Title: Grounding early time perception in infant motor activity
Funder: Economic and Social Research Council

Abstract: The goal of this project is to investigate the role of embodiment - specifically, infant motor activity - in the early development of time perception. Typically, adults from all cultures and backgrounds can accurately anticipate short-term events, can catch objects thrown to them, and can perform the enormous range of human activities that require timing. This universality may seem to imply that the sense of time does not rely on learning, a position implicitly taken by the wellknown pacemaker-accumulator models and multiple-oscillator/coincidence-detection models of time perception. But there are problems with these models, among them, the problem of resetting the system for every event for which a time judgment might later be required. Recently, the reset problem has been avoided by suggesting that time perception depends on memory-trace decay. We will propose a novel memory-trace model of the early development time perception in which learning and coordinated motor activity in infancy play a key role. This hypothesis is explored through a combination of behavioural studies of infant time keeping and computational modelling. Specifically, the aims of the projects are to: develop a novel memory-based computational model of time-perception explore the developmental trajectory and implications of this model for understanding emerging time perception verify empirically the key hypothesis of the model - i.e., that time-perception in infants is learned behavior driven by coordinated motor activity explore the relation between infant motor activity and infant timing accuracy. develop more advanced statistical methods to analyse infant EMG data, thus facilitating the use of EMGs in future developmental studies I. Empirical work with infants: Each of four research streams will explore a facet of how motor-activity manipulations affect infants' time perception. The infant experiments will use a "missed-beat" time-assessment methodology in which a highly salient auditory-visual stimulus appears then disappears. This will be repeated at fixed intervals and will be skipped every nth interval. Infants' responses to the missed event will be the measure of their anticipation of that event - and, therefore, of their encoding of the inter-stimulus test interval. The four research streams are: - To what extent does the amount and quality of infants' motor activity predict their event-timing ability? - What is the relation between accuracy in pointing and time-interval estimation? - Can EMGs be used effectively as a subthreshold measure of motor activity in infant time-perception tasks? - What is the relation between memory decay (i.e., fast/slow habituation) and timing accuracy? An important and original aspect of this work is our use of anticipative postural adjustment EMG signals that appear before the visible onset of arm movements, thus giving a better assessment of movement onset responses than with movement observation. While EMG signals are a more sensitive indicator of infant movement, they tend to be noisy, which will require developing novel (machine-learning) techniques to analyze the data. Discriminating the EMG time series from infant groups with regularly timed stimuli versus randomly timed stimuli, implies that the former had perceived the time-intervals. II. Computational modeling: Underlying our proposed computational model is an unexpected observation: that the distributions of neurons in the cortex, of time-perception errors (Gibbon, 1977) and of motor-activity errors (Fitt's Law) are all scale-free (i.e., roughly, that at all scales of observation, there is the same distribution of items, errors, etc.). We will design a learning- and memory-based computational model of infant time perception based on scale-free neuronal organization, use (simulated) motor-activity to calibrate it, and test it empirically.


Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES
Health categories: MENTAL HEALTH