Parallel Constraint Satisfaction Research Articles

A Cognitive Processing Framework for Media Interpretation

Abstract. The same media content can be interpreted by different people in radically different ways. We propose a framework that considers both the cognitive processes and the associated mental representations implicated in the interpretation of media content. The foundation of this argument stems from a constraint satisfaction approach to coherence, and it explains the dynamic relationship between media content and media consumers’ processing and interpreting of that content. By integrating parallel constraint satisfaction and coherence with reflective imaginative involvement, we present an explanation of how people interpret media stories and how they may engage with these stories in the future. We believe this framework has significant implications for media scholars interested in message processing and effects.

SOCIAL STEREOTYPES AND COGNITIVE MECHANISM OF THEIR PROCESSING

Given study tests cognitive model related to social information processing - Parallel Constraint Satisfaction Theory. The study was conducted as one of the research steps within another scientific project, which explored stereotypic mode of impression formation. Unlike other models, PCST presents stereotypic mode of thinking as the process, in which received information just as a neural impulse flows in the endless web of mental associations in observer’s mind and automatically acxites/inhibites certain information stored in there. We decided to test these two cognitive processes on the content of real social stereotypes. We explored stereotypes of five social categories (Cook, Lecturer, Doctor, IT and Typical Georgian Man). Then, we measured the level of acxitation/inhibition of certain characteristics within the stereotype of each category. After comparing the level of character activation-deactivation between each category, as well as to the textual characterizations obtained from focus groups about the same categories, it became clear that activation/deactivation process indeed takes place. Moreover, associations activate/deactivate in such a way, that they automatically fit (satisfy) the content of stereotype, held by the observer about perceived social category.

Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem

ABSTRACT We introduce the identity labeling problem – given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity. We apply our model to data collected in two vignette experiments, finding that it predicts identity labeling choices of participants with a mean absolute error of 10.9%, a 100% improvement over previous models based on parallel constraint satisfaction and affect control theory.

Denotative and connotative management of uncertainty: A computational dual-process model

AbstractThe interplay between intuitive and deliberative processing is known to be important for human decision making. As independent modes, intuitive processes can take on many forms from associative to constructive, while deliberative processes often rely on some notion of decision theoretic rationality or pattern matching. Dual process models attempt to unify these two modes based on parallel constraint networks or on socially or emotionally oriented adjustments to utility functions. This paper presents a new kind of dual process model that unifies decision theoretic deliberative reasoning with intuitive reasoning based on shared cultural affective meanings in a single Bayesian sequential model. Agents constructed according to this unified model are motivated by a combination of affective alignment (intuitive) and decision theoretic reasoning (deliberative), trading the two off as a function of the uncertainty or unpredictability of the situation. The model also provides a theoretical bridge between decision-making research and sociological symbolic interactionism. Starting with a high-level view of existing models, we advance Bayesian Affect Control Theory (BayesACT) as a promising new type of dual process model that explicitly and optimally (in the Bayesian sense) trades off motivation, action, beliefs and utility. We demonstrate a key component of the model as being sufficient to account for some aspects of classic cognitive biases about fairness and dissonance, and outline how this new theory relates to parallel constraint satisfaction models.

Immediate online use of prosody reveals the ironic intentions of a speaker: neurophysiological evidence.

In social interactions, speakers often use their tone of voice ("prosody") to communicate their interpersonal stance to pragmatically mark an ironic intention (e.g., sarcasm). The neurocognitive effects of prosody as listeners process ironic statements in real time are still poorly understood. In this study, 30 participants judged the friendliness of literal and ironic criticisms and compliments in the absence of context while their electrical brain activity was recorded. Event-related potentials reflecting the uptake of prosodic information were tracked at two time points in the utterance. Prosody robustly modulated P200 and late positivity amplitudes from utterance onset. These early neural responses registered both the speaker's stance (positive/negative) and their intention (literal/ironic). At a later timepoint (You are such a great/horrible cook), P200, N400, and P600 amplitudes were all greater when the critical word valence was congruent with the speaker's vocal stance, suggesting that irony was contextually facilitated by early effects from prosody. Our results exemplify that rapid uptake of salient prosodic features allows listeners to make online predictions about the speaker's ironic intent. This process can constrain their representation of an utterance to uncover nonliteral meanings without violating contextual expectations held about the speaker, as described by parallel-constraint satisfaction models.

