TEMPORAL CHARACTERISTICS OF THE MEMORY CODE

Size: px

Start display at page:

Download "TEMPORAL CHARACTERISTICS OF THE MEMORY CODE"

Garry Adams
5 years ago
Views:

1 TEMPORAL CHARACTERISTICS OF THE MEMORY CODE Irina V. Maltseva 1, Yuri Masloboev 2 Institute of Psychology, Russian Academy of Sciences Moscow, Russia 1 Moscow Institute of Electronic Engineering Moscow, Russia 2 imaltseva@mail.ru ymaslob@mail.ru Abstract In the experiments with mental marking of the omitted stimulus one of the subjects (SH) showed paradoxical alpha reactivity. Bursts of the alpha oscillations were recorded in 100 per cent of the trials at all electrode locations when he mentally marked omitted tones. The omitted stimulus paradigm applies to intentional component of behaviour during cognitive task performance. Subjects were asked to predict the moment of omitted stimuli onset, as if they were not actually omitted. To perform this task subject had to keep in memory the duration of interstimulus interval and to recall it. It was supposed that bursts of alpha activity corresponding to the omitted stimuli in ongoing EEG of SH were indicators of retrieval processes during time intervals reproduction. In the present study temporal characteristics of this alpha activity were estimated in order to predict individual memory capacity of the subject using the statements of the oscillatory model. The results of our experiments indicate that interindividual variability of short-term memory span can be predicted as a function of the alpha rhythm parameters ( Maltseva and Masloboev, 1997). These studies were based on the theoretical predictions of the oscillatory model of memory (Lebedev, 1980,1990 ). The central concept of the model is memory code as a phase locked combination of the brain oscillations in the alpha range of human EEG. Temporal characteristics of the alpha-rhythm determining memory capacity have been estimated from spontaneous EEG so far. This study is aimed at experimental estimation of the EEG parameters in the ongoing alpha activity emitted by a subject during performance of the memory task. There follows a short description of the temporal organization of the memory code proposed in the oscillatory model of memory. Memory code Elements of the memory code are undamped oscillations produced by neural networks. They are formed under the influence of internal and external stimuli on neurons, located in various areas of a brain. All these neurons begin to work coherently, forming a new neural network, which is capable to generate oscillatory activity and to maintain it for a long time. Such undamped oscillations can form a basis for both short-term and long-term memory under certain conditions. One of the most important conditions is maintenance of step-type behavior of separate oscillations. If absolute difference between the periods of oscillations (1/F1) - (1/F2) exceeds the certain critical size (R) it provides their independent existence. Oscillations can differ from each other not only by their frequency, but also by phase. It

2 means, that oscillations with the same frequency can be independent if there is a certain phase delay between them. Thus there should also be a minimal critical size of such a delay (R). If the period or phase difference between two oscillations is less than this minimal value they merge together. This critical value is given the name "frequency refractoriness". If there is a step difference of phases R, there must be a limited number of discrete oscillations with identical frequency. Their maximum quantity (N) is determined by a ratio of the period of oscillations (1/F, where F - frequency) and critical difference R: N = 1/FR-1 (1) Unit is subtracted to take into account number of degrees of freedom of system states since one of oscillations should serve as a reference mark in a set of oscillations. Separate neural networks can generate some discrete oscillations interconnected according to the described rules. Their joint activity carries the information on the certain attribute of stimulus. The initial state of a network is set at the moment when a certain property of stimulus is fixed. Specificity of each code of memory is determined by the number of oscillations participating in its forming, and phase relations between them. The initial state of the network is determined by stimulus properties. The number of created oscillations might vary from 1 to N. Involvement of several net units is needed to code information about different characteristics of one and the same stimulus. For example, if a spot of light is presented as a stimulus, then one group of oscillations stores the information about the color of the spot, a second group keeps information about its brightness, a third group holds information about its size. All these oscillations carrying the features of the same stimulus exist as a module, or wave bunch, or ensemble and they store the whole information about the stimulus. The activity of net units is strictly ordered within the module. The cycle of their joint activity begins with some initial oscillation generated by one of the involved net units. It corresponds to 0 phase on the time axis. The next unit might be involved in the joint activity with the delay not less than the critical difference, R, determined earlier. It means, that number of neural units included in the module, is limited to a ratio of the period of their joint fluctuation ( 1/F ) and the critical difference ( R ). This ratio also does not exceed size (N) in the equation (1) and determines maintenance capacity of the module. Thus, the more neural networks form a uniform module, the greater number of elements from the stimulus set can be coded and kept in memory. Memory codes in human EEG According to the model three main parameters describe conditions needed for storing of information by network oscillations. They are regularity of oscillations with a stable period, existence of a variety of oscillations because of step-like difference between their phases and periods, and coherent behavior of oscillations. The alpha rhythm of human EEG meets these requirements most of all in comparison with the other standard EEG bands. The alpha rhythm is the most stable rhythm in human EEG. Such stable activity is necessary for information maintenance, storing and processing. The important feature of the alpha activity is a multimodal distribution of its power spectra. One can see two or more separate peaks in it. It means that several discrete oscillations exist simultaneously in the alpha activity.

