Image representation,scaling and cognitive model of object perception

Neuropsychological investigations of visual imagery and representations have led to a deeper understanding of the spatial perception, representation and memory. But how each individual perceives object’s geometrical properties and how they differ from person to person, both under event-related memory and normal recollecting memory in the presence or in the absence of direct sensory stimulation is still unclear. Spatial knowledge is diverse, complex, and multi-modal, as are the situations in which it is used. All seem to agree that a cognitive map is a mental representation of an external environment. The image scaling is important in understanding the psychological dysfunctions of patients suffering from spatial cognition problems. The scaling becomes self-evident in art forms, when people are asked to draw image of objects they see actively or from their short or long term memory. In this paper we develop a comprehensive model of this scaling factor and its implications in spatial image representation and memory. We also extend its notion in understanding the perception of objects whose representations are normally not possible (like the perception of universal scales, infinities and parallel lines) but are well comprehended by the human brains. Here we give a scaling factor which is variable depending on the situations for a person based on his visual memory and drawing capabilities. And then extend it to analyse his cognitive strengths, disorders and any imperfections. This model also helps in formalizing the architectural cognitive maps needed to change the scaling factor, depending on the types of visual works one performs.     

Religious beliefs,patient–doctor interaction and resilience as predictors for treatment decisions and health outcomes of heart patients

Treatment decision-making in chronic illness poses long-lasting effects on the health status of patients. In Western individualistic cultures, they are independently taken by the individual in collaboration with doctors contrary to the collectivistic Indian context, where it’s decided by families with little or no involvement of patients. Religious beliefs, patient–doctor interaction, and resilience measures of 100 CAD patients were used to assess their religious beliefs, patient–doctor interaction, and resilience (predictors). Hierarchical Regression Analysis was conducted to test for the significance of the proposed model. Religious beliefs, patient–doctor interaction, and resilience collectively predicted the significant change in decision-making styles, somatic symptoms, anxiety, social dysfunctions, depression, and general health total of the participants. While the treatment decision-making is heavily contingent upon the social factors namely – religious beliefs, patient–doctor interaction, and resilience, there may yet be some underlying psychological factors that have not been explored in the present study.     

Effectiveness of Beck Depression Inventory-II subscales in screening for major depressive disorders in adolescent psychiatric inpatients

The Beck Depression Inventory-II (BDI-II) was administered to 45 male and 55 female psychiatric inpatients who were 12 to 17 years old, and the Mood Module from the Primary Care Evaluation of Mental Disorders (PRIME-MD) was used to determine whether these patients met criteria for a diagnosis of a DSM-IV major depressive disorder (MDD). Binormal receiver-operating-characteristic (ROC) analyses found that BDI-II total scores, Cognitive subscale scores, Noncognitive subscale scores, and embedded BDI FastScreen for Medical Patients subscale scores were comparably effective in differentiating inpatients who were and were not diagnosed with a MDD; the areas under the ROC curves were, respectively, .92 (95% confidence interval [CI]: .85-.96), .90 (95% CI: .82-.95), .90 (95% CI: .83-.95), and .90 (95% CI: .83-.95).     

Medical burden, cerebrovascular disease, and cognitive impairment in geriatric depression: modeling the relationships with the CART analysis

Herein, the background information reflecting roles of medical burden, cerebrovascular disease and risk factors, and cognitive impairment in geriatric depression are reviewed. The authors then propose a nonparametric statistical approach to the data analysis of multiple putative causal variables for late-life depression, the Classification and Regression Tree Analysis. This analysis presents a useful approach to modeling nonlinear relationships and interactions among variables measuring physical and mental health, as well as magnetic resonance imaging and cognitive measures in depressed elderly. This method uncovers the existing interactions among multiple predictor variables, and provide thresholds for each variable, at which its predictive power becomes statistically significant. It presents a "hierarchy" of the predictors in a form of a decision tree by finding the best combination of predictors of an outcome. The authors present two models based on demographic variables, measures of vascular and nonvascular medical burden, neuroimaging indices, the Mini-Mental State Examination score, and neuropsychological test scores of 81 elderly depressed subjects. Cognitive tests of verbal fluency and executive function are identified as the best predictors of depression, followed by the frontal lobe volume and Mini-Mental State Examination. The authors observed that an interaction between frontal lobe volume, total lesion volume, and medical burden was predictive of depression.     

