Does the A-not-B error in adult pet dogs indicate sensitivity to human communication?

Recent dog-infant comparisons have indicated that the experimenter's communicative signals in object hide-and-search tasks increase the probability of perseverative (A-not-B) errors in both species (Topál et al. 2009). These behaviourally similar results, however, might reflect different mechanisms in dogs and in children. Similar errors may occur if the motor response of retrieving the object during the A trials cannot be inhibited in the B trials or if the experimenter's movements and signals toward the A hiding place in the B trials ('sham-baiting') distract the dogs' attention. In order to test these hypotheses, we tested dogs similarly to Topál et al. (2009) but eliminated the motor search in the A trials and 'sham-baiting' in the B trials. We found that neither an inability to inhibit previously rewarded motor response nor insufficiencies in their working memory and/or attention skills can explain dogs' erroneous choices. Further, we replicated the finding that dogs have a strong tendency to commit the A-not-B error after ostensive-communicative hiding and demonstrated the crucial effect of socio-communicative cues as the A-not-B error diminishes when location B is ostensively enhanced. These findings further support the hypothesis that the dogs' A-not-B error may reflect a special sensitivity to human communicative cues. Such object-hiding and search tasks provide a typical case for how susceptibility to human social signals could (mis)lead domestic dogs.     

Individual differences in cognitive plasticity: an investigation of training curves in younger and older adults

To date, cognitive intervention research has provided mixed but nevertheless promising evidence with respect to the effects of cognitive training on untrained tasks (transfer). However, the mechanisms behind learning, training effects and their predictors are not fully understood. Moreover, individual differences, which may constitute an important factor impacting training outcome, are usually neglected. We suggest investigating individual training performance across training sessions in order to gain finer-grained knowledge of training gains, on the one hand, and assessing the potential impact of predictors such as age and fluid intelligence on learning rate, on the other hand. To this aim, we propose to model individual learning curves to examine the intra-individual change in training as well as inter-individual differences in intra-individual change. We recommend introducing a latent growth curve model (LGCM) analysis, a method frequently applied to learning data but rarely used in cognitive training research. Such advanced analyses of the training phase allow identifying factors to be respected when designing effective tailor-made training interventions. To illustrate the proposed approach, a LGCM analysis using data of a 10-day working memory training study in younger and older adults is reported.     

Persönlichkeitsfragebögen

The influence of defensivity, measured by validity scales of personality inventories (MMPI-K, FPI-R, GIESSEN-Test) on test results and psychotherapy outcome was studied in 309 patients treated with inpatient client-centered therapy. In general, psychotherapy patients exhibited low defensivity. 25% of patients, who denied psychopathology at admission, did so at discharge and to some extent at follow-up, demonstrating an ”especially good outcome” (means). Relative improvement (effect sizes), however, was significantly diminished in psychopathology scales and significantly enlarged in validity scales. – Results are discussed with regard to prior hypotheses about the influence of defensivity.     

Time-varying decision boundaries: insights from optimality analysis

The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this evidence reaches a decision boundary. It is frequently assumed that this decision boundary stays constant during a decision, depending on the evidence collected but not on time. Recent experimental and theoretical work has challenged this assumption, showing that constant decision boundaries are, in some circumstances, sub-optimal. We introduce a theoretical model that facilitates identification of the optimal decision boundaries under a wide range of conditions. Time-varying optimal decision boundaries for our model are a result only of uncertainty over the difficulty of each trial and do not require decision deadlines or costs associated with collecting evidence, as assumed by previous authors. Furthermore, the shape of optimal decision boundaries depends on the difficulties of different decisions. When some trials are very difficult, optimal boundaries decrease with time, but for tasks that only include a mixture of easy and medium difficulty trials, the optimal boundaries increase or stay constant. We also show how this simple model can be extended to more complex decision-making tasks such as when people have unequal priors or when they can choose to opt out of decisions. The theoretical model presented here provides an important framework to understand how, why, and whether decision boundaries should change over time in experiments on decision-making.     

Obey or not obey? Dogs (Canis familiaris) behave differently in response to attentional states of their owners

Sixteen domestic dogs (Canis familiaris) were tested in a familiar context in a series of 1-min trials on how well they obeyed after being told by their owner to lie down. Food was used in 1/3 of all trials, and during the trial the owner engaged in 1 of 5 activities. The dogs behaved differently depending on the owner's attention to them. When being watched by the owner, the dogs stayed lying down most often and/or for the longest time compared with when the owner read a book, watched TV, turned his or her back on them, or left the room. These results indicate that the dogs sensed the attentional state of their owners by judging observable behavioral cues such as eye contact and eye, head, and body orientation.     

The ad coefficient as a descriptive measure of the within‐group agreement of ratings

The a_d coefficient was developed to measure the within‐group agreement of ratings. The underlying theory as well as the construction of the coefficient are explained. The a_d coefficient ranges from 0 to 1, regardless of the number of scale points, raters, or items. With some limitations the measure of the within‐group agreement of different groups and groups from different studies is directly comparable. For statistical significance testing, the binomial distribution is introduced as a model of the ratings' random distribution given the true score of a group construct. This method enables a decision about essential agreement and not only about a significant difference from 0 or a chosen critical value. The a_d coefficient identifies a single true score within a group. It is not provided for multiple true score settings. The comparison of the a_d coefficient with other agreement indices shows that the new coefficient is in line with their outcomes, but does not result in infinite or inappropriate values.