Entraining IDyOT: Timing in the Information Dynamics of Thinking

Contrast Information Dynamics: A Novel Information Measure for Cognitive Modelling

We present contrast information, a novel application of some specific cases of relative entropy, designed to be useful for the cognitive modelling of the sequential perception of continuous signals. We explain the relevance of entropy in the cognitive modelling of sequential phenomena such as music and language. Then, as a first step to demonstrating the utility of constrast information for this purpose, we empirically show that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally, we propose future work toward a cognitive architecture that uses it.

Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D, and augmented reality

The introduction of Augmented Reality (AR) has attracted several developments, although the people's experience of AR has not been clearly studied or contrasted with the human experience in 2D and 3D environments. Here, the directional task was applied in 2D, 3D, and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted was also done. Research question: Are the main differences between 2D, 3D, and AR able to be predicted using Markov chains? Methods: A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants attached to the arranged RT for the theory of Markov chain probability. A Markov chain analysis was performed on the participants' data. Subsequently, the way certain factors influenced participants RT amongst the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statistically tested using ANOVA. Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D tasks with the variance predicted with the current state. Moreover, a clear explanation of delayed RT in every environment was done. Mood and coffee did not show significant differences in RTs on a simplified videogame. Gender differences were found in 3D, where endogenous directional goals are in 3D, but no gender differences appeared in AR where exogenous AR buttons can explain the larger RT that compensate for the gender difference. Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions in different environments and gender evolve from the dynamics of preceding cognitive activity can fit and improve neurocomputational models.

Understanding Design Features of Music and Language: The Choric/Dialogic Distinction

Music and spoken language share certain characteristics: both consist of sequences of acoustic elements that are combinatorically combined, and these elements partition the same continuous acoustic dimensions (frequency, formant space and duration). However, the resulting categories differ sharply: scale tones and note durations of small integer ratios appear in music, while speech uses phonemes, lexical tone, and non-isochronous durations. Why did music and language diverge into the two systems we have today, differing in these specific features? We propose a framework based on information theory and a reverse-engineering perspective, suggesting that design features of music and language are a response to their differential deployment along three different continuous dimensions. These include the familiar propositional-aesthetic ('goal') and repetitive-novel ('novelty') dimensions, and a dialogic-choric ('interactivity') dimension that is our focus here. Specifically, we hypothesize that music exhibits specializations enhancing coherent production by several individuals concurrently-the 'choric' context. In contrast, language is specialized for exchange in tightly coordinated turn-taking-'dialogic' contexts. We examine the evidence for our framework, both from humans and non-human animals, and conclude that many proposed design features of music and language follow naturally from their use in distinct dialogic and choric communicative contexts. Furthermore, the hybrid nature of intermediate systems like poetry, chant, or solo lament follows from their deployment in the less typical interactive context.

Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling

In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Networks (GDN) was explored through an innovative method for fMRI analysis considering all voxels in four experimental conditions: standard target (Goal; G), novel (N), neutral (Z) and noisy target (NG). First, average reaction times (RTs) for each condition were calculated. In the second-level analysis, 'distracted' participants, as indicated by slower RTs, evoked brain activations and differences in both hemispheres' neural networks for selective attention, while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions, i.e. Z.G, N.G, NG.G. Results showed different prefrontal activations dependent on prior context in the auditory modality, recruiting between 1 to 10 prefrontal areas. The higher the motor response and influence of the previous novel stimulus, the more prefrontal areas were engaged, which extends the findings of hierarchical studies of prefrontal control of attention and better explains how auditory processing interferes with movement. Also, the current study addressed how subcortical loops and models of previous motor response affected the signal processing of the novel stimulus, when this was presented laterally or simultaneously with the target. This multitasking model could enhance our understanding on how an auditory stimulus is affecting motor responses in a way that is self-induced, by taking into account prior context, as demonstrated in the standard condition and as supported by Pulvinar activations complementing visual findings. Moreover, current BCI works address some multimodal stimulus-driven systems.