Influence of the Order of Provided Information on the First Impression

The study aims to explore the dominance of stereotypic thinking in the process of social information processing. It tests one of the assumptions of Parallel Constraint Satisfaction Theory regarding the order of provided information. It is hypothesized that dominance of either type of processing (stereotypic or individuating) depends on the primacy of provided social information. In the experiment, each participant (N = 144) was randomly assigned to either of the condition, where one was presented with one and the same stereotypic (categorical) and individuating (counter stereotypic behavioral) information about one and the same person, provided into a certain order. Based on the level of p < 0.05, comparison of impressions received in different order conditions (category primacy vs. behavior primacy) revealed that the first impressions followed the effect of primacy. This effect appeared to be consistent in the impressions of both observed targets (Doctor and IT). Hypothesis about the importance of order and primacy in social perception has been proved, but hypothesis about averaging impression in the condition of the information being provided together was not proved. The study shows that order of provided information determines whether target instantly stereotypically categorizes others or focuses more on their behavior and makes individualistic impression of them.

A new and unique prediction for cue-search in a parallel-constraint satisfaction network model: The attraction search effect.

A common assumption of many established models for decision making is that information is searched according to some prespecified search rule. While the content of the information influences the termination of search, usually specified as a stopping rule, the direction of search is viewed as being independent of the valence of the retrieved information. We propose an extension to the parallel constraint satisfaction network model (iCodes: integrated coherence-based decision and search), which assumes-in contrast to prespecified search rules-that the valence of available information influences search of concealed information. Specifically, the model predicts an attraction search effect in that information search is directed toward the more attractive alternative given the available information. In 3 studies with participants choosing between two options based on partially revealed probabilistic information, the attraction search effect was consistently observed for environments with varying costs for information search although the magnitude of the effect decreased with decreasing monetary search costs. We also find the effect in reanalyses of 5 published studies. With iCodes, we propose a fully specified formal model and discuss implications for theory development within competing modeling frameworks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Linking process and measurement models of recognition-based decisions.

When making inferences about pairs of objects, one of which is recognized and the other is not, the recognition heuristic states that participants choose the recognized object in a noncompensatory way without considering any further knowledge. In contrast, information-integration theories such as parallel constraint satisfaction (PCS) assume that recognition is merely one of many cues that is integrated with further knowledge in a compensatory way. To test both process models against each other without manipulating recognition or further knowledge, we include response times into the r-model, a popular multinomial processing tree model for memory-based decisions. Essentially, this response-time-extended r-model allows to test a crucial prediction of PCS, namely, that the integration of recognition-congruent knowledge leads to faster decisions compared to the consideration of recognition only-even though more information is processed. In contrast, decisions due to recognition-heuristic use are predicted to be faster than decisions affected by any further knowledge. Using the classical German-cities example, simulations show that the novel measurement model discriminates between both process models based on choices, decision times, and recognition judgments only. In a reanalysis of 29 data sets including more than 400,000 individual trials, noncompensatory choices of the recognized option were estimated to be slower than choices due to recognition-congruent knowledge. This corroborates the parallel information-integration account of memory-based decisions, according to which decisions become faster when the coherence of the available information increases. (PsycINFO Database Record

Eye Movements in Risky Choice.

We asked participants to make simple risky choices while we recorded their eye movements. We built a complete statistical model of the eye movements and found very little systematic variation in eye movements over the time course of a choice or across the different choices. The only exceptions were finding more (of the same) eye movements when choice options were similar, and an emerging gaze bias in which people looked more at the gamble they ultimately chose. These findings are inconsistent with prospect theory, the priority heuristic, or decision field theory. However, the eye movements made during a choice have a large relationship with the final choice, and this is mostly independent from the contribution of the actual attribute values in the choice options. That is, eye movements tell us not just about the processing of attribute values but also are independently associated with choice. The pattern is simple—people choose the gamble they look at more often, independently of the actual numbers they see—and this pattern is simpler than predicted by decision field theory, decision by sampling, and the parallel constraint satisfaction model. © 2015 The Authors. Journal of Behavioral Decision Making published by John Wiley & Sons Ltd.

What is adaptive about adaptive decision making? A parallel constraint satisfaction account

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns – as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular.