3 Another evidence of the step-like behavior of the alpha oscillations presents bursts of alpha activity known as alpha spindles. It is known from physics that oscillations neighboring in frequency can create beatings or superposition. We assume that alpha spindles of spontaneous EEG originate from this phenomenon. Short-term memory capacity is limited by two parameters of alpha oscillations (1). They are period of dominant oscillatory activity (1/F) and critical period of step-like difference between separate oscillations or frequency refractoriness (R). Both parameters can be estimated from the alpha spectra of spontaneous EEG. To examine the model statements on the memory code structure we estimated parameters of spontaneous alpha rhythm of different subjects in relation to their short-term memory capacity (Maltseva, Masloboev, 1997). The minimal values of frequency refractoriness were measured from the alpha spectra of spontaneous EEG in occipital area when a subject was sitting with eyes closed. Parts of EEG with the most pronounced alpha rhythm were selected for analysis. The values of frequency refractoriness changed from 4.84 ms to 7.87 ms, to produce an average of 6.35 ms. They were shown to have significant correlation with short-term memory span in the group of 40 subjects. Period of the dominant alpha activity was assessed from a histogram of maximal peaks of 120 spectra. The dominant frequency was determined as the histogram maximum. A significant correlation of ratio (1) and short-term memory span supported the prediction of the oscillatory model on information encoding. Resent studies have demonstrated that memory mechanisms may be provided by theta (Miller, 1991, Gevins et al., 1997) and gamma (Tallon-Baudry et al., 1996, 1997, Elliott and Mueller, 2000) oscillations. The model explaining short-term memory functioning by phase-coupled theta-gamma oscillations (Lisman and Idart, 1995) was proposed. The other studies reported contribution of joint alpha-theta activity (Klimesch, 1996, 1999) in memory performance. All these studies show that a variety of EEG oscillatory activity takes part in memory processing at its different stages. The oscillatory model we have described considers alpha rhythm to be of great importance for maintenance of information in memory in contradiction to the notion that it is only an idling EEG activity. The main argument against contribution of the alpha activity into cognitive processing is blocking of alpha amplitude by attention accompanying mental activity. Traditionally the absence of blocking was considered to be just an individual kind of reactivity. In spite of that studies of skilled actions in sport and performance of different mental tasks showed that alpha power may not block, or may enhance in some conditions (see review in Shaw, 1996). This type of the alpha reactivity reported at first as paradoxical response (Mundy-Castle, 1957, Mulholland and Runnals, 1962, Kreitman and Show, 1965, Glass, 1967) was an experimental basis for developing of models of EEG alpha rhythm reactivity which involved the concept of intention (Mulholland, 1969, 1972, Wertheim, 1974, 1981, Show, 1996). These models proposed that alpha reduces during attentive but not during intentive behavior. Taking into account these findings it seems that all of the cases showing paradoxical alpha responses are the objects of special interest. In the experiments with mental marking of the omitted stimulus (Maltseva et al., 2000) both types of the alpha reactivity were observed. One subject (SH) emitted bursts of high amplitude alpha activity in 100 per sent of the trials in all sites when he mentally marked omitted tones. In addition, this subject showed an extraordinary result in the short-term memory test (8,6 of digits). The omitted stimulus paradigm (Basar et al, 1988) applies to intentional component of behaviour during cognitive task performance. Subjects were asked to predict the moment of omitted stimuli onset, as if they were not actually omitted. To perform this task subject had to keep in memory the duration of interstimulus interval and to recall it. It means that the

