Justification pressure in risky decision making: search for risk defusing operators

Under justification pressure, the decision maker knows in advance that the decision has to be justified to somebody afterwards. The effect of justification pressure on the search for risk defusing operators (RDOs) and the role of RDOs in the justification texts were investigated. An RDO is an action intended by the decision maker to be performed in addition to an otherwise attractive alternative to decrease the risk. As predicted, in Experiment 1 participants (60 non-students) under justification pressure searched more RDOs. Additionally, in Experiment 2 (80 non-students) RDO search success was varied. Under justification pressure, persistence of RDO search was higher when no RDO could be detected. In the justification texts, the existence or non-existence of RDOs played a prominent role. Searching for RDOs supports people in their goal to make a good decision and in their attempt to convince the addressee of their justification that the decision was good.     

In the absence of rose-colored glasses: Ratings of self-attributes and their differential certainty and importance across multiple dimensions in social phobia

Sixty-seven individuals with social phobia (social anxiety disorder) and 60 healthy controls rated their perceived standing relative to others on 13 self-attribute dimensions, their level of certainty concerning those standings, and the importance of each dimension. As expected, individuals with social phobia provided self-ratings that were significantly more negative than controls across all dimensions. In addition, positive self-views were equated with higher levels of certainty and importance for controls, but not for individuals with social phobia. Thus, whereas reports of control participants reflected a healthy, positive framing of self-views, the ratings of clinical participants demonstrated an orientation toward self-framing that was neither positive nor negative. Together, these novel findings shed light on the nature of self-appraisals in social anxiety. Implications of these results are discussed in terms of contemporary cognitive-behavioral models of social phobia.     

Sources of information for discriminating dynamic human actions

Despite the importance of action identification and discrimination in action perception and social cognition more broadly, little research has investigated how these processes are achieved. To this end, we sought to identify the extent to which adults capitalize on featural versus configural sources of information when discriminating small-scale human actions such as grasp and place. Results across two experiments indicate adults are sensitive to both sources of information in action discrimination, but selectively attend to featural over configural action information. The findings also parallel what is known regarding face processing: processing of configural information is especially disrupted by inversion, whereas processing of featural information is specifically affected by low-pass filtering.     

Transverse diffusivity of cerebral parenchyma predicts visual tracking performance in relapsing–remitting multiple sclerosis

This study investigated the relation between cerebral damage related to multiple sclerosis (MS) and cognitive decline as determined by two classical mental tracking tests. Cerebral damage in 15 relapsing–remitting MS patients was measured by diffusion tensor imaging (DTI). Fractional anisotropy, longitudinal and transverse diffusivity were defined in the cerebral parenchyma. Cognitive performance of the MS patients was assessed with the oral response format of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT). A significant correlation was found between performance on the SDMT and the fractional anisotropy in the brain. This correlation was predominantly induced by transverse diffusivity. Transverse diffusivity refers to the diffusion across fibers rather than along the fibers and is believed to be a specific marker for axonal loss and demyelination associated with MS. No significant association between DTI-measures and PASAT performance was found and this negative finding was mainly attributed to psychometric qualities. These results indicate that diffusivity along the non-principal diffusion direction, a possible signature of MS-related white matter pathology, contributes to information processing speed as measured with the SDMT, a task that requires close visual tracking and a widely used clinical marker for cognitive decline in MS.     

Representation recovers information

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing.     

Improving image annotation via useful representative feature selection

This paper describes the automatic assignment of images into classes described by individual keywords provided with the Corel data set. Automatic image annotation technology aims to provide an efficient and effective searching environment for users to query their images more easily, but current image retrieval systems are still not very accurate when assigning images into a large number of keyword classes. Noisy features are the main problem, causing some keywords never to be assigned to their correct images. This paper focuses on improving image classification, first by selection of features to characterise each image, and then the selection of the most suitable feature vectors as training data. A Pixel Density filter (PDfilter) and Information Gain (IG) are proposed to perform these respective tasks. We filter out the noisy features so that groups of images can be represented by their most important values. The experiments use hue, saturation and value (HSV) colour feature space to categorise images according to one of 190 concrete keywords or subsets of these. The study shows that feature selection through the PDfilter and IG can improve the problem of spurious similarity.

A positive information logic for inferential information

Performing an inference involves irreducibly dynamic cognitive procedures. The article proposes that a non-associative information frame, corresponding to a residuated pogroupoid, underpins the information structure involved. The argument proceeds by expounding the informational turn in logic, before outlining the cognitive actions at work in deductive inference. The structural rules of Weakening, Contraction, Commutation, and Association are rejected on the grounds that they cause us to lose track of the information flow in inferential procedures. By taking the operation of information application as the primary operation, the fusion connective is retained, with commutative failure generating a double implication. The other connectives are rejected.     

A Multistage,Multiprocess Analysis of Consumer Judgment: A Selective Review and Conceptual Framework

Although numerous dual‐process models have been brought to bear on the cognitive operations that underlie consumer judgment, they are typically limited in their ability to capture the processes that occur at different stages of cognitive activity and to specify how these processes interface. In many (if not most) cases, a mix of both automatic and controlled processes underlies judgments and decisions. We suggest a theoretical framework that potentially provides an integration of these processes and review representative research and theory that might be conceptualized in terms of this framework.     

Brain states matter. A reply to the unfolding argument

Recently, it has been claimed that Integrated Information Theory and other theories of its type cannot explain consciousness (“unfolding argument”). We unravel this argument mathematically and prove that the premises of the argument imply a much stronger result according to which the observed problem holds for almost all theories of consciousness. We find, however, that one of the premises is unwarranted and show that if this premise is dropped, the argument ceases to work. Thus our results show that the claim of the unfolding argument cannot be considered valid. The premise in question is that measures of brain activity cannot be used in an empirical test of theories of consciousness.     

Graph-based ontology reasoning for formal verification of BREEAM rules

Globally, the need to check regulation compliance for sustainability has become central in the delivery of construction projects. This is partly due to policies by various governments requiring existing and new buildings to comply with certain standards or regulations. However, the verification of whether a building complies with any particular standard or regulation has proven challenging in practice. The purpose of formal verification is to prove that under a certain set of assumptions, a building will adhere to a certain set of requirements, for example the minimum performance standards of key environmental issues. Compliance checking requires different criteria often difficult to straightforwardly define and combine in an integrated fashion for providing holistic interpretation to facilitate easy decision-making. Such criteria, their various flows and combinations can easily be dealt with using conceptual graph theories and Semantic Web concepts which allow rules to be imbued to facilitate reasoning. The aim of this study is to tap on conceptual graphs and Semantic Web concepts to develop a system for checking Building Research Establishment Environmental Assessment Methodology (BREEAM) sustainability standard compliance in the French construction industry. A conceptual graph-based framework that formally describes BREEAM requirements and visually analyse compliance checking processes has been proposed. When implemented in a software that integrates conceptual graphs and Semantic Web knowledge, automatic reasoning allows both the logical specification and the visual interpretation to be displayed and further provides a semantic support for compliance checking information.