1
|
Oblak A, Slana Ozimič A, Repovš G, Kordeš U. What Individuals Experience During Visuo-Spatial Working Memory Task Performance: An Exploratory Phenomenological Study. Front Psychol 2022; 13:811712. [PMID: 35664146 PMCID: PMC9159378 DOI: 10.3389/fpsyg.2022.811712] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
In experimental cognitive psychology, objects of inquiry are typically operationalized with psychological tasks. When interpreting results from such tasks, we focus primarily on behavioral measures such as reaction times and accuracy rather than experiences - i.e., phenomenology - associated with the task, and posit that the tasks elicit the desired cognitive phenomenon. Evaluating whether the tasks indeed elicit the desired phenomenon can be facilitated by understanding the experience during task performance. In this paper we explore the breadth of experiences that are elicited by and accompany task performance using in-depth phenomenological and qualitative methodology to gather subjective reports during the performance of a visuo-spatial change detection task. Thirty-one participants (18 females) were asked to remember either colors, orientations or positions of the presented stimuli and recall them after a short delay. Qualitative reports revealed rich experiential landscapes associated with the task-performance, suggesting a distinction between two broad classes of experience: phenomena at the front of consciousness and background feelings. The former includes cognitive strategies and aspects of metacognition, whereas the latter include more difficult-to-detect aspects of experience that comprise the overall sense of experience (e.g., bodily feelings, emotional atmosphere, mood). We focus primarily on the background feelings, since strategies of task-performance to a large extent map onto previously identified cognitive processes and discuss the methodological implications of our findings.
Collapse
Affiliation(s)
- Aleš Oblak
- Laboratory for Cognitive Neuroscience and Psychopathology, University Psychiatric Hospital Ljubljana, Ljubljana, Slovenia
| | - Anka Slana Ozimič
- Mind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Grega Repovš
- Mind & Brain Lab, Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Urban Kordeš
- Center for Cognitive Science, Faculty of Education, University of Ljubljana, Ljubljana, Slovenia.,Observatory: Laboratory for Empirical Phenomenology, Faculty of Education, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
2
|
Jo S, Kim HC, Lustig N, Chen G, Lee JH. Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies. Hum Brain Mapp 2021; 42:5374-5396. [PMID: 34415651 PMCID: PMC8519860 DOI: 10.1002/hbm.25627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.
Collapse
Affiliation(s)
- Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Niv Lustig
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| |
Collapse
|
3
|
Knowles JP, Evans NJ, Burke D. Some Evidence for an Association Between Early Life Adversity and Decision Urgency. Front Psychol 2019; 10:243. [PMID: 30804859 PMCID: PMC6377396 DOI: 10.3389/fpsyg.2019.00243] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/24/2019] [Indexed: 11/24/2022] Open
Abstract
The relationship between early life adversity and adult outcomes is traditionally investigated relative to risk and protective factors (e.g., resilience, cognitive appraisal), and poor self-control or decision-making. However, life history theory suggests this relationship may be adaptive-underpinned by mechanisms that use early environmental cues to alter the developmental trajectory toward more short-term strategies. These short-term strategies have some theoretical overlap with the most common process models of decision-making-evidence accumulation models-which model decision urgency as a decision threshold. The current study examined the relationship between decision urgency (through the linear ballistic accumulator) and early life adversity. A mixture of analysis methods, including a joint model analysis designed to explicitly account for uncertainty in estimated decision urgency values, revealed weak-to-strong evidence in favor of a relationship between decision urgency and early life adversity, suggesting a possible effect of life history strategy on even the most basic decisions.
