affective arousal influences reward-based decision-making in SZ. Taking advantage of a two-choice probabilistic gambling task and utilizing three facial expressions as affective primes (i.e., neutral, angry, and happy conditions) in each trial, we investigated how affective arousal influences reward-related choice based on behavioral, model fitting, and feedback-related negativity (FRN) data in 38 SZ and 26 healthy controls (CTRL). We also correlated our measurements with patients' symptom severity. Compared with the CTRL group, SZ expressed blunted responses to angry facial primes. They had lower total game scores and displayed more maladaptive choice strategies (i.e., less win-stay and more lose-shift) and errors in monitoring rewards. Model fitting results revealed that the SZ group had a higher learning rate and lower choice consistency, especially in the happy condition. Brain activity data further indicated that SZ had smaller amplitudes of FRN than their controls in the angry and happy conditions. Importantly, the SZ group exhibited attenuated affective influence on decision-making, and their impairments in decision-making were only correlated with their clinical symptoms in the angry condition. Our findings imply the affective processing is dysregulated in SZ and it is selectively involved in the regulation of choice strategies, choice behaviors, and FRN in SZ, which lead to impairments in reward-related decision-making, especially in the angry condition.

Misfortune may be a blessing in disguise: Fairness perception and emotion modulate decision making.

Fairness perception and equality during social interactions frequently elicit affective arousal and affect decision making. By integrating the dictator game and a probabilistic gambling task, this study aimed to investigate the effects of a negative experience induced by perceived unfairness on decision making using behavioral, model fitting, and electrophysiological approaches. Participants were randomly assigned to the neutral, harsh, or kind groups, which consisted of various asset allocation scenarios to induce different levels of perceived unfairness. The monetary gain was subsequently considered the initial asset in a negatively rewarded, probabilistic gambling task in which the participants were instructed to maintain as much asset as possible. Our behavioral results indicated that the participants in the harsh group exhibited increased levels of negative emotions but retained greater total game scores than the participants in the other two groups. Parameter estimation of a reinforcement learning model using a Bayesian approach indicated that these participants were more loss aversive and consistent in decision making. Data from simultaneous ERP recordings further demonstrated that these participants exhibited larger feedback-related negativity to unexpected outcomes in the gambling task, which suggests enhanced reward sensitivity and signaling of reward prediction error. Collectively, our study suggests that a negative experience may be an advantage in the modulation of reward-based decision making.

Effects of affective arousal on choice behavior, reward prediction errors, and feedback-related negativities in human reward-based decision making.

Emotional experience has a pervasive impact on choice behavior, yet the underlying mechanism remains unclear. Introducing facial-expression primes into a probabilistic learning task, we investigated how affective arousal regulates reward-related choice based on behavioral, model fitting, and feedback-related negativity (FRN) data. Sixty-six paid subjects were randomly assigned to the Neutral-Neutral (NN), Angry-Neutral (AN), and Happy-Neutral (HN) groups. A total of 960 trials were conducted. Subjects in each group were randomly exposed to half trials of the pre-determined emotional faces and another half of the neutral faces before choosing between two cards drawn from two decks with different assigned reward probabilities. Trial-by-trial data were fit with a standard reinforcement learning model using the Bayesian estimation approach. The temporal dynamics of brain activity were simultaneously recorded and analyzed using event-related potentials. Our analyses revealed that subjects in the NN group gained more reward values than those in the other two groups; they also exhibited comparatively differential estimated model-parameter values for reward prediction errors. Computing the difference wave of FRNs in reward vs. non-reward trials, we found that, compared to the NN group, subjects in the AN and HN groups had larger "General" FRNs (i.e., FRNs in no-reward trials minus FRNs in reward trials) and "Expected" FRNs (i.e., FRNs in expected reward-omission trials minus FRNs in expected reward-delivery trials), indicating an interruption in predicting reward. Further, both AN and HN groups appeared to be more sensitive to negative outcomes than the NN group. Collectively, our study suggests that affective arousal negatively regulates reward-related choice, probably through overweighting with negative feedback.

A threshold theory account of psychometric functions with response confidence under the balance condition.

The study of thresholds for discriminability has been of long-standing interest in psychophysics. While threshold theories embrace the concept of discrete-state thresholds, signal detection theory discounts such a concept. In this paper we concern ourselves with the concept of thresholds from the discrete-state modelling viewpoint. In doing so, we find it necessary to clarify some fundamental issues germane to the psychometric function (PF), which is customarily constructed using psychophysical methods with a binary-response format. We challenge this response format and argue that response confidence also plays an important role in the construction of PFs, and thus should have some impact on threshold estimation. We motivate the discussion by adopting a three-state threshold theory for response confidence proposed by Krantz (1969, Psychol. Rev., 76, 308-324), which is a modification of Luce's (1963, Psychol. Rev., 70, 61-79) low-threshold theory. In particular, we discuss the case in which the practice of averaging over order (or position) is enforced in data collection. Finally, we illustrate the fit of the Luce-Krantz model to data from a line-discrimination task with response confidence.

Inferring reward prediction errors in patients with schizophrenia: a dynamic reward task for reinforcement learning.

