Benjamin M. Rottman

Can Clinicians Recognize DSM-IV Personality Disorders From Five-Factor Model Descriptions of Patient Cases?

OBJECTIVE This article examined, using theories from cognitive science, the clinical utility of the Five-Factor Model (FFM) of Personality, an assessment and classification system under consideration for integration into the forthcoming fifth edition of the Diagnostic and Statistical Manual (DSM) of Mental Disorders. Specifically, the authors sought to test whether FFM descriptors are specific enough to allow practicing clinicians to capture core features of personality disorders. METHOD In two studies, a large nationwide sample of clinical psychologists, psychiatrists, and clinical social workers (N=187 and N=191) were presented case profiles based on symptom formats from either the DSM-IV and/or FFM. Ratings for six aspects of clinical utility for DSM-IV and FFM profiles were obtained and participants provided DSM-IV diagnoses. Prototypic cases (only one personality disorder) and comorbid cases were tested in separate studies. RESULTS Participants rated the DSM-IV as more clinically useful than the FFM on five out of six clinical utility questions. Despite demonstrating considerable background knowledge of DSM-IV diagnoses, participants had difficulty identifying correct diagnoses from FFM profiles. CONCLUSION The FFM descriptors may be more ambiguous than the criteria of the DSM-IV and the FFM may therefore be less able to convey important clinical details than the DSM-IV. The findings flag challenges to clinical utility for dimensional-trait systems such as the FFM.

Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena

We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.

Causal Structure Learning over Time: Observations and Interventions

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically.

Parental Numeric Language Input to Mandarin Chinese and English Speaking Preschool Children.

The present study examined the number-specific parental language input to Mandarin- and English-speaking preschool-aged children. Mandarin and English transcripts from the CHILDES database were examined for amount of numeric speech, specific types of numeric speech and syntactic frames in which numeric speech appeared. The results showed that Mandarin-speaking parents talked about number more frequently than English-speaking parents. Further, the ways in which parents talked about number terms in the two languages was more supportive of a cardinal interpretation in Mandarin than in English. We discuss these results in terms of their implications for numerical understanding and later mathematical performance.

What matters in scientific explanations: Effects of elaboration and content

Given the breadth and depth of available information, determining which components of an explanation are most important is a crucial process for simplifying learning. Three experiments tested whether people believe that components of an explanation with more elaboration are more important. In Experiment 1, participants read separate and unstructured components that comprised explanations of real-world scientific phenomena, rated the components on their importance for understanding the explanations, and drew graphs depicting which components elaborated on which other components. Participants gave higher importance scores for components that they judged to be elaborated upon by other components. Experiment 2 demonstrated that experimentally increasing the amount of elaboration of a component increased the perceived importance of the elaborated component. Furthermore, Experiment 3 demonstrated that elaboration increases the importance of the elaborated information by providing insight into understanding the elaborated information; information that was too technical to provide insight into the elaborated component did not increase the importance of the elaborated component. While learning an explanation, people piece together the structure of elaboration relationships between components and use the insight provided by elaboration to identify important components.

Causal learning about tolerance and sensitization

We introduce two abstract, causal schemata used during causal learning. (1) Tolerance is when an effect diminishes over time, as an entity is repeatedly exposed to the cause (e.g., a person becoming tolerant to caffeine). (2) Sensitization is when an effect intensifies over time, as an entity is repeatedly exposed to the cause (e.g., an antidepressant becoming more effective through repeated use). In Experiment 1, participants observed either of these cause—effect data patterns unfolding over time and exhibiting the tolerance or sensitization schemata. Participants inferred stronger causal efficacy and made more confident and more extreme predictions about novel cases than in a condition with the same data appearing in a random order over time. In Experiment 2, the same tolerance/sensitization scenarios occurred either within one entity or across many entities. In the manyentity conditions, when the schemata were violated, participants made much weaker inferences. Implications for causal learning are discussed.

Children Use Temporal Cues to Learn Causal Directionality

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real-world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.

Searching for the best cause: Roles of mechanism beliefs, autocorrelation, and exploitation.

When testing which of multiple causes (e.g., medicines) works best, the testing sequence has important implications for the validity of the final judgment. Trying each cause for a period of time before switching to the other is important if the causes have tolerance, sensitization, delay, or carryover (TSDC) effects. In contrast, if the outcome variable is autocorrelated and gradually fluctuates over time rather than being random across time, it can be useful to quickly alternate between the 2 causes, otherwise the causes could be confounded with a secular trend in the outcome. Five experiments tested whether individuals modify their causal testing strategies based on beliefs about TSDC effects and autocorrelation in the outcome. Participants adaptively tested each cause for longer periods of time before switching when testing causal interventions for which TSDC effects were plausible relative to cases when TSDC effects were not plausible. When the autocorrelation in the baseline trend was manipulated, participants exhibited only a small (if any) tendency toward increasing the amount of alternation; however, they adapted to the autocorrelation by focusing on changes in outcomes rather than raw outcome scores, both when making choices about which cause to test as well as when making the final judgment of which cause worked best. Understanding how people test causal relations in diverse environments is an important first step for being able to predict when individuals will successfully choose effective causes in real-world settings. (PsycINFO Database Record

Medication Adherence as a Learning Process: Insights from Cognitive Psychology.

ABSTRACT Non-adherence to medications is one of the largest contributors to sub-optimal health outcomes. Many theories of adherence include a ‘value–expectancy’ component in which a patient decides to take a medication partly based on expectations about whether it is effective, necessary, and tolerable. We propose reconceptualising this common theme as a kind of ‘causal learning’ – the patient learns whether a medication is effective, necessary, and tolerable, from experience with the medication. We apply cognitive psychology theories of how people learn cause–effect relations to elaborate this causal-learning challenge. First, expectations and impressions about a medication and beliefs about how a medication works, such as delay of onset, can shape a patient’s perceived experience with the medication. Second, beliefs about medications propagate both ‘top-down’ and ‘bottom-up’, from experiences with specific medications to general beliefs about medications and vice versa. Third, non-adherence can interfere with learning about a medication, because beliefs, adherence, and experience with a medication are connected in a cyclic learning problem. We propose that by conceptualising non-adherence as a causal-learning process, clinicians can more effectively address a patient’s misconceptions and biases, helping the patient develop more accurate impressions of the medication.

Do capuchin monkeys (Cebus apella) diagnose causal relations in the absence of a direct reward

We adapted a method from developmental psychology [1] to explore whether capuchin monkeys (Cebus apella) would place objects on a “blicket detector” machine to diagnose causal relations in the absence of a direct reward. Across five experiments, monkeys could place different objects on the machine and obtain evidence about the objects’ causal properties based on whether each object “activated” the machine. In Experiments 1–3, monkeys received both audiovisual cues and a food reward whenever the machine activated. In these experiments, monkeys spontaneously placed objects on the machine and succeeded at discriminating various patterns of statistical evidence. In Experiments 4 and 5, we modified the procedure so that in the learning trials, monkeys received the audiovisual cues when the machine activated, but did not receive a food reward. In these experiments, monkeys failed to test novel objects in the absence of an immediate food reward, even when doing so could provide critical information about how to obtain a reward in future test trials in which the food reward delivery device was reattached. The present studies suggest that the gap between human and animal causal cognition may be in part a gap of motivation. Specifically, we propose that monkey causal learning is motivated by the desire to obtain a direct reward, and that unlike humans, monkeys do not engage in learning for learning’s sake.