Beck Depression Inventory-II items associated with self-reported symptoms of ADHD in adult psychiatric outpatients

The Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996) and the Conners' Adult ADHD Rating Scale-Self-Report: Screening Version (CAARS-S:SV; Conners, Erhardt, & Sparrow, 1999) were administered to 371 (64%) female and 204 (36%) male adult (> 18 years old) outpatients who were diagnosed with various psychiatric disorders to determine whether any of the 21 items or subsets of items in the BDI-II were related to symptoms of attention deficits and hyperactivity as measured by the CAARS-S:SV DSM-IV Total ADHD Symptoms scale (attention-deficit/hyperactivity disorder [ADHD] Symptoms). Stepwise multiple-regression analyses found that the BDI-II Concentration Difficulty explained 30% of the variance in these total scores. Ratings > 1 for the BDI-II Concentration Difficulty item were discussed as being useful for ruling out possible symptoms of ADHD.     

Lack of age differences in the Beck Depression Inventory-II scores of clinically depressed adolescent outpatients

To assess whether the mean Beck Depression Inventory-II scores of adolescents who were diagnosed with unipolar depressive disorders differed with respect to age, the inventory was administered to 144 (60%) female and 96 (40%) male outpatients between 13 and 17 yr. who were diagnosed with depressive disorders. The internal consistency of the scores was high (coefficient alpha=.89). A factorial analysis of variance was used to test for the main effects of age, sex, ethnicity, type of depressive disorder, comorbidity, and the two-way interactions of age with the other main effects. The mean scores were not differentiated by age or by the interactions of age with the other effects. The lack of age differences in this 5-year range on the mean scores of clinically depressed adolescents was discussed with respect to previous findings that have reported such differences in adolescents and adults.     

Use of the Conners-Wells' Adolescent Self-Report Scale: Short Form with Psychiatric Outpatients

To provide information about the psychometric properties of the Conners-Wells' Adolescent Self-Report Scale: Short Form (CASS:S; C. K. Conners, 1997), the CASS:S was administered to 110 (46%) female and 130 (54%) male psychiatric outpatients between 13 and 17 years old. The coefficient s for the CASS:S Conduct Problems, Cognitive Problems, Hyperactivity, and ADHD Index scales were, respectively, .73, .80, .84, and .80. An exploratory maximum-likelihood factor analysis of the 18 item ratings from the Conduct, Cognitive, and Hyperactive scales identified two positively correlated dimensions; one was composed of the six items from the Conduct scale, and the other was composed of the 12 items from the Cognitive and the Hyperactive scales. The results were discussed as supporting the clinical utility of the CASS:S.     

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20-item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam-algebra) and generated a one-word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk-based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes.     

Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective

Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal? This question impacts how far RL can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we study when an RL agent has an instrumental goal to tamper with its reward process, and describe design principles that prevent instrumental goals for two different types of reward tampering (reward function tampering and RF-input tampering). Combined, the design principles can prevent reward tampering from being an instrumental goal. The analysis benefits from causal influence diagrams to provide intuitive yet precise formalizations.

     

Texture development during cross rolling of a dual-phase Fe–Cr–Ni alloy: experiments and simulations

Texture development during multi-step cross rolling of a dual-phase Fe–Cr–Ni alloy has been investigated. X-ray diffraction was used to investigate changes in crystallographic texture of both the constituent phases (austenite and ferrite) through changes in orientation distribution function. After deformation, rotated brass (rotated along φ₁, i.e. the sample normal direction ND), along with a weak cube texture was observed in austenite, while a strong rotated cube texture was obtained in ferrite. Texture was also simulated for various strains using a co-deformation model by self-consistent visco-plastic (VPSC) formulation. Simulations showed strong rotated brass texture in austenite and a strongly rotated cube, α-fibre (sample rolling direction RD //<1 1 0>) and γ-fibre (ND //<1 1 1>) in ferrite after highest strain (ε_t = 1.6). VPSC models could not effectively capture the change in crystallographic texture during cross rolling. In ferrite, simulations showed an overestimation of γ-fibre component and an underestimation of rotated cube component. Simulated texture of austenite, on the other hand, showed an overestimation of rotated brass with an absence of cube component. The results are rationalised based on the possible role of shear banding and activation of non-octahedral slip system during cross rolling, both of which are not incorporated in conventional VPSC models.