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Speed and Variability Brain Training

Note – this is only for a legacy HighIQPro app – not the most recent version.

Speed and Variability in HighIQPro’s 2G Dual N-Back Game

 

Electrical activity in the brain can be recorded using EEG electrodes on the scalp. Different types of brain waves (electrical oscillations) have been linked to sleep, navigation, cognition, attention, and can help diagnose a wide range of disorders including autism, schizophrenia and epilepsy.

During standard dual n-back training, while our attention system keeps track of the updating letters and squares,  sensory areas of the brain go into an electrical rhythm that matches the rhythm of the n-back letters and squares – for instance, every second. The n-back stimuli drive the cortex rhythmically (Lakatos et al., 2009).

 

attention oscillations
Rhythm of selective attention.
Adapted from Fig. 3, Lakatos et al. (2013)

 

The cortical brain rhythm helps process the stimuli, anticipating when the next sounds and squares will appear, and ‘binding’ the audio and visual information together in working memory.

How does this relate to real world cognition? Speech is rhythmic, our gestures are rhythmic, visual saccades (the moment to moment eye movements that we scan scenes or text with) are rhythmic. Our attention system works in these rhythms, resulting in periodic increases in excitability in anticipation of attended stimuli – making information processing more selective and efficient.

2G options

The speed and variability of  the stimuli (letter sounds and square moves) in our 2G n-back can be systematically increased. You can select different difficulty levels in the first (timer icon) option shown here. The default is 1 which is a regular 3 second stimulus rhythm. This is the standard Jaeggi n-back setting, and the setting that is used in most commercial and online n-back brain training apps. 2 and 3 settings increase the speed and break the rhythm of the n-back stimuli, requiring more focused attention and demands on working memory.[space]

 

Cortical Rhythms of Attention – The Science

Selective Attention

Working memory – our mental ‘workspace’ – depends on selective attention, a fundamental cognitive capacity. This allows the brain to enhance its coding of events in the world that are relevant while tuning out irrelevant or distracting events (Desimone & Duncan, 1995).

Selective attention is based on rhythmic brain waves, with the high excitability phase of the oscillation in phase with the rhythmic inputs (such as speech sounds, or visual fixations while scanning a scene). This results in the sensory responses to attended stimuli being amplified while processing of stimuli ‘out of phase’ (in the troughs of the wave) are dampened.  Attention to rhythmic streams in any one modality (e.g. visual) results in a synchronous ‘entrainment’ (tuning in) of neuronal activity across other modalities (e.g. audio), making it easier to integrate multi-modal information about the world around us (Lakatos et al., 2009;  Bosman et al., 2009; Saleh et al., 2010; Besle et al., 2011; Lakatos et al., 2013).

These attention-based rhythms occur in theta and delta bands 0.5 – 7 hz (cycles per second). Other rhythms are associated with other types of brain states. (Alpha waves, for instance, are associated with relaxed mental states such as those induced by meditation.)

 

theta and delta waves attention
Theta and Delta brain waves in focused attention

 

Attention control & working memory

Phase resetting – to rapidly adapt to changing rhythms and ‘reset’ the brain waves – is under the control of our attention (Lakatos, 2009). Attention focus itself is part of our working memory system, and it is our working memory system that n-back training targets.

2G N-back training

2G n-back training on Speed/Variability settings 2 and 3 systematically breaks up the input rhythms, forcing attention to continually reset its oscillations. This has 3 effects that should benefit working memory training:

  • More need for ‘interference control’ from the irregular stimulus rhythm. This irregular stimulus rate is irrelevant to your task, and you have to try to ignore it while performing the n-back task. It is known that interference control is linked to fluid intelligence
  • More ‘cognitive load’ – with the need to focus attention over loose time windows, rather than in regular narrow ‘pulses’.  Imagine running down a set of regular steps of different colors and you have to look for n-back color matches! You will soon descend the stairs in a rhythm, and simply focus on matching colors. However, if the steps are irregular – with some steeper and some shallower, you will also have to focus your attention on the steps themselves – and not just the colors. This places more demands on your working memory system, as well as the need for more interference control.  
  • Irregular stimulus presentation also prevents automatization of the attention system’s sensory processing. Working memory training benefits from preventing automatization when you can process on ‘automatic pilot’ mode.

 


References

Besle, J., Schevon, C.A., Mehta, A.D., Lakatos, P., Goodman, R.R., McKhann, G.M., Emerson, R.G., and Schroeder, C.E. (2011). Tuning of the human neocortex to the temporal dynamics of attended events. J. Neurosci.31, 3176–3185[space]

Bosman, C.A., Womelsdorf, T., Desimone, R., and Fries, P. (2009). A microsaccadic rhythm modulates gamma-band synchronization and behavior. J. Neurosci.29, 9471–9480.[space]

Desimone, R., and Duncan, J. (1995). Neural mechanisms of selective visual attention. Annu. Rev. Neurosci.18, 193–222.[space]

Lakatos, P., Musacchia, G., O’Connel, M. N., Falchier, A. Y., Javitt, D. C., & Schroeder, C. E. (2013). The Spectrotemporal Filter Mechanism of Auditory Selective Attention. Neuron, 77(4), 750–761. doi:10.1016/j.neuron.2012.11.034[space]

Lakatos, P., O’Connell, M. N., Barczak, A., Mills, A., Javitt, D. C., & Schroeder, C. E. (2009). The Leading Sense: Supramodal Control of Neurophysiological Context by Attention. Neuron, 64(3), 419–430. doi:10.1016/j.neuron.2009.10.014[space]

Saleh, M., Reimer, J., Penn, R., Ojakangas, C.L., and Hatsopoulos, N.G. (2010). Fast and slow oscillations in human primary motor cortex predict oncoming behaviorally relevant cues. Neuron 65, 461–471.

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