4 estimation of time interval was the stimulus itself in this experimental paradigm. It is also reasonable to suppose that bursts of alpha activity corresponding to the omitted stimuli in ongoing EEG of SH were indicators of retrieval processes during time intervals reproduction. In the present study temporal characteristics of this alpha activity were estimated in different scalp locations in order to predict individual memory capacity of the subject using the statements of the oscillatory model. Experimental design and methods Subjects. The subject ( 19, male) was seated in a soundproof and dimly illuminated room with eyes open.. He was requested to limit eye and head movements as much as possible. Stimuli. Auditory stimuli ( 2000 Hz 80 db SPL tones) of 800 ms duration were presented via earphones regularly with an inter-stimulus interval of 2600 ms. A series contained 100 signals. Every fourth signal was omitted. Tasks. Subject was instructed to mark the omitted stimuli only mentally, avoiding any kind of movement activities or counting of stimuli. Data acquisition The EEG was recorded from F 3, F 4, C Z, C 3, C 4, T 3, T 4, P 3, P 4, O 1 and O 2 electrode position referred to the right ear. The filter bandpass was Hz. The EEG 800 ms prior and 800 ms after stimuli onset, including omitted ones, was digitised with sampling intervals of 1.56 ms and stored on the hard disk. Data processing. For off-line analysis 25 subsets of EEG sweeps corresponding to the omitted stimuli were used. Each subset included 11 sweeps recorded from different sites. Because of the short duration, 3 consequent sweeps were merged together. As a result 8 sweeps with duration of 1600 x 3 = 4800 ms were obtained for each site. Fourier transform was applied to these sweeps in order to get a periodogramm. Evaluation of parameters. Two parameters of the alpha-rhythm were evaluated: period of dominant frequency (1/F) and frequency refractoriness (R) (Tab. 1). In each periodogramm position of the main peak was estimated. An arithmetic mean of such estimates was calculated for each site. It was considered as the period of the dominant alpha activity (1/F). For calculation of frequency refractoriness the peaks with the amplitude bigger than 0.3 of maximum in current periodogram were defined. Differences between selected neighboring peaks were calculated. Mean value of all obtained differences of periods was used as frequency refractoriness parameter for every site. Short-term memory span test. Sets of random digits were presented on a computer screen for 2 s with groups of 3 digits in a horizontal line. The subject was instructed to read the digits from the left to the right once. When the digits were removed from the screen, the subject was asked to recall and reproduce the digits by means of keyboard of the computer in the same order as they had been presented. If the subject was correct, the number of digits was increased by one in the next trial; if the subject made an error, the number of digits was decreased by one item. The first trial consisted of 5 digits. After an initial training series consisting of 5 stimulus presentations the subject was presented with 2 blocks of 20 trials. At the end of each block of stimuli mean number of the correctly recalled items was calculated. Prediction of the short-term memory span. Predicted values of the short-term memory capacity were calculated using the following equation of the model: STM=N*logN=(1/F- 1)*log(1/F-1) (Lebedev, 1990).

5 Analysis of the results and discussion Table 1 contains values of dominant alpha periods (1/F) and frequency refractoriness ( R ) measured from different sites and the predicted values of the short-term memory span.(stm). The periods of dominant frequency were about 100 ms of duration. The most variable dominant alpha activity was observed at Cz location. The values of frequency refractoriness changed from 4.69 ms to ms through all of the derivations. Experimental short-term memory capacity was 8,6, SD=0,9. The closest predicted STM values were calculated using EEG data recorded from F3, C3, T3 and O2 EEG derivations. The main purpose of the present study was to estimate two characteristics of the alpha oscillations proposed in the oscillatory model of memory in the brain activity emitted during memory task performance. It was anticipated that a value of frequency refractoriness should make about 10 ms (Lebedev, 1980). Our study showed that R varies in the wide range. The minimal value of frequency refractoriness 4.69 ms obtained in our study is in agreement with value of elementary time quantum, which was predicted at first theoretically from psychological data (Geissler 1987). Later direct evidence was obtained from the experiments on apparent movement that mental processes exhibit a discrete quantal temporal structure with a period duration of 4.5 ms (Geissler et al, 1999). It can be that size of step about 10 ms proposed in the oscillatory model of memory represents some optimal but not minimal period of alpha activity quantisation. A possibility to predict short-term memory capacity using temporal characteristics of the alpha oscillations opens new perspectives for investigation of memory and cognition. Table 1. Measured estimates and predicted STM values. Electrode placements 1/F(ms) R(ms) STM Mean St. D. Mean St. D. F3 101,7 4,6 10,2 3,2 8,55 F4 103,5 4,3 7,6 1,6 13,89 Cz 105,2 8,2 8,5 1,4 12,01 C3 101,3 4,2 9,7 2,8 9,21 C4 101,3 4,1 7,8 0,9 12,93 T3 101,7 5,2 9,3 2,7 9,91 T4 100,1 3,2 6,6 1,6 16,31 P3 100,1 3,4 7,8 2,0 12,70 P4 100,3 2,6 7,2 2,5 14,08 O1 100,9 2,4 6,6 2,1 16,14 O2 98,6 2,5 10,9 4,1 7,29 All 101,33 4,06 8,46 1,4 12,09

