A quantification of robustness

Robustness is an important construct in domains as diverse as evolutionary biology, structural engineering, and decision-making. Unfortunately, in many domains, most relevantly cognitive science, considerations of robustness end with vague semantic references. Little attention is paid to formal analysis. The aim of this paper is to initiate a discussion in the scientific community regarding methods for quantifying and analyzing robustness. To this end, we propose a means for assessing robustness that may supplant the current ambiguous use of the term. We demonstrate our quantitative approach using examples of heuristic-based decision processes, selected due to their explicit association with robustness in the psychological literature. These examples serve to illustrate basic properties of our general methodology for quantifying robustness.     

Bootstrapping cognition from behavior-a computerized thought experiment

We show that simple perceptual competences can emerge from an internal simulation of action effects and are thus grounded in behavior. A simulated agent learns to distinguish between dead ends and corridors without the necessity to represent these concepts in the sensory domain. Initially, the agent is only endowed with a simple value system and the means to extract low-level features from an image. In the interaction with the environment, it acquires a visuo-tactile forward model that allows the agent to predict how the visual input is changing under its movements, and whether movements will lead to a collision. From short-term predictions based on the forward model, the agent learns an inverse model. The inverse model in turn produces suggestions about which actions should be simulated in long-term predictions, and long-term predictions eventually give rise to the perceptual ability.     

Robust affective priming effects in a conditional pronunciation task: Evidence for the semantic representation of evaluative information

Based on the hypothesis that information about the valence of words is encoded in a semantic system, we predicted that the match between the valence of a prime and the valence of a target word will influence the pronunciation of the target only if and to the extent that pronunciation is semantically mediated. In line with this prediction, we found affective priming effects (faster pronunciation when prime and target had the same valence than when they had a different valence) only when participants were instructed to read words but not nonwords (Experiment 1) or words that were not names of occupations (Experiment 2). Priming was not significant when participants were asked to read white but not red words (Experiment 1) or words that did not have a frame around them (Experiment 2).     

认知固执导致持续应激的认知与神经机制

认知固执(Perseverative Cognition, PC)是一种重复或长期的消极认知表征,既能够使用量表测量,也可以通过实验诱发。认知固执会导致持续应激反应,进而影响个体的身心健康。其导致持续应激反应的认知机制体现在个体难以识别安全信号,高认知固执个体会表现出低心率变异性的显著特征;在脑机制方面主要表现为前额叶的抑制控制功能失调、杏仁核与前扣带回的病理性改变以及前额叶-杏仁核的连接减弱。未来研究应深入探讨认知固执的影响因素以及干预措施,同时需要进一步分析内隐认知固执的测量方式及其对个体的影响。     

Symbolically speaking: a connectionist model of sentence production

The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of connectionist models of language often center on the inability of these models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a connectionist model of sentence production was developed. The model had variables (role‐concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual‐pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the model's hidden units demonstrated that the model learned different types of information in each pathway, and that the model's compositional behavior arose from the combination of these two pathways. The model's ability to balance symbolic and statistical behavior in syntax acquisition and to model aphasic double dissociations provided independent support for the dual‐pathway architecture.     

An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.     

认知需求和认知闭合需求对风险决策中外语效应的影响

外语效应指相比于母语背景,个体在外语背景下风险决策偏差会降低。针对影响外语效应的因素研究大多聚焦于外部因素和与个体外语水平相关的因素,少有研究探讨认知风格的作用。本研究通过524名成人被试,探索认知需求和认知闭合需求对外语效应的影响。结果表明:(1)风险决策中存在外语效应,相比于母语背景,外语背景下框架效应被削弱,且个体更偏向风险寻求。(2)对于认知需求和认知闭合需求都高或都低的个体,语言类型会影响风险决策,在母语环境下倾向于风险规避,在外语环境下倾向于风险寻求;而对于认知需求和认知闭合需求高低不一致的个体,语言类型对其风险决策倾向的影响不显著。本研究为认知风格如何综合影响风险决策提供了进一步的证据。     

