Conflict in moral and nonmoral decision making: an empirical study coupled with a computational model.

While moral psychology research has extensively studied decision making using moral dilemmas, such high-conflict situations may not fully represent all moral decisions. Moreover, most studies on the effect of conflict have focused on nonmoral decisions, and it is unclear how it applies to the moral realm. The present mixed-method research investigates how conflict impacts moral compared to nonmoral decision making. In a preregistered empirical study ( N = 42 ), participants made moral and nonmoral decisions with varying levels of conflict while their mouse trajectories were recorded. Results indicate that moral decisions were more stable in the presence of conflict, while still seeking compromise. In addition, decisions were more affected when conflict got higher. Mouse-tracking data further indicate that some factors are impacting the decision process earlier than others, supporting the relevance of tracing methods to dig into finer-grained decision dynamics. We also present a computational model that aims to capture decision mechanisms and how conflict and morality influence decision making. The model uses dynamic neural fields coupled with sensorimotor control to map a continuous decision space. Two model versions were compared: one with greater perceptual weight for moral information, and another with earlier processing of moral versus nonmoral information. The simulated data more successfully reproduced empirical patterns for the second version, thus providing insights into the underlying decision processes for both moral and nonmoral decisions, in the presence of conflict or not.

When ethics also matter: Influence of taste, health, and ethical attributes on food decisions traced with a novel mouse-tracking paradigm.

Understanding which food attributes influence food decisions is a matter of public health and a lever for interventions promoting healthy diets. Research shows that food decisions are strongly influenced by taste, with health having a weaker and later influence in the food decision process. Yet, the influence of other food attributes and specifically ethical attributes in food decision processes-as traceable in mouse-tracking data-has not been investigated. Furthermore, past research tracing food decision processes with classical mouse-tracking tools has artificially reduced the occurrence of neutral food items, particularly on the taste attribute. This represents an important limitation as neutral items on taste are particularly likely to be influenced by higher-order level attributes, such as health, but also ethics. Extending previous research, two preregistered studies (Study 1, N = 77; Study 2, N = 92) aimed at filling these gaps using a novel one-dimensional mouse-tracking paradigm. Results showed that taste, health, and ethics all influenced food decisions and interacted over time during decision processes. Taste still had the strongest influence, hence replicating previous findings with the present novel mouse-tracking paradigm. Of importance, ethics and health also influenced decisions-and sometimes had an early significant effect-especially for food items rated as neutral on taste. Beyond these effects and taking full advantage of the use of mixed effects models for all analyses, graphical representations of the influence of taste, health, and ethical attributes for all individual food items were provided. Results are discussed considering previous findings and suggested levers for interventions.

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.

Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale-for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation.

The Effects of Intent, Outcome, and Causality on Moral Judgments and Decision Processes.

Over the past decade, moral judgments and their underlying decision processes have more frequently been considered from a dynamic and multi-factorial perspective rather than a binary approach (e.g., dual-system processes). The agent's intent and his or her causal role in the outcome-as well as the outcome importance-are key psychological factors that influence moral decisions, especially judgments of punishment. The current research aimed to study the influence of intent, outcome, and causality variations on moral decisions, and to identify their interaction during the decision process by embedding the moral scenarios within an adapted mouse-tracking paradigm. Findings of the preregistered study (final n = 80) revealed main effects for intent, outcome, and causality on judgments of punishment, and an interaction between the effects of intent and causality. We furthermore explored the dynamics of these effects during the decision process via the analysis of mouse trajectories in the course of time. It allowed detecting when these factors intervened during the trial time course. The present findings thus both replicate and extend previous research on moral judgment, and evidence that, despite some ongoing challenges, mouse-tracking represents a promising tool to investigate moral decision-making.

Fear of Missing Out Predicts Distraction by Social Reward Signals Displayed on a Smartphone in Difficult Driving Situations.