Beat-based and Memory-based Temporal Expectations in Rhythm: Similar Perceptual Effects, Different Underlying Mechanisms

Predicting the timing of incoming information allows the brain to optimize information processing in dynamic environments. Behaviorally, temporal expectations have been shown to facilitate processing of events at expected time points, such as sounds that coincide with the beat in musical rhythm. Yet, temporal expectations can develop based on different forms of structure in the environment, not just the regularity afforded by a musical beat. Little is still known about how different types of temporal expectations are neurally implemented and affect performance. Here, we orthogonally manipulated the periodicity and predictability of rhythmic sequences to examine the mechanisms underlying beat-based and memory-based temporal expectations, respectively. Behaviorally and using EEG, we looked at the effects of beat-based and memory-based expectations on auditory processing when rhythms were task-relevant or task-irrelevant. At expected time points, both beat-based and memory-based expectations facilitated target detection and led to attenuation of P1 and N1 responses, even when expectations were task-irrelevant (unattended). For beat-based expectations, we additionally found reduced target detection and enhanced N1 responses for events at unexpected time points (e.g., off-beat), regardless of the presence of memory-based expectations or task relevance. This latter finding supports the notion that periodicity selectively induces rhythmic fluctuations in neural excitability and furthermore indicates that, although beat-based and memory-based expectations may similarly affect auditory processing of expected events, their underlying neural mechanisms may be different.

Learning and Consolidation as Re-representation: Revising the Meaning of Memory

In this Hypothesis and Theory paper, we consider the problem of learning deeply structured knowledge representations in the absence of predefined ontologies, and in the context of long-term learning. In particular, we consider this process as a sequence of re-representation steps, of various kinds. The Information Dynamics of Thinking theory (IDyOT) admits such learning, and provides a hypothetical mechanism for the human-like construction of hierarchical memory, with the provision of symbols constructed by the system that embodies the theory. The combination of long-term learning and meaning construction in terms of symbols grounded in perceptual experience entails that the system, like a human, be capable of memory consolidation, to manage the complex and inconsistent structures that can result from learning of information that becomes more complete over time. Such consolidation changes memory structures, and thus changes their meaning. Therefore, memory consolidation entails re-representation, while re-representation entails changes of meaning. Ultimately, the theory proposes that the processes of learning and consolidation should be considered as repeated re-representation of what is learned.

A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns

Rhythm is a ubiquitous feature of music that induces specific neural modes of processing. In this paper, we assess the potential of a stimulus-driven linear oscillator model (57) to predict dynamic attention to complex musical rhythms on an instant-by-instant basis. We use perceptual thresholds and pupillometry as attentional indices against which to test our model predictions. During a deviance detection task, participants listened to continuously looping, multiinstrument, rhythmic patterns, while being eye-tracked. Their task was to respond anytime they heard an increase in intensity (dB SPL). An adaptive thresholding algorithm adjusted deviant intensity at multiple probed temporal locations throughout each rhythmic stimulus. The oscillator model predicted participants’ perceptual thresholds for detecting deviants at probed locations, with a low temporal salience prediction corresponding to a high perceptual threshold and vice versa. A pupil dilation response was observed for all deviants. Notably, the pupil dilated even when participants did not report hearing a deviant. Maximum pupil size and resonator model output were significant predictors of whether a deviant was detected or missed on any given trial. Besides the evoked pupillary response to deviants, we also assessed the continuous pupillary signal to the rhythmic patterns. The pupil exhibited entrainment at prominent periodicities present in the stimuli and followed each of the different rhythmic patterns in a unique way. Overall, these results replicate previous studies using the linear oscillator model to predict dynamic attention to complex auditory scenes and extend the utility of the model to the prediction of neurophysiological signals, in this case the pupillary time course; however, we note that the amplitude envelope of the acoustic patterns may serve as a similarly useful predictor. To our knowledge, this is the first paper to show entrainment of pupil dynamics by demonstrating a phase relationship between musical stimuli and the pupillary signal.

Creativity, information, and consciousness: The information dynamics of thinking

This paper presents a theory of the basic operation of mind, Information Dynamics of Thinking, which is intended for computational implementation and thence empirical testing. It is based on the information theory of Shannon, and treats the mind/brain as an information processing organ that aims to be information-efficient, in that it predicts its world, so as to use information efficiently, and regularly re-represents it, so as to store information efficiently. The theory is presented in context of a background review of various research areas that impinge upon its development. Consequences of the theory and testable hypotheses arising from it are discussed.

Linking Neural and Symbolic Representation and Processing of Conceptual Structures

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.