Automatic Mechanisms for Social Attention Are Culturally Penetrable

Are mechanisms for social attention influenced by culture? Evidence that social attention is triggered automatically by bottom‐up gaze cues and is uninfluenced by top‐down verbal instructions may suggest it operates in the same way everywhere. Yet considerations from evolutionary and cultural psychology suggest that specific aspects of one's cultural background may have consequence for the way mechanisms for social attention develop and operate. In more interdependent cultures, the scope of social attention may be broader, focusing on more individuals and relations between those individuals. We administered a multi‐gaze cueing task requiring participants to fixate a foreground face flanked by background faces and measured shifts in attention using eye tracking. For European Americans, gaze cueing did not depend on the direction of background gaze cues, suggesting foreground gaze alone drives automatic attention shifting; for East Asians, cueing patterns differed depending on whether the foreground cue matched or mismatched background cues, suggesting foreground and background gaze information were integrated. These results demonstrate that cultural background influences the social attention system by shifting it into a narrow or broad mode of operation and, importantly, provides evidence challenging the assumption that mechanisms underlying automatic social attention are necessarily rigid and impenetrable to culture.     

Cognitive solutions for security and cryptography

This paper describes the new security solutions based on cognitive approaches and new computing paradigm called cognitive cryptography. This new security area establish a new generation of computational methods and security systems, focused on creation intelligent cryptographic algorithms and security protocols using cognitive information processing approaches. Such systems are designed for semantic evaluation of encrypted data, and allow to select the most appropriate techniques of its encryption. This paper presents a possible application of such techniques for different security tasks like authentication, secret sharing, secure data management etc. Additionally, some cryptographic solutions inspired by biological models will be presented.     

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

A long-standing question in cognitive science is how high-level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top-down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes-capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. Because this phoneme-to-phoneme compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would constitute strong support for top–down interaction. However, results from previous LCfC studies have been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous context-final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for coarticulation for a subsequent target phoneme. We observed robust LCfC in a well-powered, preregistered experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40). These results provide strong evidence in favor of computational models of spoken word recognition that include top–down feedback.     

The enactive computational basis of cognition and the explanatory cognitive basis for computing

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation.     

Proactive inhibitory control of emotional distractors: Evidence for the benefit of precuing emotional distractors

The present study investigated the role of proactive inhibitory control in processing emotional distractors by examining the benefit of precuing the following emotional distractor. In Experiments 1A and 1B, an emotional flanker task was used while schematic emotional faces were presented as targets and distractors. We found the benefit of precuing the emotional distractor. In Experiment 2, the precue could not predict the following emotional distractor. The benefit of precuing the emotional distractor diminished, suggesting that the benefit was not due to reactive inhibition of the precued distractor. In Experiments 3A and 3B, an emotional Stroop task was used while schematic emotional faces were presented as distractors. The benefit of precuing the emotional distractors was observed when these distractors were emotional faces but not observed when the distractors were scrambled faces. These findings suggested that the benefit of precuing the emotional distractors operates at the emotional level.     

Bio-inspired computational object classification model for object recognition

Human beings can effortlessly perceive stimuli through their sensory systems to learn, understand, recognize and act on our environment or context. Over the years, efforts have been made to enable cybernetic entities to be close to performing human perception tasks; and in general, to bring artificial intelligence closer to human intelligence.Neuroscience and other cognitive sciences provide evidence and explanations of the functioning of certain aspects of visual perception in the human brain. Visual perception is a complex process, and its has been divided into several parts. Object classification is one of those parts; it is necessary for carrying out the declarative interpretation of the environment. This article deals with the object classification problem.In this article, we propose a computational model of visual classification of objects based on neuroscience, it consists of two modular systems: a visual processing system, in charge of the extraction of characteristics; and a perception sub-system, which performs the classification of objects based on the features extracted by the visual processing system.With the results obtained, a set of aspects are analyzed using similarity and dissimilarity matrices. Also based on the neuroscientific evidence and the results obtained from this research, some aspects are suggested for consideration to improve the work in the future and bring us closer to performing the task of visual classification as humans do.     

