Free will fallibilism and the “two-standpoints” account of freedom

Synthese - In this paper I propose a form of free will fallibilism. Unlike the free will realist who is fully persuaded that we have sufficient evidence of freedom to justify holding individuals...     

Applying the self-determination theory continuum to unhealthy eating: Consequences on well-being and behavioral frequency

The present research applies self-determination theory (SDT) to the context of unhealthy eating. The extent to which each of the six types of motivations stemming from the SDT continuum applies to unhealthy eating is examined, as well as the contribution of each motivation for eating unhealthily in predicting psychological well-being and frequency of unhealthy eating. A three-wave longitudinal study (N = 379) was conducted before, during, and after the Christmas holidays. Results demonstrated that three types of motivations (i.e., identified, introjected, and external regulation) for unhealthy eating fluctuated over time and peaked during the holidays, a time when unhealthy eating becomes especially salient in Western societies. However, amotivation for unhealthy eating reached its lowest level during the holidays. While identified regulation was associated with greater well-being, introjected regulation, and amotivation for unhealthy eating were linked with lower well-being. Integrated regulation was associated with lower well-being only before and after the holiday period. Finally, the integrated, introjected, external regulations, and amotivation were linked to higher frequency of unhealthy eating. These results confirm that each type of motivation presents distinct patterns of associations with well-being and actual behavior. Results also demonstrate that the social context in which eating takes place can have an impact on the relationship between motivations and well-being as well as behavior.     

Examining the item response process to personality measures in high-stakes situations: Issues of measurement validity and predictive validity

We conducted two experimental studies with between-subjects and within-subjects designs to investigate the item response process for personality measures administered in high- versus low-stakes situations. Apart from assessing measurement validity of the item response process, we examined predictive validity; that is, whether or not different response models entail differential selection outcomes. We found that ideal point response models fit slightly better than dominance response models across high- versus low-stakes situations in both studies. Additionally, fitting ideal point models to the data led to fewer items displaying differential item functioning compared to fitting dominance models. We also identified several items that functioned as intermediate items in both the faking and honest conditions when ideal point models were fitted, suggesting that ideal point model is “theoretically” more suitable across these contexts for personality inventories. However, the use of different response models (dominance vs. ideal point) did not have any substantial impact on the validity of personality measures in high-stakes situations, or the effectiveness of selection decisions such as mean performance or percent of fakers selected. These findings are significant in that although prior research supports the importance and use of ideal point models for measuring personality, we find that in the case of personality faking, though ideal point models seem to have slightly better measurement validity, the use of dominance models may be adequate with no loss to predictive validity.     

The typology of parricide and the role of mental illness: Data-driven approach

Parricide is a rare type of homicide in which mental illness is often an important factor. The aims of this study were (a) to describe the characteristics of parricide offenders with a focus on mental illness and clinical care and (b) to examine Heide's widely used typology of parricide through a data-driven approach. We analyzed all homicides in England and Wales between 1997 and 2014. Parricide offenders in our sample were most often male, unmarried, and unemployed, with a third of offenders diagnosed with schizophrenia; 28% had been in contact with mental health services before the offense. The latent class analysis resulted in three types of parricide offenders: middle-aged with affective disorder, previously abused, and seriously mentally Ill, which confirmed, to an extent, Heide's typology. Health and social care services should actively engage with carers of people with mental illness and support to those caring for older relatives and victims of abuse.     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology     

Diagnostic validity statistics and the MCMI-III

This article (a) describes and illustrates the nonredundant and clinically important information that may be obtained from 5 diagnostic validity statistics (DVSs): incremental validities of positive and negative test diagnoses, Cohen's kappas, Cohen's effect sizes, and areas under receiver operating characteristic (ROC) curves and (b) determines values of these DVSs for 24 Million Multiaxial Clinical Inventory III scales from results reported in 1994 and 1997 validity studies. The DVSs for the 1997 study (T. Millon, R. Davis, & C. Millon, 1997) were often more than 3 times larger than corresponding DVSs for the 1994 study (T. Millon, 1994). The author suggests these large differences could reflect not only effects of factors that caused underestimation of validities by DVSs of the 1994 study, but also effects of factors that may have caused overestimation of validities by DVSs of the 1997 study.     

The role of reinforcement contingencies in chronic alcoholism: An experimental analysis of one case

investigators have questioned chronic alcoholics and round that some of them have returned to normal social drinking (Davies, 1962; Lemere, 1953; Nørvig and Nielsen, 1956; Selzer and Holloway. 1957; and Shea, 1954). These findings would seem inconsistent with such notions as loss of control or craving as the basis of compulsive drinking. Moreover, antecedents and consequences of these explanatory constructs, have not been elucidated. The purpose of this research was to make an intensive study of a single subject to see if specific conditions could be identified under which a chronic alcoholic's drinking could be controlled, in a setting in which environmental variables could be systematically varied and drinking behavior documented.     

How Should a Speech Recognizer Work?

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.     

Biases of success rate differences shown in binomial effect size displays

The intent of a binomial effect size display (BESD) is to show "the [real-world] importance of [an] effect indexed by a correlation [r]" (R. Rosenthal, 1994, p. 242) by reexpressing this correlation as a success rate difference (SRD) (e.g., treatment group success rate-control group success rate). However, SRDs displayed in BESDs generally overestimate real-world SRDs implied by correlations of (a) dichotomous X and Y variables (phi coefficients), (b) dichotomous X and continuous Y variables (point-biserial coefficients [rpbs]). and (c) continuous X and Y variables (rxys). Furthermore, overestimation biases are larger for rxys than for rpbs. Differences in the sizes of biases linked to different correlations suggest that BESD SRDs reported for different correlations are not comparable. The stochastic difference index (N. Cliff, 1993: A. Vargha & H. D. Delaney, 2000) is recommended as an alternative to the BESD.