Martin Finn

Martin Finn
Ghent University | UGhent · Department of Experimental Clinical and Health Psychology

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14
Publications
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Introduction
Martin Finn currently works at the Department of Experimental Clinical and Health Psychology , Ghent University. Martin does research in Experimental Psychology and Behavioural Science. Their current project is 'Odysseus '.

Publications

Publications (14)
Article
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Some learning psychologists refer to relational cues (Crels) and functional cues (Cfuncs) in their analyses of verbal behavior. However, past research about Crels and Cfuncs is limited in two ways. First, there has been relatively little research into how Crels and Cfunc functions can be acquired, and whether such acquisition is similar to the acqu...
Article
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According to relational frame theory Cfunc stimuli select which stimulus properties are transformed via derived stimulus relations. To date there has been no demonstration of the selective action of Cfunc control. We provide an analysis of the requirements for such a demonstration, and describe the results from four experiments employing a paradigm...
Article
Little is known about why people behave the way they do in threatening situations. Some theories invoke a transfer of responses from unconditioned stimuli (US) to conditioned threat signals (CS), but this principle goes astray, because responses to the US and CS can differ substantially. The idea that we introduce here is that the pattern of respon...
Article
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In the target article ‘Functional and Descriptive Contextualism’ the argument is made that Functional Contextualism may benefit from the Interbehavioral tradition. This commentary supports this perspective. The Functional Contextual approach is elaborated, and some of its weaknesses are highlighted. Following this some descriptions of analyses of c...
Article
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A recently published article reported a particular pattern of responding that has been observed on the Implicit Relational Assessment Procedure (IRAP), referred to as a Single-Trial-Type-Dominance-Effect (STTDE; Finn, Barnes-Holmes, & McEnteggart in The Psychological Record, 68(1), 11–25, 2018). To account for the phenomenon, the Differential Arbit...
Article
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Under the rubric of Relational Frame Theory (RFT), researchers have investigated the role of deictic relational responding in the analysis of self in relation to others, place, and time, primarily through the use of an extended developmental protocol (Barnes-Holmes, 2001). In a move towards extending methodologies for studying deictic relational re...
Article
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The study investigated how conflicting stimulus–response mappings influenced affordance processing given a manipulation of the functional relations. Participants performed a task involving consistent–inconsistent stimulus–response mappings: Implicit Relational Assessment Procedure (IRAP). They were instructed to confirm or to deny a relation betwee...
Article
The Implicit Relational Assessment Procedure (IRAP) has been used as a measure of implicit cognition and has been used to analyze the dynamics of arbitrarily applicable relational responding. The current study uses the IRAP for the latter purpose. Specifically, the current research focuses on a pattern of responding observed in a previously publish...
Article
The article describes how the study of derived stimulus relations has provided the basis for a behavior-analytic approach to the study of human language and cognition in purely functional-analytic terms. The article begins with a brief history of the early behavior-analytic approach to human language and cognition, focusing on Skinner’s (1957) text...
Article
Full-text available
The Implicit Relational Assessment Procedure (IRAP) is increasingly used in applied and clinical settings, and yet many of the procedural variables of the measure have not been subjected to a systematic analysis. One such variable is the type of rules that are employed when instructing the IRAP and the effect this might have on participants’ perfor...

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Projects

Projects (2)
Project
Learning can be viewed as changes in an organism’s behavior that are due to regularities in its environment. However, learning often involves more than the impact of simple environmental regularities. For instance, behavioral changes may result from the joint impact of multiple regularities (e.g., moderated learning; De Houwer, Barnes-Holmes & Moors, 2013), or from regularities in the presence of regularities (i.e., meta-regularities). Changes such as these can be characterized as instances of ‘complex’ learning (De Houwer & Hughes, 2020). Recognizing the role of complex learning significantly increases our ability to describe the relationship between behavior and the environment. Furthermore, it allows us to consider the functions that relations between stimuli can have, and in this way might shed a new light on relational learning (e.g., in the context of analogical reasoning) and relational responding as described by Relational Frame Theory (RFT; Hayes, Barnes-Holmes & Roche, 2001). Our aim for this project is to interface these recent insights in functional learning psychology with the computational Reinforcement Learning (RL) framework (Sutton & Barto, 2018). In recent years, RL has become a popular computational framework in cognitive psychology and neuroscience. It has significantly contributed to our understanding of the dynamics of learning and decision-making processes, as well as their neural correlates. Nevertheless, we believe there is much to be gained by pooling insights from complex learning and the RL framework. Interfacing these two perspectives on learning could be fruitful for researchers in both functional learning psychology and RL. That is, we expect predictions and implications to spread bilaterally. The project will begin with a functional analysis of the prominent RL algorithms to identify the implications of complex learning for RL research, as well as potential avenues to extend the RL framework. We believe there are limitations to the RL framework as it is currently applied to psychological research questions, and hope to address those limitations in this way. Furthermore, we think that certain insights from learning psychology, especially considering complex learning effects, could be valuable for RL research. For instance, RL models famously suffer from the ‘curse of dimensionality’, which complicates their translation from use in lab-experiments to real-world problems, and illustrates the importance for an organism to have an efficient and suitable representation of its environment (i.e., representational learning; Gershman & Niv, 2010). We argue that the function of relational contextual cue is particularly interesting in this regard, because its function is to indicate how stimuli in the environment should be related, and could thus be an important component of representational learning. More generally, we believe that a complex, functional perspective on learning has a lot to offer, and intend to systematically investigate these questions. A second component of the project will investigate the merits of the computational RL framework for functional learning psychology. Previous work, as part of my Master’s dissertation, focused on whether relations between stimuli can have the function of conditioned reinforcer. More work is needed to better understand the moderators of complex conditioned reinforcement (e.g., generalization, extinction, counter-conditioning, and so on) and we believe that a computational approach could help us provide a precise and quantitative understanding of the dynamics involved in the acquisition of this function. Research on the functions of relations opens up a number of related questions, such as how exactly relational responding can be reinforced and how derived relational responding may become a generic mode of responding (i.e., relating stimuli in previously untrained ways). Here too, we believe RL to be a promising framework to help better understand these processes. RFT has provided an influential conceptual account of the development of relational responding, and has led to the development of relational training procedures to increase intelligence and scholastic aptitude in children (i.e., SMART training; Cassidy, Roche & Hayes, 2011) or symbolic thinking in children with Autism Spectrum disorder (i.e., PEAK training; Dixon, Carman, Tyler, Whiting, Enoch & Daar, 2014). Better understanding the intricacies of such procedures with help from RL models could be a valuable addition to this work.