Smartphones are particularly likely to elicit driver distraction with obvious negative repercussions on road safety. Recent selective attention models lead to expect that smartphones might be very effective in capturing attention due to their social reward history. Hence, individual differences in terms of Fear of Missing Out (FoMO) - i.e., of the apprehension of missing out on socially rewarding experiences - should play an important role in driver distraction. This factor has already been associated with self-reported estimations of greater attention paid to smartphones while driving, but the potential link between FoMO and smartphone-induced distraction has never been tested empirically. Therefore, we conducted a preliminary study to investigate whether FoMO would modulate attentional capture by reward distractors displayed on a smartphone. First, participants performed a classical visual search task in which neutral stimuli (colored circles) were associated with high or low social reward outcomes. Then, they had to detect a pedestrian or a roe deer in driving scenes with various levels of fog density. The social reward stimuli were displayed as distractors on the screen of a smartphone embedded in the pictures. The results showed a significant three-way interaction between FoMO, social reward distraction, and task difficulty. More precisely, under attention-demanding conditions (i.e., high-fog density), individual FoMO scores predicted attentional capture by social reward distractors, with longer reaction times (RTs) for high rather than low social reward distractors. These results highlight the importance to consider reward history and FoMO when investigating smartphone-based distraction. Limitations are discussed, notably regarding our sample characteristics (i.e., mainly young females) that might hamper the generalization of our findings to the overall population. Future research directions are provided.

Reward history modulates perceptual load effects.

The reward history of a stimulus can yield strong attentional selection biases. Indeed, attentional capture can be triggered by previously rewarded items which are neither salient nor relevant for the ongoing task, even when selection is clearly counter-productive to actually obtain the reward outcome. Therefore, value-driven attentional capture (VDAC) has been argued to be an automatic attention mechanism. Our study aimed at putting the VDAC automaticity directly to the test. For this purpose, the Load Theory offers a comprehensive framework where distraction is observed under low but not high perceptual load condition. Nevertheless, if VDAC is indeed automatic, distraction by reward-stimuli should be observed on both perceptual load conditions. We used a feature vs. conjunction discrimination of a go/no-go cue to manipulate perceptual load. As expected, our results revealed that perceptual load decreased interference produced by low-reward distractor. However, this effect was not significant for high-reward distractor, giving support to VDAC automaticity. We discussed our results in light of the Load Theory literature and we strongly encourage to consider reward history along with perceptual load in determining attentional capture.

Looking with the (computer) mouse: How to unveil problem-solving strategies in matrix reasoning without eye-tracking.

Problem-solving strategies in visual reasoning tasks are often studied based on the analysis of eye movements, which yields high-quality data but is costly and difficult to implement on a large scale. We devised a new graphical user interface for matrix reasoning tasks where the analysis of computer mouse movements makes it possible to investigate item exploration and, in turn, problem-solving strategies. While relying on the same active perception principles underlying eye-tracking (ET) research, this approach has the additional advantages of being user-friendly and easy to implement in real-world testing conditions, and records only voluntary decisions. A pilot study confirmed that embedding items of Raven's Advanced Progressive Matrices (APM) in the interface did not significantly alter its psychometric properties. Experiment 1 indicated that mouse-based exploration indices, when used to assess two major problem-solving strategies in the APM, are related to final performance-as has been found in past ET research. Experiment 2 suggested that constraining some features of the interface favored the adoption of the more efficient solving strategy for some participants. Overall, the findings support the relevance of the present methodology for accessing and manipulating problem-solving strategies.

Neurophysiology of visually guided eye movements: critical review and alternative viewpoint.

In this article, we perform a critical examination of assumptions that led to the assimilation of measurements of the movement of a rigid body in the physical world to parameters encoded within brain activity. In many neurophysiological studies of goal-directed eye movements, equivalence has indeed been made between the kinematics of the eyes or of a targeted object and the associated neuronal processes. Such a way of proceeding brings up the reduction encountered in projective geometry when a multidimensional object is being projected onto a one-dimensional segment. The measurement of a movement indeed consists of generation of a series of numerical values from which magnitudes such as amplitude, duration, and their ratio (speed) are calculated. By contrast, movement generation consists of activation of multiple parallel channels in the brain. Yet, for many years, kinematic parameters were supposed to be encoded in brain activity, even though the neuronal image of most physical events is distributed both spatially and temporally. After explaining why the "neuronalization" of such parameters is questionable for elucidating the neural processes underlying the execution of saccadic and pursuit eye movements, we propose an alternative to the framework that has dominated the last five decades. A viewpoint is presented in which these processes follow principles that are defined by intrinsic properties of the brain (population coding, multiplicity of transmission delays, synchrony of firing, connectivity). We propose reconsideration of the time course of saccadic and pursuit eye movements as the restoration of equilibria between neural populations that exert opposing motor tendencies.