Cognitive integration of recognition information and additional cues in memory-based decisions

AbstractGlöckner and Bröder (2011) have shown that for 77.5% of their participants’ decision making behavior in decisions involving recognition information and explicitly provided additional cues could be better described by weighted-compensatory Parallel Constraint Satisfaction (PCS) Models than by non-compensatory strategies such as recognition heuristic (RH) or Take the Best (TTB). We investigate whether this predominance of PCS models also holds in memory-based decisions in which information retrieval is effortful and cognitively demanding. Decision strategies were analyzed using a maximum-likelihood strategy classification method, taking into account choices, response times and confidence ratings simultaneously. In contrast to the memory-based-RH hypothesis, results show that also in memory-based decisions for 62% of the participants behavior is best explained by a compensatory PCS model. There is, however, a slight increase in participants classified as users of the non-compensatory strategies RH and TTB (32%) compared to the previous study, mirroring other studies suggesting effects of costly retrieval.

Judgment: a cognitive processing perspective.

Historically, judgment research has been mainly concerned with identifying regularities in sensation (e.g., discriminability laws) and assessing judgment accuracy. More recently, the focus has shifted toward specifying the information processing mechanisms underlying judgment and modeling them, for example, as cognitive strategies. We contrast this strategy approach with previous prominent research programs on judgment and provide an overview of various process-level accounts that have been proposed in terms of computational models (e.g., compensatory and noncompensatory cue-abstraction strategies, evidence accumulation, exemplar processing, and parallel constraint satisfaction). Importantly, empirical investigations show that the cognitive processes underlying judgment differ considerably as a function of the individual's cognitive capacity and characteristics of the task environment (e.g., information cost, cognitive capacity, cue inter-correlations, relationship between cues and the to-be-judged criterion). We argue that these systematic contingencies in strategy use can be understood as adaptive responses to costs in learning, information acquisition, and strategy execution. WIREs Cogn Sci 2013, 4:665-681. doi: 10.1002/wcs.1259 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article For further resources related to this article, please visit the WIREs website.

Comparing holistic and atomistic evaluation of evidence

Fact finders in legal trials often need to evaluate a mass of weak, contradictory and ambiguous evidence. There are two general ways to accomplish this task: by holistically forming a coherent mental representation of the case, or by atomistically assessing the probative value of each item of evidence and integrating the values according to an algorithm. Parallel constraint satisfaction (PCS) models of cognitive coherence posit that a coherent mental representation is created by discounting contradicting evidence, inflating supporting evidence and interpreting ambivalent evidence in a way coherent with the emerging decision. This leads to inflated support for whichever hypothesis the fact finder accepts as true. Using a Bayesian network to model the direct dependencies between the evidence, the intermediate hypotheses and the main hypothesis, parameterised with (conditional) subjective probabilities elicited from the subjects, I demonstrate experimentally how an atomistic evaluation of evidence leads to a convergence of the computed posterior degrees of belief in the guilt of the defendant of those who convict and those who acquit. The atomistic evaluation preserves the inherent uncertainty that largely disappears in a holistic evaluation. Since the fact finders’ posterior degree of belief in the guilt of the defendant is the relevant standard of proof in many legal systems, this result implies that using an atomistic evaluation of evidence, the threshold level of posterior belief in guilt required for a conviction may often not be reached. Max Planck Institute for Research on Collective Goods, Bonn

Attention and choice: A review on eye movements in decision making

This paper reviews studies on eye movements in decision making, and compares their observations to theoretical predictions concerning the role of attention in decision making. Four decision theories are examined: rational models, bounded rationality, evidence accumulation, and parallel constraint satisfaction models. Although most theories were confirmed with regard to certain predictions, none of the theories adequately accounted for the role of attention during decision making. Several observations emerged concerning the drivers and down-stream effects of attention on choice, suggesting that attention processes plays an active role in constructing decisions. So far, decision theories have largely ignored the constructive role of attention by assuming that it is entirely determined by heuristics, or that it consists of stochastic information sampling. The empirical observations reveal that these assumptions are implausible, and that more accurate assumptions could have been made based on prior attention and eye movement research. Future decision making research would benefit from greater integration with attention research.

The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

The affective meanings of automatic social behaviors: Three mechanisms that explain priming.

