Counselors’ Mandated Responsibility to Report Child Maltreatment: A Review of U.S. Laws

Although counselors in 44 states are mandatory reporters of child maltreatment, they may lack an understanding of their legally designated role. This article presents the results of a systematic review of child maltreatment reporting laws in all 50 states and the District of Columbia. The authors apply relevant legislation to real‐life contexts for counselors.     

On the influence of causal beliefs on the feeling of agency

The sense of agency is the experience of being the origin of a sensory consequence. This study investigates whether contextual beliefs modulate low-level sensorimotor processes which contribute to the emergence of the sense of agency. We looked at the influence of causal beliefs on ‘intentional binding’, a phenomenon which accompanies self-agency. Participants judged the onset-time of either an action or a sound which followed the action. They were induced to believe that the tone was either triggered by themselves or by somebody else, although, in reality, the sound was always triggered by the participants. We found that intentional binding was stronger when participants believed that they triggered the tone, compared to when they believed that another person triggered the tone. These results suggest that high-level contextual information influences sensorimotor processes responsible for generating intentional binding.     

Prime and probability: causal knowledge affects inferential and predictive effects on self-agency experiences

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their (combined) contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence (50% or 80%) of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.     

Evidence that a transient but cognitively demanding process underlies forward blocking

Blocking is a learning phenomenon in which prior experience inhibits learning about novel cues. Though the phenomenon itself has been well documented, the details of blocking-related processes still remain contentious. Two experiments investigated whether participants were engaged in demanding cognitive processing during different portions of a standard blocking paradigm. Participants in Experiment 1 engaged in a simple secondary task while completing a standard blocking procedure. Results showed that performance on the secondary task was briefly diminished early in the second phase of the blocking paradigm, when the novel cue is first paired with the pretrained cue. Participants in Experiment 2 performed a difficult cognitive load task during either the early or the late portions of the second phase of blocking. The blocking effect was eliminated when learners were under load early in the second phase, but not when learners were under load late in the second phase. These results suggest that blocking relies on a cognitively demanding process with a distinct time course. Computational simulations illustrate how a model that includes top-down (i.e., cognitively demanding) attentional modulation can reproduce the observed behaviour. This suggests that purely associative processes are not sufficient to explain the observed behaviour. Implications for current accounts of blocking are discussed.     

Technical intelligence in animals: the kea model

The ability to act on information flexibly is one of the cornerstones of intelligent behavior. As particularly informative example, tool-oriented behavior has been investigated to determine to which extent nonhuman animals understand means-end relations, object affordances, and have specific motor skills. Even planning with foresight, goal-directed problem solving and immediate causal inference have been a focus of research. However, these cognitive abilities may not be restricted to tool-using animals but may be found also in animals that show high levels of curiosity, object exploration and manipulation, and extractive foraging behavior. The kea, a New Zealand parrot, is a particularly good example. We here review findings from laboratory experiments and field observations of keas revealing surprising cognitive capacities in the physical domain. In an experiment with captive keas, the success rate of individuals that were allowed to observe a trained conspecific was significantly higher than that of naive control subjects due to their acquisition of some functional understanding of the task through observation. In a further experiment using the string-pulling task, a well-probed test for means-end comprehension, we found the keas finding an immediate solution that could not be improved upon in nine further trials. We interpreted their performance as insightful in the sense of being sensitive of the relevant functional properties of the task and thereby producing a new adaptive response without trial-and-error learning. Together, these findings contribute to the ongoing debate on the distribution of higher cognitive skills in the animal kingdom by showing high levels of sensorimotor intelligence in animals that do not use tools. In conclusion, we suggest that the 'Technical intelligence hypothesis' (Byrne, Machiavellian intelligence II: extensions and evaluations, pp 289-211, 1997), which has been proposed to explain the origin of the ape/monkey grade-shift in intelligence by a selection pressure upon an increased efficiency in foraging behavior, should be extended, that is, applied to some birds as well.     

Distinguishing Between Causes and Enabling Conditions—Through Mental Models or Linguistic Cues?

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson-Laird, 2001, Cognitive Science, 25, 565–610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that frame which event to assume as given (the enabling condition) and which to consider as responsible for the effect under this assumption (the cause). By disentangling these two factors, we were able to show that when identifying causes and enabling conditions in these tasks, people rely strongly on the linguistic cues but not on the proposed set of models and that this set of models does not even reflect people's typical interpretation of the tasks. We propose an alternative explanation that integrates syntactic and causal considerations.     

Time and Singular Causation—A Computational Model

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co-occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model.