A functional approach for research on cognitive control: Analysing cognitive control tasks and their effects in terms of operant conditioning

Cognitive control is an important mental ability that is examined using a multitude of cognitive control tasks and effects. The present paper presents the first steps in the elaboration of a functional approach, which aims to uncover the communalities and differences between different cognitive control tasks and their effects. Based on the idea that responses in cognitive control tasks qualify as operant behaviour, we propose to reinterpret cognitive control tasks in terms of operant contingencies and cognitive control effects as instances of moderated stimulus control. We illustrate how our approach can be used to uncover communalities between topographically different cognitive control tasks and can lead to novel questions about the processes underlying cognitive control.     

Criteria for the design and evaluation of cognitive architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.     

Survey on biometry for cognitive automotive systems

A survey on biometry for cognitive automotive systems is presented in this paper, specially those biometric systems used for high tech security access, law enforcement and/or commercial transactions. In general, biometric systems can be expensive due to the amount of sensors and processing resources involved. Efforts have been made to integrate these systems to vehicles mostly for security purposes and user authentication. Until now, most of the systems do not go beyond using facial and fingerprint data to start the engine or access the car; however, new generations demand more personalization plus the vehicle making decisions based on their physiological characteristics. Although this kind of technology is considered a luxury feature in general, actually it could help users and save their lives. Indeed, biometry is the way to make the human-vehicle relationship happen, whether the biometric devices are embedded inside the vehicle, used as an accessory or wearables. By using as input not only the sensors inside the vehicle but also data from outside, the vehicle could adapt and/or learn new information to make the best possible decision.     

Between-task competition for intentions and actions

People can switch quickly and flexibly from one task to another, but suffer the effects of between-task competition when they do so: After switching, they tend to be distracted by irrelevant stimulus information and hampered by incorrect actions associated with recently performed tasks. This competition results in performance costs of switching, as well as a bias against switching when there is choice over which task to perform, particularly when switching from a difficult task to an easier one. Two experiments investigated the locus of these between-task competition effects in voluntary task switching. Participants switched between an easy location classification and a harder shape classification, making two responses on each trial: the first to register their task choice, the second to perform the chosen task on a subsequently presented stimulus. The results indicated that participants chose to perform the difficult shape task more often than the easier location task, evidence that between-task competition affects intentions that are expressed independently of task-specific actions. The bias was stronger in participants with faster choice speed, suggesting that these influences are relatively automatic. Moreover, even though participants had unlimited time to choose and prepare a task before stimulus presentation, their subsequent performance was nonetheless sensitive to persisting effects of between-task competition. Altogether these results indicate the pervasive influence of between-task competition, which affects both the expression of global task intentions and the production of task-specific actions.     

Face recognition algorithms and the other‐race effect: computational mechanisms for a developmental contact hypothesis

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent with the predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces.     

Mechanisms for enhancing the credibility of an adviser: prepayment and aligned incentives

We tested the effectiveness of prepayment for advice and aligned incentives as mechanisms for enhancing trust in unfamiliar advisers in decision‐making under uncertainty. Participants were low‐income Zimbabweans who played two rounds of the Monty Hall three‐door game. In round 1, participants who purchased advice were significantly more likely to follow advice for how to win the game than were participants who received free advice. In round 2, the apparent effectiveness of advisers' suggestions in round 1 moderated participants' propensity to follow advice. If the round 1 advice appeared wrong, the credibility enhancing benefits of prepayment diminished. If the advice appeared right, the benefits of prepayment maintained. Hypotheses with regard to the benefits of aligned incentives received only weak support. Copyright © 2006 John Wiley & Sons, Ltd.