0000000001285592
AUTHOR
Denis Mareschal
GAMIT - A Fading-Gaussian Activation Model of Interval-Timing: Unifying Prospective and Retrospective Time Estimation
Two recent findings constitute a serious challenge for all existing models of interval timing. First, Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies of participants’ judgments of interval duration (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried (i.e., retrospectively or prospectively) and cognitive load. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds…
Embodiment and the origin of interval timing : kinematic and electromyographic data
International audience; Recent evidence suggests that interval timing (the judgment of durations lasting from approximately 500 ms. to a few minutes) is closely coupled to the action control system. We used surface electromyography (EMG) and motion capture technology to explore the emergence of this coupling in 4-, 6-, and 8-month-olds. We engaged infants in an active and socially relevant arm-raising task with seven cycles and response period. In one condition, cycles were slow (every 4 s); in another, they were fast (every 2 s). In the slow condition, we found evidence of time-locked sub-threshold EMG activity even in the absence of any observed overt motor responses at all three ages. Th…
Unifying prospective and retrospective interval-time estimation: A fading-Gaussian activation-based model of interval-timing
International audience; Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried and cognitive load on participants’ judgments of interval duration. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds, with increased cognitive load leading to shorter estimates. We describe GAMIT, a Gaussian spreading-activation model,…
Learning to perceive time: A connectionist, memory-decay model of the development of interval timing in infants
International audience; We present the first developmental model of interval timing. It is a memory-based connectionist model of how infants learn to perceive time. It has two novel features that are not found in other models. First, it uses the uncertainty of a memory for an event as an index of how long ago that event happened. Secondly, embodiment – specifically, infant motor activity – is crucial to the calibration of time-perception both within and across sensory modalities. We describe the model and present three simulations which show (1) how it uses sensory memory uncertainty and bodily representaions to index time, (2) that the scalar property of interval timing (Gibbon, 1977) emer…
Motor Activity Improves Temporal Expectancy
International audience; Certain brain areas involved in interval timing are also important in motor activity. This raises the possibility that motor activity might influence interval timing. To test this hypothesis, we assessed interval timing in healthy adults following different types of training. The pre- and post-training tasks consisted of a button press in response to the presentation of a rhythmic visual stimulus. Alterations in temporal expectancy were evaluated by measuring response times. Training consisted of responding to the visual presentation of regularly appearing stimuli by either: (1) pointing with a whole-body movement, (2) pointing only with the arm, (3) imagining pointi…
TRACX2: a RAAM -like autoencoder modeling graded chunking in infant visual -sequence learning
International audience; Even newborn infants are able to extract structure from a stream of sensory inputs and yet, how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights, and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-none in nature and during learning their component parts become ever more tightly bound together. TRACX2 successfully models data from four experiments from the infant visual statistical-learning literature, including tasks i…