A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms:\ud 1) perceptual learning, the learning of new objects, categories, relations, etc.,\ud 2) episodic learning of events, the what, where, and when,\ud 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence

How the stimulus influences mind wandering in semantically rich task contexts

What do we think about when we mind wander and where do these thoughts come from? We tested the idea that semantically rich stimuli yield patterns of mind wandering that are closely coupled with the stimuli compared to being more internally triggered. We analyzed the content of 949 self-reported zone outs (1218 thoughts) and 519 of their triggers from 88 participants who read an instructional text and watched a film for 20 min each. We found that mind wandering associated with memory retrieval was more frequent than prospection and introspection across both stimuli. Over 70% of autobiographical and semantic memory retrievals were triggered by the content of the stimuli, compared to around 30% for prospective and introspective thoughts. Further, latent semantic analysis revealed that semantic and unspecific memories were more “semantically” similar to their triggers than prospective and introspective thoughts, suggesting that they arise from spontaneous associations with the stimulus. These findings suggest a re-evaluation of how internal concerns and the external world give rise to mind wandering and emphasize the importance of studying mind wandering in semantically rich contexts akin to much of the real world

Driven to distraction: A lack of change gives rise to mind wandering

How does the dynamic structure of the external world direct attention? We examined the relationship between event structure and attention to test the hypothesis that narrative shifts (both theoretical and perceived) negatively predict attentional lapses. Self-caught instances of mind wandering were collected while 108 participants watched a 32.5 min film called The Red Balloon. We used theoretical codings of situational change and human perceptions of event boundaries to predict mind wandering in 5-s intervals. Our findings suggest a temporal alignment between the structural dynamics of the film and mind wandering reports. Specifically, the number of situational changes and likelihood of perceiving event boundaries in the prior 0–15 s interval negatively predicted mind wandering net of low-level audiovisual features. Thus, mind wandering is less likely to occur when there is more event change, suggesting that narrative shifts keep attention from drifting inwards

The Eye-Mind Wandering Link: Identifying Gaze Indices of Mind Wandering Across Tasks

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Gaze-based signatures of mind wandering during real-world scene processing

Item does not contain fulltextPhysiological limitations on the visual system require gaze to move from location to location to extract the most relevant information within a scene. Therefore, gaze provides a real-time index of the information-processing priorities of the visual system. We investigated gaze allocation during mind wandering (MW), a state where cognitive priorities shift from processing task-relevant external stimuli (i.e., the visual world) to task-irrelevant internal thoughts. In both a main study and a replication, we recorded the eye movements of college-aged adults who studied images of urban scenes and responded to pseudorandom thought probes on whether they were mind wandering or attentively viewing at the time of the probe. Probe-caught MW was associated with fewer and longer fixations, greater fixation dispersion, and more frequent eyeblinks (only observed in the main study) relative to periods of attentive scene viewing. These findings demonstrate that gaze indices typically considered to represent greater engagement with scene processing (e.g., longer fixations) can also indicate MW. In this way, the current work exhibits a need for empirical investigations and computational models of gaze control to account for MW for a more accurate representation of the moment-to-moment information-processing priorities of the visual system

Moment-to-Moment Detection of Internal Thought from Eye Vergence Behaviour

Internal thought refers to the process of directing attention away from a primary visual task to internal cognitive processing. Internal thought is a pervasive mental activity and closely related to primary task performance. As such, automatic detection of internal thought has significant potential for user modelling in intelligent interfaces, particularly for e-learning applications. Despite the close link between the eyes and the human mind, only a few studies have investigated vergence behaviour during internal thought and none has studied moment-to-moment detection of internal thought from gaze. While prior studies relied on long-term data analysis and required a large number of gaze characteristics, we describe a novel method that is computationally light-weight and that only requires eye vergence information that is readily available from binocular eye trackers. We further propose a novel paradigm to obtain ground truth internal thought annotations that exploits human blur perception. We evaluate our method for three increasingly challenging detection tasks: (1) during a controlled math-solving task, (2) during natural viewing of lecture videos, and (3) during daily activities, such as coding, browsing, and reading. Results from these evaluations demonstrate the performance and robustness of vergence-based detection of internal thought and, as such, open up new directions for research on interfaces that adapt to shifts of mental attention