Abnormalities in the dopamine system have long been implicated in explanations of reinforcement learning and psychosis. The updated reward prediction error (RPE)-a discrepancy between the predicted and actual rewards-is thought to be encoded by dopaminergic neurons. Dysregulation of dopamine systems could alter the appraisal of stimuli and eventually lead to schizophrenia. Accordingly, the measurement of RPE provides a potential behavioral index for the evaluation of brain dopamine activity and psychotic symptoms. Here, we assess two features potentially crucial to the RPE process, namely belief formation and belief perseveration, via a probability learning task and reinforcement-learning modeling. Forty-five patients with schizophrenia [26 high-psychosis and 19 low-psychosis, based on their p1 and p3 scores in the positive-symptom subscales of the Positive and Negative Syndrome Scale (PANSS)] and 24 controls were tested in a feedback-based dynamic reward task for their RPE-related decision making. While task scores across the three groups were similar, matching law analysis revealed that the reward sensitivities of both psychosis groups were lower than that of controls. Trial-by-trial data were further fit with a reinforcement learning model using the Bayesian estimation approach. Model fitting results indicated that both psychosis groups tend to update their reward values more rapidly than controls. Moreover, among the three groups, high-psychosis patients had the lowest degree of choice perseveration. Lumping patients' data together, we also found that patients' perseveration appears to be negatively correlated (p = 0.09, trending toward significance) with their PANSS p1 + p3 scores. Our method provides an alternative for investigating reward-related learning and decision making in basic and clinical settings.

On the limitations of fixed-step-size adaptive methods with response confidence.

The family of (non-parametric, fixed-step-size) adaptive methods, also known as 'up-down' or 'staircase' methods, has been used extensively in psychophysical studies for threshold estimation. Extensions of adaptive methods to non-binary responses have also been proposed. An example is the three-category weighted up-down (WUD) method (Kaernbach, 2001) and its four-category extension (Klein, 2001). Such an extension, however, is somewhat restricted, and in this paper we discuss its limitations. To facilitate the discussion, we characterize the extension of WUD by an algorithm that incorporates response confidence into a family of adaptive methods. This algorithm can also be applied to two other adaptive methods, namely Derman's up-down method and the biased-coin design, which are suitable for estimating any threshold quantiles. We then discuss via simulations of the above three methods the limitations of the algorithm. To illustrate, we conduct a small scale of experiment using the extended WUD under different response confidence formats to evaluate the consistency of threshold estimation.

Relative salience affects the process of detecting changes in orientation and luminance.

This study tests the effect of relative saliency on perceptual comparison and decision processes in the context of change detection in which distinct visual mechanisms process two features (e.g., luminance and orientation). Townsend and Nozawa's (1995) systems factorial technology was used to investigate the process architecture and stopping rule when deciding whether luminance or orientation of a Gabor patch had changed. Experiment 1 found individual differences in decision strategies when we did not control relative saliency. One group of participants adopted co-active processing, and the other group adopted serial self-terminating processing to detect the change signals. When Experiment 2 eliminated the relative saliency, all but one observer adopted parallel processing and followed a self-terminating rule. These results support the relative saliency hypothesis and highlight the fact that observers adopt different change-detection strategies for two features, especially when relative saliency exists between the two feature dimensions.

Applications of nonparametric adaptive methods for simple reaction time experiments.

Adaptive methods are commonly used in psychophysical research for detection and discrimination (see Leek, 2001; Treutwein, 1995, for reviews). In recent years, researchers have investigated via simulations some asymptotic and small-sample properties of two nonparametric adaptive methods-namely, the fixed-step-size up-down (García-Pérez, 1998, 2001) and the (accelerated) stochastic approximation (Faes et al., 2007). In the present article, we extend both methods to the simple reaction time (RT) situation for the measure of signal intensities that elicit certain (fixed) RT percentiles. We focus on extending the following four methods: the stochastic approximation of Robbins and Monro (1951), its accelerated version of Kesten (1958), the transformed up-down of Wetherill (1963), and the "biased coin design" of Durham and Flournoy (1994, 1995). In all simulations, we assume that the RT is Weibull distributed and that there is a linear relationship between the mean RT and its standard deviation. The convergences of the asymptotic and small-sample properties for different starting values, step sizes, and response criteria are systematically investigated.

On measuring the minimum detection time: a simple reaction time study in the time estimation paradigm.

Kornblum's time estimation paradigm, together with the so-called 'race model', provides an appealing alternative for measuring the 'cut-off' which separates 'true' reaction times from anticipatory reaction times. However, the model is not precise enough to reveal the relation between the signal intensity and the 'cut-off'. Accordingly, Kornblum's model is extended with an emphasis on the measure of the 'cut-off'. Another aspect of the extension is to use a parametric method to analyse the data. In particular, it is assumed that the time estimation-induced latency is gamma distributed and the signal-induced latency is Weibull distributed, with the latter shifted by the 'cut-off'. The rationale behind the parametric assumption is discussed. For illustrative purposes, two pieces of experimental work are presented. Since the core of the race model is the assumption of an independent race between the time estimation process and the detection process, the first experiment tests whether, for the same signal intensity, the signal-induced latency distribution is invariant across different time intervals; the second experiment tests whether, for the same time interval, the time estimation-induced latency distribution is invariant across different signal intensity conditions. The data from the second experiment are also used to test various parametric assumptions in the model, which include the signal effect on the 'cut-off'. The new model fits the data well.