Eye movements in response to different cognitive activities measured by eyetracking: a prospective study on some of the neurolinguistics programming theories.

The eyes are in constant movement to optimize the interpretation of the visual scene by the brain. Eye movements are controlled by complex neural networks that interact with the rest of the brain. The direction of our eye movements could thus be influenced by our cognitive activity (imagination, internal dialogue, memory, etc.). A given cognitive activity could then cause the gaze to move in a specific direction (a brief movement that would be instinctive and unconscious). Neuro Linguistic Programming (NLP), which was developed in the 1970s by Richard Bandler and John Grinder (psychologist and linguist respectively), issued a comprehensive theory associating gaze directions with specific mental tasks. According to this theory, depending on the visual path observed, one could go back to the participant's thoughts and cognitive processes. Although NLP is widely used in many disciplines (communication, psychology, psychotherapy, marketing, etc), to date, few scientific studies have examined the validity of this theory. Using eye tracking, this study explores one of the hypotheses of this theory, which is one of the pillars of NLP on visual language. We created a protocol based on a series of questions of different types (supposed to engage different brain areas) and we recorded by eye tracking the gaze movements at the end of each question while the participants were thinking and elaborating on the answer. Our results show that 1) complex questions elicit significantly more eye movements than control questions that necessitate little reflection, 2) the movements are not random but are oriented in selected directions, according to the different question types, 3) the orientations observed are not those predicted by the NLP theory. This pilot experiment paves the way for further investigations to decipher the close links between eye movements and neural network activities in the brain.

Neural correlates of cue predictiveness during intentional and incidental associative learning: A time-frequency study.

Incidental learning allows us to extract statistical relations between events in our daily lives without the intention to learn them. Whereas anticipation during intentional associative learning has been linked to increased and decreased theta band activity, comparatively little research has focused on incidental learning. The study of such a pervasive mechanism of incidental learning faces the challenge of finding an appropriate paradigm. Similarly, while posterior alpha band activity has been shown to facilitate attention to a predictable target location, it is not clear whether alpha power could mediate attention given other predictive information; e.g., when the only available information provided by the cue is the likelihood of the target outcome. Here we used a stimulus-stimulus associative learning task to investigate whether a cue carries information on its contingent relationship with a target outcome, not only when their relationship is learned intentionally but also when it could be learned incidentally. Moreover, by presenting the target outcome in a visual search task, we were also able to study whether anticipatory attention can be modulated by the intentional or the incidental knowledge of the likelihood of a target outcome given a predictive (or non-predictive) cue. Participants were exposed to streams of cue-target outcome trials, where one of two possible cues and one of two possible outcomes were displayed. Intention to learn was manipulated by asking participants to assess whether one of the target outcomes (the intentional one) was more likely to appear following one of the cues (the intentional one). Any learning regarding the other cue-outcome relationship would be incidental. We found that frontal and temporal theta band activity were sensitive to the predictive value of a cue (predictive cues elicited lower theta power). Moreover, left temporal theta was sensitive to the intention to learn associations (theta activity elicited by intentional learning cues was higher). Alpha power, by contrast, was not modulated by cue predictiveness of the target outcome. These findings suggest that theta band activity carries information about the predictive value of a cue. The topographical differences between theta for intentional and incidental learning suggest distinct cortical networks activated depending on whether the relationship between a cue and an outcome has been learned intentionally or incidentally.

Guidance of spatial attention during associative learning: Contributions of predictability and intention to learn.

Expectations of an event can facilitate its neural processing. One of the ways we build expectations is through associative learning. Interestingly, the learning of contingencies between events can also occur without intention. Here, we study feature-based attention during associative learning, by asking how a learned association between a cue and a target outcome impacts the attention allocated to this outcome. Moreover, we investigate attention in learning depending on the intention to learn the association. We used an associative learning paradigm where we manipulated outcome predictability and intention to learn an association within streams of cue-target outcome visual stimuli, while stimulus characteristics and probability were held constant. In order to measure the event-related component N2pc, widely recognized to reflect allocation of spatial attention, every outcome was embedded among distractors. Importantly, the location of the target outcome could not be anticipated. We found that predictable target outcomes showed an increased spatial attention as indexed by a greater N2pc component. A later component, the P300, was sensitive to the intention to learn the association between the cue and the target outcome. The current study confirms the remarkable ability of the brain to extract and update predictive information, in accordance with a predictive-coding model of brain function. Associative learning can guide a visual search and shape covert attentional selection in our rich environments.