6 References Bazar, E., Bazar-Eroglu, C. & Röschke, J. (1988) Do coherent patterns of the strange attractor EEG reflect deterministic sensory-cognitive states of the brain? In: M. Markus, M. Müller, and G. Nicolis (Eds.), >From chemical to biological organisation. Springer, Berlin-Heidelberg-New-York, Elliot, M. A., Mueller, H. J., (2000) Evidence for 40-Hz oscillatory short-term visual memory revealed by human reaction-time measurements. Journal of Experimental Psychology: Learning, Memory and Cognition 2000, Vol. 26, No. 3, Geissler, H.-G. (1987) The temporal architecture of central information processing: Evidence for a tentative time-quantum model. Psycological Research 49, Geissler, H.-G., Schebera, F.-U., Kompass R. (1999) Ultra-precise quantal timing: Evidence from simultaneity thresholds in long-range apparent movement. Perception & Psychophysics, 61 (4), Gevins, A., Smith, E.S., McEvoy, L., Yu, Daphne (1997) High-resolution EEG Mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral cortex 7: Klimesch, W. (1996) Memory processes, brain oscillations and EEG synchronisation. Int. J. of Psychophysiol., 24: Klimesch, W. (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews 29: Lebedev, A.N. (1980) A mathematical model for human visual information, perception and storage. In: R.F.Thompson (Ed.) Neural mechanisms of goal directed behaviour and learning, Academic Press, New York, Lebedev A.N. Cyclical (1990) neural codes of human memory and some quantitative regularities in experimental psychology. In: Psychophysical explorations of mental structures. Ed. by H.-G. Geissler. Toronto. Hogrefe and Huber Pbl., Lisman, J.E., & Idiart, M.A.P. (1995). Storage of 7+/-2 short-term memories in oscillatory subcycles. Science, 240, Maltseva, I.V. and Masloboev, Y.P. (1997) Alpha rhythm parameters and short-term memory span. Int. J. of Psychophysiol., 25, Malseva, I.V., Geissler, H-.G., Erol Bazar (2000) Alpha oscillations as an indicator of dynamic memory operations anticipation of omitted stimuli. Int. J. of Psychophysiol., 36: Mulholland, T.B. (1995) Human EEG, behavioural stillness and biofeedback. Int. J. Psychophysiol., 19, Mulholland, T.B. (1969) The concept of attention and the electroencephalographic alpha rhythm. In: C.R. Evans and T.B. Mullholand (Eds.), Attention in Neurophysiology, Butterworths, London, Mulholland, T.B. (1972) Occipital alpha revisited. Psychol. Bull., 78, Mulholland, T.B. and Runnals, S. (1962) Increased occurrence of alpha during increased attention. J. Psychol., 54, Mundy-Castle, A.C. (1957) The EEG and mental activity. Electroencephalogr. Clin. Neurophysiol., 9, Show, J.C. (1996) Intension as a component of the alpha-rhytm response to mental activity. International Journal of Psychophysiology 24, Tallon-Baudry, C., Bertrand, O., Delpeuch, C., & Pernier, J. (1996).Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. Journal of Neuroscience, 16, Tallon-Baudry, C., Bertrand, O., Delpeuch, C., & Pernier, J. (1997). Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans. Journal of Neuroscience, 17, Wertheim, A.H. (1974) Oculomotor control and occipital alpha activity: a review and a hypothesis. Acta Psychol., 38, Wertheim, A.H. (1981) Occipital alpha activity as a measure of retinal involvement in oculomotor control. Psychophysiology, 18, This study was supported by the RFBR grant

Brain wave synchronization and entrainment to periodic acoustic stimuli

Neuroscience Letters 424 (2007) 55 60 Brain wave synchronization and entrainment to periodic acoustic stimuli Udo Will, Eric Berg School of Music, Cognitive Ethnomusicology, Ohio State University, 110