Assuming that motor action might help understanding underlying psychological processes, the current study attempts to identify deceptive behaviour by tracking the dynamics of hand movements during a computer-sorting task. Participants were asked to deceive by pretending that some of the items they “owned” have been paid for even though they were stolen. Analysis of participants’ streaming x-, y- mouse coordinates, during decision-making suggested that movement trajectories might indeed reveal underlying cognitive processes during deception. Statistical indicators of curvature and reaction times, including area-under- the-curve (AUC) and maximum deviation (MD) implied that there was as greater cognitive competition during deceptive than during truthful responding. Deceptive responds were made more slowly, with a stronger curvature tendency towards the alternate truthful answer. Non-deceptive responding was associated with shorter reaction times and more linear response trajectories. This supports prior research indicating that action dynamic measures might capture deceptive processes.
Traditionally motor responses are thought to be the outcome of discrete intermediate sequential stages of perception, cognition and decision-making. However there seems to be increasing evidence supporting a non-linear system consisting of various internal states, which are simultaneously activated and coexist (Song and Nakayama, 2009). Various neurophysiological studies provide physiological plausibility for this view by stressing the presence of multiple concurrent motor plans and fluent transitions between sensory, decision-making and motor operations in the brain (Schall, 2005). Consistent with this, Spivey and Dale (2006) introduced the “dynamic real-time cognition framework“, which proposes that the process of decision making is not always completed in the brain’s cognitive subsystems before it is forwarded to other subsystems. Those researchers conclude that „when actions accompany thinking, they are part and parcel of it“( McKinstry et al. 2008, p. 24).
In an early study by Abrams and Balota (1991) participants took part in a lexical decision (is this a word or a nonword?) and recognition memory test and were asked to answer by pulling a joystick handle to the right or the left. Higher –frequency words evoked not only faster initiation but were strongly linked to a shorter time being spent pulling the handle to its limit. Thus the authors conclude that not only the latency of its initiation but the kinematics of the entire response movements can reveal information about cognitive processes. Consistent with this a more recent paper by Papesh and Goldinger (2012) concluded that arm movement trajectories expose underlying response confidence. In their study shorter decision times and more linear response trajectories have been correlated with more confident decisions, whereas slow answers with increased trajectory curvature were associated with less confident decisions.
Freeman, Dale and Farmer (2011) argue, that dynamics of a simple bodily movement can provide powerful cues of internal cognitive occurrences. They further state that particularly hand movement trajectories, during target choice reaching tasks, provide a continuous stream of motor output, potentially revealing the on-going cognitive dynamics of information processing.
Based on these research results it seems plausible that reaching movements of the hand potentially reveal underlying competing cognitive processes. This is consistent with the results of a study conducted by Wojnowicz, Ferguson, Dale and Spivey (2009). The authors revealed that when participants had to state their explicit attitude (like vs. dislike) towards Black and White people, the hand reaching trajectories of the “like” option for black people were attracted to the “dislike” response. Similarly Dale, Roche, Snyder and McCall (2008) used a wireless Nintendo wii remote to investigate the interaction between cognition and action and Song and Nakayama (2006) used a position sensor to measure movements. However Freeman and Ambady (2011) conclude that using computer mouse-tracking is by far the most practical technique to study action dynamics during decision making. Farmer, Cargilla, Hindya, Dale, & Spivey (2007) further promote the mouse-tracking method arguing that for instance eye-movement data only allows for roughly 3 to 4 data points (saccades) per second whereas mouse-tracking generates up to 60 data points per second while being more user-friendly and affordable.
Deception and its identification are crucial elements in legal contexts, not least since dishonest testimonies might corrupt the functioning of a justice system (Masip, Garrido and Herrero, 2004). There have been numerous studies investigating nonverbal cues during verbal communication, revealing that both professionals and novices strongly rely on nonverbal cues when assessing a person’s honesty (Vrij, 2004). The so called nonverbal approach –or behavioural-indicator approach to lie detection emphasizes the relevance of observing the person’s behaviour, in particular the non-verbal signals, in order to asses whether that person is lying or telling the truth (Masip & Garrido, 2000). However it seems that studies using this approach often yield ambiguous, low-accuracy results, which are difficult to quantify and compare while also being highly dependent on individual differences, and thus are often seen as having low scientific validity and reliability (e.g. Masip, Garrido and Herrero, 2004; Simpson, 2008). Song and Nakayama(2006) argue that measuring continuous hand movements during target choice reaching reveal the evolution of hidden cognitive processes, while providing quantifiable and analysable data.
- Quote paper
- Laura Weis (Author), 2012, The honest hand - how computer mouse trajectories might capture deceptive processes, Munich, GRIN Verlag, https://www.grin.com/document/205918