Collapse
Affiliation(s)
- Johanne P. Knowles
- School of Psychology, University of Newcastle, Callaghan, NSW, Australia
| | - Nathan J. Evans
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Darren Burke
- School of Psychology, University of Newcastle, Callaghan, NSW, Australia
| |
Collapse
|
4
|
Salzer Y, de Hollander G, van Maanen L, Forstmann BU. A neural substrate of early response capture during conflict tasks in sensory areas. Neuropsychologia 2019; 124:226-235. [DOI: 10.1016/j.neuropsychologia.2018.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 10/29/2018] [Accepted: 12/11/2018] [Indexed: 10/27/2022]
|
5
|
Ghosts in machine learning for cognitive neuroscience: Moving from data to theory. Neuroimage 2018; 180:88-100. [DOI: 10.1016/j.neuroimage.2017.08.019] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 07/17/2017] [Accepted: 08/04/2017] [Indexed: 12/17/2022] Open
|
6
|
Abstract
Constant decision-making underpins much of daily life, from simple perceptual decisions about navigation through to more complex decisions about important life events. At many scales, a fundamental task of the decision-maker is to balance competing needs for caution and urgency: fast decisions can be more efficient, but also more often wrong. We show how a single mathematical framework for decision-making explains the urgency/caution balance across decision-making at two very different scales. This explanation has been applied at the level of neuronal circuits (on a time scale of hundreds of milliseconds) through to the level of stable personality traits (time scale of years).
Collapse
|
7
|
Raymond JG, Steele JD, Seriès P. Modeling Trait Anxiety: From Computational Processes to Personality. Front Psychiatry 2017; 8:1. [PMID: 28167920 PMCID: PMC5253387 DOI: 10.3389/fpsyt.2017.00001] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/03/2017] [Indexed: 12/15/2022] Open
Abstract
Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits that are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in "trait" anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational studies are described in the light of this perspective and appropriate targets for future studies are discussed.
Collapse
Affiliation(s)
- James G. Raymond
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| | - J. Douglas Steele
- School of Medicine (Neuroscience), Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Peggy Seriès
- Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
8
|
|
9
|
Nassar MR, Frank MJ. Taming the beast: extracting generalizable knowledge from computational models of cognition. Curr Opin Behav Sci 2016; 11:49-54. [PMID: 27574699 DOI: 10.1016/j.cobeha.2016.04.003] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Generalizing knowledge from experimental data requires constructing theories capable of explaining observations and extending beyond them. Computational modeling offers formal quantitative methods for generating and testing theories of cognition and neural processing. These techniques can be used to extract general principles from specific experimental measurements, but introduce dangers inherent to theory: model-based analyses are conditioned on a set of fixed assumptions that impact the interpretations of experimental data. When these conditions are not met, model-based results can be misleading or biased. Recent work in computational modeling has highlighted the implications of this problem and developed new methods for minimizing its negative impact. Here we discuss the issues that arise when data is interpreted through models and strategies for avoiding misinterpretation of data through model fitting.
Collapse
Affiliation(s)
- Matthew R Nassar
- Department of Cognitive, Linguistic and Psychological Sciences, Brown Institute for Brain Science, Brown University, Providence RI 02912-1821
| | - Michael J Frank
- Department of Cognitive, Linguistic and Psychological Sciences, Brown Institute for Brain Science, Brown University, Providence RI 02912-1821
| |
Collapse
|
10
|
White CN, Curl RA, Sloane JF. Using Decision Models to Enhance Investigations of Individual Differences in Cognitive Neuroscience. Front Psychol 2016; 7:81. [PMID: 26903896 PMCID: PMC4746304 DOI: 10.3389/fpsyg.2016.00081] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 01/14/2016] [Indexed: 11/17/2022] Open
Abstract
There is great interest in relating individual differences in cognitive processing to activation of neural systems. The general process involves relating measures of task performance like reaction times or accuracy to brain activity to identify individual differences in neural processing. One limitation of this approach is that measures like reaction times can be affected by multiple components of processing. For instance, some individuals might have higher accuracy in a memory task because they respond more cautiously, not because they have better memory. Computational models of decision making, like the drift–diffusion model and the linear ballistic accumulator model, provide a potential solution to this problem. They can be fitted to data from individual participants to disentangle the effects of the different processes driving behavior. In this sense the models can provide cleaner measures of the processes of interest, and enhance our understanding of how neural activity varies across individuals or populations. The advantages of this model-based approach to investigating individual differences in neural activity are discussed with recent examples of how this method can improve our understanding of the brain–behavior relationship.
Collapse
Affiliation(s)
- Corey N White
- Department of Psychology, Syracuse University Syracuse, NY, USA
| | - Ryan A Curl
- Department of Psychology, Syracuse University Syracuse, NY, USA
| | | |
Collapse
|