Letter to nephew, no date.

Handwritten letter from Daniel D. Whedon to nephew, Daniel Avery Whedon. Not dated.

Letter to Rev. Epaphras Kibby. 01/11/1801

Handwritten letter from Timothy Merritt to Rev. Epaphras Kibby regarding lodging and preaching schedule. Sent in care of Mr. Lambert. Dated Jan. 11, 1801, Bath, ME.

Letter to nephew, 01/13/1868

January 13 1868 letter from Daniel A. Whedon to nephew.

Letter to nephew, 12/11/1868

Letter from Daniel Whedon to nephew, Daneil Avery Whedon. Dated December 11 1868.

Letter to nephew, June 7, 1881

Handwritten letter dated June 7, 1881, to nephew, Daniel Avery Whedon.

Letter to nephew, 10/10/1867

Daniel Avery Whedon's four page letter to nephew, dated October 10, 1867.

Letter to Daniel A. Whedon. New York. April 13, 1867

Handwritten 1867 letter from Daniel D. Whedon to his nephew, Daniel A. Whedon, requesting books.

Letter to Daniel Avery Whedon. Brooklyn, NY. Nov. 22, 1865.

Handwritten 1865 handwritten letter from Daniel D. Whedon to Daniel A. Whedon, his nephew, regarding slavery in relation to the Church as well as the Christian Union.

Letter to D. A. Whedon. Pittsfield. July 12, 1844.

Letter from Daniel Denison Whedon to Daniel A. Whedon, his nephew.

Letter to nephew, August 9 1867

Hanwritten letter from Daniel Denison Whedon to nephew. Dated 08/09/1867.

Letter to nephew, 02/06/1868

Letter from Daniel Whedon to nephew, Daniel Avery Whedon. Dated February, 6, 1868.

Letter to V.V. Haynes, 04/02/1878

Two page letter from Daniel D. Whedon to V.V. Haynes. Dated March 2, 1878.

Letter to Gershom Cox, dated January 29, 1844

This is a letter from Phoebe Palmer and her husband written on January 29, 1844 to Gershom Cox.

A Quantitative Evaluation of the AVITEWRITE Model of Handwriting Learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an intense relation between curvature and speed. The Adaptive Vector Integration to Endpoint (AVITEWRITE) model of Grossberg and Paine (2000) proposed how such complex movements may be learned through attentive imitation. The model suggest how frontal, parietal, and motor cortical mechanisms, such as difference vector encoding, under volitional control from the basal ganglia, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psycophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing human trajectories. The results show that model performance was variable across subjects, with an average correlation between the model and human data of 89+/-10%. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and learning of other complex sensory-motor skills would benefit from further research.

From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits

Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.