An Entropy Model for Artificial Grammar Learning

A model is proposed to characterize the type of knowledge acquired in artificial grammar learning (AGL). In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy

How cognitive biases can distort environmental statistics: introducing the rough estimation task

The purpose of this study was to develop a novel behavioural method to explore cognitive biases. The task, called the Rough Estimation Task, simply involves presenting participants with a list of words that can be in one of three categories: appetitive words (e.g. alcohol, food, etc.), neutral related words (e.g. musical instruments) and neutral unrelated words. Participants read the words and are then asked to state estimates for the percentage of words in each category. Individual differences in the propensity to overestimate the proportion of appetitive stimuli (alcohol-related or food-related words) in a word list were associated with behavioural measures (i.e. alcohol consumption, hazardous drinking, BMI, external eating and restrained eating, respectively), thereby providing evidence for the validity of the task. The task was also found to be associated with an eye-tracking attentional bias measure. The Rough Estimation Task is motivated in relation to intuitions with regard to both the behaviour of interest and the theory of cognitive biases in substance use

Substance usage intention does not affect attentional bias: implications from Ecstasy/MDMA users and alcohol drinkers

Background: An attentional bias towards substance-related stimuli has been demonstrated with alcohol drinkers and many other types of substance user. There is evidence to suggest that the strength of an attentional bias may vary as a result of context (or use intention), especially within Ecstasy/MDMA users. Objective: Our aim was to empirically investigate attentional biases by observing the affect that use intention plays in recreational MDMA users and compare the findings with that of alcohol users. Method: Regular alcohol drinkers were compared with MDMA users. Performance was assessed for each group separately using two versions of an eye-tracking attentional bias task with pairs of matched neutral, and alcohol or MDMA-related visual stimuli. Dwell time was recorded for alcohol or MDMA. Participants were tested twice, when intending and not intending to use MDMA or alcohol. Note, participants in the alcohol group did not complete any tasks which involved MDMA-related stimuli and vice versa. Results: Significant attentional biases were found with both MDMA and alcohol users for respective substance-related stimuli, but not control stimuli. Critically, use intention did not affect attentional biases. Attentional biases were demonstrated with both MDMA users and alcohol drinkers when usage was and was not intended. Conclusions: These findings demonstrate the robust nature of attentional biases i.e. once an attentional bias has developed, it is not readily affected by intention

Cognitive biases to healthy and unhealthy food words predict change in BMI

The current study explored the predictive value of cognitive biases to food cues (assessed by emotional Stroop and dot probe tasks) on weight change over a 1-year period. This was a longitudinal study with undergraduate students (N = 102) living in shared student accommodation. After controlling for the effects of variables associated with weight (e.g., physical activity, stress, restrained eating, external eating, and emotional eating), no effects of cognitive bias were found with the dot probe. However, for the emotional Stroop, cognitive bias to unhealthy foods predicted an increase in BMI whereas cognitive bias to healthy foods was associated with a decrease in BMI. Results parallel findings in substance abuse research; cognitive biases appear to predict behavior change. Accordingly, future research should consider strategies for attentional retraining, encouraging individuals to reorient attention away from unhealthy eating cues

Quantum principles in psychology: The debate, the evidence, and the future

The attempt to employ quantum principles for modeling cognition has enabled the introduction of several new concepts in psychology, such as the uncertainty principle, incompatibility, entanglement, and superposition. For many commentators, this is an exciting opportunity to question existing formal frameworks (notably classical probability theory) and explore what is to be gained by employing these novel conceptual tools. This is not to say that major empirical challenges are not there. For example, can we definitely prove the necessity for quantum, as opposed to classical, models? Can the distinction between compatibility and incompatibility inform our understanding of differences between human and non-human cognition? Are quantum models less constrained than classical ones? Does incompatibility arise as a limitation, to avoid the requirements from the principle of unicity, or is it an inherent (or essential?) characteristic of intelligent thought? For everyday judgments, do quantum principles allow more accurate prediction than classical ones? Some questions can be confidently addressed within existing quantum models. A definitive resolution of others will have to anticipate further work. What is clear is that the consideration of quantum cognitive models has enabled a new focus on a range of debates about fundamental aspects of cognition

Separate influences in learning: Evidence from artificial grammar learning with traumatic brain injury patients

Artificial grammar learning (AGL) is one of the most extensively employed paradigms for the study of learning. Grammaticality is one of the most common ways to index performance in AGL. However, there is still extensive debate on whether there is a distinct psychological process which can lead to grammaticality knowledge. An application of the COVIS model of categorization in AGL suggests that grammaticality might arise from a hypothesis-testing system (when grammaticality is appropriately balanced with other knowledge influences), so that prefrontal cortex damage should be associated with impaired grammaticality and intact chunk strength performance. This prediction was confirmed in a study of traumatic brain injury (TBI) patients and matched controls. The TBI patient cohort had diffuse prefrontal cortex damage as evidenced by the history of their injury, CT scans, and severe executive functioning problems. Our results allow a novel interpretation of grammaticality and AGL in general

Development and validation of a Food Preoccupation Questionnaire

Existing Food Preoccupation Questionnaires do not take account of food-related thoughts that have a positive emotional valence. We report on the development and validation of a questionnaire that provides independent assessments of thought frequency and emotional valence (positive, negative or neutral)