title: Regularities in responding during performance of a complex choice task.
abstract: Systematic variations in the rate and temporal patterns of responding under a multiple concurrent-chains schedule were quantified using recurrence metrics and self-organizing maps to assess whether individual rats showed consistent or idiosyncratic patterns. The results indicated that (1) the temporal regularity of response patterns varied as a function of number of training sessions, time on task, magnitude of reinforcement, and reinforcement contingencies; (2) individuals showed heterogeneous, stereotyped patterns of responding, despite similarities in matching behavior; (3) the specific trajectories of behavioral variation shown by individuals were less evident in group-level analyses; and (4) reinforcement contingencies within terminal links strongly modulated response patterns within initial links. Temporal regularity in responding was most evident for responses that led to minimally delayed reinforcers of larger magnitude. Models of response production and selection that take into account the time between individual responses, probabilities of transitions between response options, periodicity within response sequences, and individual differences in response dynamics can clarify the mechanisms that drive behavioral adjustments during operant conditioning.
authors.affiliation: Department of Psychology, Park Hall, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA. emiii@buffalo.edu.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Mercado
authors.affiliation: Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Orduña
mesh_terms: D000818:Animals; D002755:Choice Behavior; D003216:Conditioning, Operant; D007206:Individuality; D008297:Male; D051381:Rats; D017208:Rats, Wistar; D012055:Reinforcement Schedule; D013997:Time Factors
keywords: Behavior dynamics; Individual variations; Matching law; Neural networks; Reinforcement schedules