The priming of concepts has been shown to influence peoples' subsequent actions, often unconsciously. We propose 3 mechanisms (psychological, cultural, and biological) as a unified explanation of such effects. (a) Primed concepts influence holistic representations of situations by parallel constraint satisfaction. (b) The constraints among representations stem from culturally shared affective meanings of concepts acquired in socialization. (c) Patterns of activity in neural populations act as semantic pointers linking symbolic concepts to underlying emotional and sensorimotor representations and thereby causing action. We present 2 computational models of behavioral priming that implement the proposed mechanisms. One is a localist neural network that connects primes with behaviors through central nodes simulating affective meanings. In a series of simulations, where the input is based on empirical data, we show that this model can explain a wide variety of experimental findings related to automatic social behavior. The second, neurocomputational model simulates spiking patterns in populations of biologically realistic neurons. We use this model to demonstrate how the proposed mechanisms can be implemented in the brain. Finally, we discuss how our models integrate previous theoretical accounts of priming phenomena. We also examine the interactions of psychological, cultural, and biological mechanisms in the control of automatic social behavior.

Comparing Holistic and Atomistic Evaluation of Evidence

Fact finders in legal trials often need to evaluate a mass of weak, contradictory and ambiguous evidence. There are two general ways to accomplish this task: by holistically forming a coherent mental representation of the case, or by atomistically assessing the probative value of each item of evidence and integrating the values according to an algorithm. Parallel constraint satisfaction (PCS) models of cognitive coherence posit that a coherent mental representation is created by discounting contradicting evidence, inflating supporting evidence and interpreting ambivalent evidence in a way coherent with the emerging decision. This leads to inflated support for whichever hypothesis the fact finder accepts as true. Using a Bayesian network to model the direct dependencies between the evidence, the intermediate hypotheses and the main hypothesis, parameterised with (conditional) subjective probabilities elicited from the subjects, I demonstrate experimentally how an atomistic evaluation of evidence leads to a convergence of the computed posterior degrees of belief in the guilt of the defendant of those who convict and those who acquit. The atomistic evaluation preserves the inherent uncertainty that largely disappears in a holistic evaluation. Since the fact finders’ posterior degree of belief in the guilt of the defendant is the relevant standard of proof in many legal systems, this result implies that using an atomistic evaluation of evidence, the threshold level of posterior belief in guilt required for a conviction may often not be reached.

The rationality of different kinds of intuitive decision processes

Whereas classic work in judgment and decision making has focused on the deviation of intuition from rationality, more recent research has focused on the performance of intuition in real-world environments. Borrowing from both approaches, we investigate to which extent competing models of intuitive probabilistic decision making overlap with choices according to the axioms of probability theory and how accurate those models can be expected to perform in real-world environments. Specifically, we assessed to which extent heuristics, models implementing weighted additive information integration (WADD), and the parallel constraint satisfaction (PCS) network model approximate the Bayesian solution and how often they lead to correct decisions in a probabilistic decision task. PCS and WADD outperform simple heuristics on both criteria with an approximation of 88.8 % and a performance of 73.7 %. Results are discussed in the light of selection of intuitive processes by reinforcement learning.

Role‐Induced Bias in Court: An Experimental Analysis

AbstractCriminal procedure is organized as a tournament with predefined roles. We show that assuming the role of a defense counsel or a prosecutor leads to role‐induced bias even if participants are asked to predict a court ruling after they have ceased to act in that role and if they expect a substantial financial incentive for being accurate. The bias is not removed either if participants are instructed to predict the court ruling in preparation of plea bargaining. In line with parallel constraint satisfaction models for legal decision making, findings indicate that role‐induced bias is driven by coherence effects, that is, systematic information distortions in support of the favored option. This is mainly achieved by downplaying the importance of conflicting evidence. These distortions seem to stabilize interpretations, and people do not correct for this bias. Implications for legal procedure are briefly discussed. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Decisions beyond boundaries: When more information is processed faster than less

Bounded rationality models usually converge in claiming that decision time and the amount of computational steps needed to come to a decision are positively correlated. The empirical evidence for this claim is, however, equivocal. We conducted a study that tests this claim by adding and omitting information. We demonstrate that even an increase in information amount can yield a decrease in decision time if the added information increases coherence in the information set. Rather than being influenced by amount of information, decision time systematically increased with decreasing coherence. The results are discussed with reference to a parallel constraint satisfaction approach to decision making, which assumes that information integration is operated in an automatic, holistic manner.