Differential effects of visual uncertainty and contextual guidance on perceptual decisions: Evidence from eye and mouse tracking in visual search.

Visual search can be seen as a decision-making process that aims to assess whether a target is present or absent from a scene. In this perspective, eye movements collect evidence related to target detection and verification to guide the decision. We investigated whether, in real-world scenes, target detection and verification are differentially recruited in the decision-making process in the presence of prior information (expectations about target location) and perceptual uncertainty (noise). We used a mouse-tracking methodology with which mouse trajectories unveil components of decision-making and eye-tracking measures reflect target detection and verification. Indoor scenes were presented, including a target in usual or unusual locations or no target, and were degraded with additive noise (or no noise). Participants had to respond to the target's presence or absence. Degrading the scene delayed the decision due to increased verification times and reduced mouse velocity. Targets in unusual locations delayed the decision and deviated mouse trajectories toward the target-absent response. Detection times played a major role in these effects. Thus, target detection and verification processes influence decision-making by integrating the available sources of information differently and lead to an accumulation of evidence toward both the presence of a target and its absence.

Tracking and simulating dynamics of implicit stereotypes: A situated social cognition perspective.

Adopting a situated social cognition perspective, we relied on different methodologies-1 computational and 3 empirical studies-to investigate social group-related specificities pertaining to implicit gender-domain stereotypes, as measured by a mouse-tracking adapted Implicit Association Test (IAT) and IAT(-like) tasks. We tested whether the emergence of implicit stereotypes was partially determined by associations congruent with the self, by visuospatial features of the task and subsequent competition at both sensorimotor and abstract levels. We tracked human and simulated artificial participants' hand movements among gender stereotypical (e.g., male engineers) and counterstereotypical (e.g., female engineers) social groups. In the computational study, data were simulated by a novel generative connectionist model integrating strengths from recent developments in embodied models of decision-making. Results support the self-congruency hypothesis and suggest the presence of competition at both levels. Discussion focuses on the generalizability of the self-congruency hypothesis and on the relevance of a situated perspective for implicit social cognition. (PsycINFO Database Record

Dual filtering in operational and joint spaces for reaching and grasping.

To study human movement generation, as well as to develop efficient control algorithms for humanoid or dexterous manipulation robots, overcoming the limits and drawbacks of inverse-kinematics-based methods is needed. Adequate methods must deal with high dimensionality, uncertainty, and must perform in real time (constraints shared by robots and humans). This paper introduces a Bayesian filtering method, hierarchically applied in the operational and joint spaces to break down the complexity of the problem. The method is validated in simulation on a robotic arm in a cluttered environment, with up to 51 degrees of freedom.

Combined effects of expectations and visual uncertainty upon detection and identification of a target in the fog.

Detecting a pedestrian while driving in the fog is one situation where the prior expectation about the target presence is integrated with the noisy visual input. We focus on how these sources of information influence the oculomotor behavior and are integrated within an underlying decision-making process. The participants had to judge whether high-/low-density fog scenes displayed on a computer screen contained a pedestrian or a deer by executing a mouse movement toward the response button (mouse-tracking). A variable road sign was added on the scene to manipulate expectations about target identity. We then analyzed the timing and amplitude of the deviation of mouse trajectories toward the incorrect response and, using an eye tracker, the detection time (before fixating the target) and the identification time (fixations on the target). Results revealed that expectation of the correct target results in earlier decisions with less deviation toward the alternative response, this effect being partially explained by the facilitation of target identification.

Dynamic competition and binding of concepts through time and space.

Models of implicit stereotypes (e.g., association of male with math or female with language) usually explain the faster responses observed for stereotype-congruent trials in the Implicit Association Test (IAT) by requiring a fundamental opposition between the male and female concepts (or math-language), limiting the decision-making dynamics to abstract dimensions. This paper introduces alternate models exploiting the sensorimotor dimensions of the IAT, which naturally account for the opposition between concepts, because typically mapped on opposite corners of the screen space and on different response actions. In addition to the emergence of the IAT effect, dynamic characteristics of the decision-making process within these models are tested against human data, obtained with a mouse-tracking adapted IAT procedure.

How active perception and attractor dynamics shape perceptual categorization: a computational model.

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent-environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as "evidence" for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.

Competition in high dimensional spaces using a sparse approximation of neural fields.

The Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate.This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features.